By Carsten Krause, August 7th, 2025

How Leading Enterprises Are Embedding Sustainability into TOGAF, LeanIX, and ISO Frameworks to Drive ROI, Regulatory Readiness, and Environmental Performance

Sustainability is no longer an optional add-on – it’s a strategic imperative. For forward-thinking enterprises, weaving sustainability into the very fabric of Enterprise Architecture (EA) can unlock significant value. In this brief, we compare three approaches to sustainable EA – The Open Group Architecture Framework (TOGAF), LeanIX’s agile EA management model, and ISO standards-based frameworks – analyzing their strengths, weaknesses, and alignment with Environmental, Social, and Governance (ESG) objectives. We focus on Schneider Electric as a leading case study, illustrating how a “twin transformation” of digital innovation and sustainability can drive business value .

In this report:

• Comparative strengths & weaknesses of TOGAF, LeanIX, and ISO-based sustainable EA approaches
• Business value and ROI of adopting a sustainability-driven EA model
• Integration with ESG and CSRD regulatory reporting (Scope 1/2/3 emissions, digital lifecycle governance)
• Case Studies Schneider Electric, National Grid, Unilever, BMW, Microsoft

Let’s dive into how each framework supports sustainability, and what CDOs and enterprise architects can learn to lead in this new era.

Sustainable EA Frameworks at a Glance

Embedding sustainability into enterprise architecture means aligning technology, data, and business processes with environmental and social goals. Each EA framework approaches this differently:

TOGAF (The Open Group Architecture Framework): A comprehensive, standardized methodology (the Architecture Development Method) traditionally focused on business, data, application, and technology domains. LeanIX (Enterprise Architecture Management tool): A modern SaaS platform enabling agile EA practices, with evolving features for ESG data integration. ISO Standards-Based EA: An approach that aligns EA practices with relevant ISO standards (for environment, quality, etc.), emphasizing compliance and continuous improvement.

TOGAF – Proven Structure, Emerging Sustainability Extensions

Strengths: TOGAF is the de facto industry standard for enterprise architecture, used by 80% of organizations globally . It provides detailed guidance to establish an EA practice from scratch, covering all key domains (business, data, application, technology) . The prescriptive ADM steps ensure rigor and consistency. For organizations new to EA, TOGAF offers a comprehensive blueprint and a common language. Its maturity and community mean plenty of training and certified professionals are available. Notably, TOGAF can be extended – for example, SAP’s EA framework uses TOGAF 10 but adds a Sustainable Business Model Canvas artifact to address sustainability concerns . This shows TOGAF’s flexibility to incorporate new focuses like sustainability.

Weaknesses: Critics note that TOGAF can be heavyweight and inflexible, with a low-level focus that may ignore fast-changing needs . It has limited guidance on change management, stakeholder buy-in, or ESG specifics . Sustainability isn’t a built-in component of TOGAF – it must be introduced via requirements and principles. This means organizations have to explicitly layer sustainability goals (e.g. energy efficiency, carbon reduction) into TOGAF’s phases. Without careful adaptation, a TOGAF-driven program could become too documentation-centric and slow to respond to new ESG demands.

Latest Developments: The Open Group is actively promoting “EA for Sustainability” initiatives. At a 2023 Open Group event, experts stressed that EAs must ensure businesses “do the right things right,” making sustainability an integral design consideration . TOGAF’s influence is seen in emerging standards like the Open Footprint™ data model for carbon emissions, which aims to give architects a common framework for GHG data integration . In practice, TOGAF shops are starting to treat sustainability as a non-functional requirement across all architecture domains (e.g. including green IT principles in technology architecture, sustainable supply chain criteria in business architecture).

LeanIX – Agile EA with Built-in ESG Management

Strengths: LeanIX represents an agile, continuous approach to EA, implemented via a collaborative software platform. It excels at real-time transparency – creating living application portfolios, technology matrices, and capability maps that are always up-to-date. For sustainability, LeanIX has taken proactive steps: it partnered with PwC to embed an ESG Capability Map and ESG “fact sheets” into the EA tool . This allows architects to attach carbon footprint or diversity metrics to applications and processes, and analyze them just like any other IT asset data. LeanIX’s lightweight approach means faster time-to-value; teams can start capturing sustainability data alongside architecture data in just a few steps . The flexibility to integrate with data sources is another plus – e.g. pulling cloud energy usage stats or facility data into the EA repository for analysis.

Weaknesses: LeanIX is a toolset rather than a prescriptive framework. Its effectiveness for sustainability depends on how the user configures it. There is no fixed methodology – which can be a double-edged sword. Without a strong governance model, one could capture a lot of ESG data but lack the process to act on it. Also, LeanIX has historically focused on IT assets; aligning IT-centric views with broader environmental strategy may require additional methods. For example, connecting LeanIX’s application data with Scope 3 supplier emissions might need custom integration. In sum, LeanIX provides the “how” (technology and agility), but organizations still need to define the “what” (sustainability targets, KPIs) to drive decisions.

Notable Use: LeanIX’s ESG integration was piloted with manufacturing firm Viega and PwC, yielding a practical roadmap for Twin Transformation (digital + sustainability) . LeanIX quickly added the ESG Capability Map for all customers, indicating demand for such features. This agility in evolving the meta-model is a key strength – as ESG regulations evolve, LeanIX can update its templates and integrate new data fields faster than a static framework could.

ISO Standards-Based Approach – Compliance and Best Practice

Strengths: Many firms choose to align their architecture and processes with ISO sustainability standards to ensure compliance and credibility. Relevant standards include ISO 14001 (environmental management systems), ISO 50001 (energy management), ISO 26000 (social responsibility guidance), and newer climate-specific norms. Using ISO standards as a framework means sustainability is built on established Plan-Do-Check-Act cycles and is auditable – a big advantage for meeting regulatory reporting requirements. ISO/IEC 42010 and 42020 also provide guidance on architecture descriptions and processes, ensuring any sustainable EA initiative has formal rigor. An ISO-aligned EA will inherently focus on continuous improvement and risk management, which aligns with ESG goals (e.g. regularly reviewing and reducing environmental impacts as per ISO 14001).

Weaknesses: The ISO approach can be fragmented – organizations might end up juggling multiple standards and certifications. This can become bureaucratic if not streamlined. Also, ISO standards tell you what to achieve (e.g. reduce pollution, involve stakeholders) but less about how to redesign your application landscape or data architecture to do so. Without a unifying EA methodology, an ISO-centric enterprise could struggle to translate high-level guidelines into technology roadmaps. There’s also a risk of a compliance-only mindset – focusing on passing audits rather than truly reaping innovation. CDOs should ensure that ISO adoption is complemented by creative architecture work (for example, using ISO standards to set targets, but encouraging architects to innovate on how to meet them with new IT solutions).

Example: Schneider Electric itself follows numerous ISO standards (e.g. ISO 14001-certified sites) and has integrated these into its management systems . ISO alignment helped Schneider build trust and consistency globally, which was foundational in being named the world’s most sustainable corporation in 2021 . However, Schneider didn’t stop at compliance – it leveraged digital architecture (IoT platforms, data analytics) to exceed some ISO requirements and create new business value from sustainability (more on this in the case study below).

(See Figure 1 for a comparison of these frameworks’ strengths and weaknesses. Source: Nationalgrid)

Figure 1: Scope 1, 2, 3 Emissions – Key categories enterprises must capture in sustainable EA. Scope 3 (value chain emissions) are often 70–80% of total and hardest to measure . A robust EA framework should integrate data across all scopes for full ESG reporting.

Business Value and ROI of Sustainability-Driven EA

Sustainable enterprise architecture isn’t just about compliance – it’s about competitive advantage. Data shows that companies embracing sustainability see tangible ROI:

Revenue and Growth: According to Capgemini research, over half of executives report sustainability initiatives directly increased revenue . In fact, embedding sustainability across the value chain can drive 4–6% higher revenue growth . New green products and services, and winning bids with ESG requirements, contribute to this upside. Profitability: The same study found a 2–4 percentage point improvement in EBITDA margin for sustainability leaders . Efficiency plays a big role – for example, optimizing processes to reduce waste and energy use lowers operating costs. Strong ESG performance also lowers risk premiums and insurance costs over time. Sales and Market Access: 82% of executives say sustainability has a direct positive impact on sales . Customers (especially enterprise and government buyers) prefer suppliers with robust ESG credentials. Sustainability is now often a tiebreaker in RFPs. Schneider Electric, for instance, has won deals for energy management by showcasing its own carbon neutrality efforts as part of the value proposition. Brand and Investor Value: 76% of companies observed stronger brand reputation, and 81% noted increased investor interest, after doubling down on sustainability . A well-architected sustainability program boosts intangible assets – brand equity and stakeholder trust – which translate to higher valuations and customer loyalty. Risk Mitigation: Sustainability-driven EA also reduces downside risk. By proactively managing climate risks (e.g. using EA to ensure critical systems are resilient to extreme weather or supply chain disruptions), companies avoid costly incidents. It’s difficult to quantify avoided costs, but regulatory fines, supply shocks, and talent attrition (30% of employees have left a company due to poor sustainability values ) can be very expensive if ESG is ignored.

Figure 2: Business uplift from sustainability initiatives. Companies integrating sustainability into operations achieved ~5% higher revenue growth and ~3% EBITDA margin improvement on average . Beyond compliance, sustainability drives top-line and bottom-line benefits.

From an ROI perspective, investments in sustainable architecture (like emissions tracking systems, renewable energy integration, greener IT infrastructure) can pay back handsomely. A Verdantix analysis even found that modern ESG data platforms can deliver 238% ROI in 3 years by turning sustainability data into actionable efficiencies. In short, “doing good” is driving real dollars – making the case to the CFO that EA initiatives with an ESG lens are high-value investments, not just ethical choices.

Integration with ESG, CSRD, and Digital Governance

Regulatory momentum is accelerating the need for robust sustainability data and digital governance. The EU’s Corporate Sustainability Reporting Directive (CSRD) exemplifies this, mandating detailed sustainability disclosures from 2024 onward. Key integration points for EA include:

Holistic ESG Data Architecture: Enterprise architects must design data models and pipelines that consolidate ESG metrics across the enterprise. Under CSRD’s European Sustainability Reporting Standards (ESRS), firms must report on Scope 1, 2, and 3 carbon emissions, climate mitigation plans, and even supply-chain engagement on carbon reduction . This requires pulling data from many systems: ERP for direct emissions (fuel, electricity), supplier portals for Scope 3, HR systems for diversity metrics, etc. EA frameworks like TOGAF emphasize data architecture – now that must encompass carbon and social data flows as first-class citizens. Automation and Auditability: CSRD will require digital, auditable reports with third-party assurance akin to financial audits . A sustainable EA should integrate internal systems with reporting tools (e.g. a carbon accounting platform) to automatically gather and validate data. LeanIX’s approach of an ESG Fact Sheet repository is one solution – it creates a single source of truth for ESG data that can be traced and assured. ISO-based frameworks also help here: e.g. ISO 14064 provides standards for GHG measurement that ensure data quality. Architects might implement controls so that every emission data point is tagged with its source system and calculation method (meeting the audit trail requirement). Scope 3 and Value Chain Integration: The toughest challenge is Scope 3 emissions, which on average account for 70%+ of a company’s footprint but only ~22% of firms currently measure them fully . EA must extend beyond enterprise boundaries – integrating with suppliers’ and partners’ systems. Schneider Electric’s Zero Carbon Project to help 1,000 top suppliers halve their CO2 by 2025 is a prime example . Schneider provides suppliers with tools (often digital platforms) to report and reduce emissions, effectively federating the architecture into an ecosystem. The Open Group’s concept of digital ecosystems and the Open Footprint initiative are paving paths for sharing emissions data up and down the value chain . EAs should ensure their integration architecture (APIs, data lakes) can ingest partner data in standardized formats (e.g. using formats like the WBCSD/JSON standards for carbon footprints). Digital Lifecycle Governance: Sustainability must also apply inward to IT itself – often called Green IT or sustainable IT governance. This includes decisions like optimizing data center energy use, hardware lifecycle management, and software efficiency. For example, CIOs like Niklas Sundberg of ASSA ABLOY advocate placing cloud workloads in regions with cleaner energy grids to cut IT emissions . A sustainable EA model would incorporate energy efficiency metrics for applications and infrastructure as part of technology architecture reviews. LeanIX can support this by tracking, say, which applications are on-prem vs. cloud and overlaying region-specific carbon intensity data. Meanwhile, TOGAF’s Technology Architecture phase can include principles like “Energy Star certified hardware” or “Servers must run at >50% utilization” to avoid waste.

Governance frameworks should be updated so that every architecture decision is evaluated for ESG impact. For instance, when approving a new IT system, ask: Does it help automate sustainability reporting? Can it run on renewable energy? Will it increase or decrease travel needs? This embeds ESG into the IT governance council’s remit. Some organizations are creating ESG Architecture Review Boards, akin to security review boards, to vet initiatives against sustainability criteria.

In aligning EA with regulations, companies also future-proof against upcoming rules (e.g. potential U.S. SEC climate disclosure rules, or new EU digital product passport requirements). The bottom line is that sustainable EA is becoming synonymous with good data governance – comprehensive, accurate, and transparent data that satisfies both business and regulatory stakeholders .

Sustainable Enterprise Architecture: A Comparative View of Five Industry Leaders

Enterprise architecture (EA) is no longer just about aligning IT with business – it’s now a critical lever for achieving environmental sustainability goals. Chief Data Officers (CDOs), CIOs, and enterprise architects are embedding sustainability principles into their EA frameworks to drive efficiency, regulatory compliance, and business value. This report examines how five global companies – Schneider Electric, National Grid, Microsoft, Unilever, and BMW – have implemented sustainable enterprise architecture. We focus on environmental sustainability (carbon, energy, resources) and analyze the frameworks and tools (TOGAF, LeanIX, ISO, custom approaches) each uses to integrate sustainability into their IT and business architectures. We also look at how they handle ESG data (Scope 1, 2, 3 emissions), comply with standards like the EU’s CSRD, and realize ROI through energy efficiency and IT optimization.

Each of these companies has taken a forward-thinking, practical approach to weave sustainability into the fabric of their enterprise architecture. The comparative table below summarizes key aspects of their approaches:

Comparison of sustainable enterprise architecture approaches across Schneider Electric, National Grid, Microsoft, Unilever, and BMW (CDO TIMES analysis).

As the table shows, each organization blends established frameworks and innovative tools in its own way to achieve green IT and business operations. Below, we delve into each company’s strategy, frameworks, and outcomes in detail, maintaining a sharp executive perspective.

Schneider Electric: Green IT Architecture for Energy Efficiency

Schneider Electric, a leader in energy management, treats sustainability as “the bedrock of our company’s ethos”, and this ethos is deeply embedded in its enterprise architecture. Rather than strictly following off-the-shelf frameworks like TOGAF, Schneider developed a custom “Green IT” initiative led by its CIO to integrate sustainability into IT architecture decisions. A pivotal realization was that “the energy consumption of almost half a million IT assets…had been overlooked,” prompting a new architecture strategy to monitor and optimize these assets. Schneider deployed its own EcoStruxure™ IT data center infrastructure management (DCIM) solutions across global IT sites, connecting formerly siloed server rooms into a centralized energy monitoring architecture(source: blog.se.com). By using IoT sensors and software, Schneider’s EA team gained real-time visibility into power usage across its IT landscape.

This digitized energy data platform (built on Schneider’s EcoStruxure Resource Advisor) now consolidates all sustainability and energy metrics into one place. It supports automated data collection, scenario modeling, and performance tracking for Schneider’s internal operations, aligning with ESG frameworks and streamlining reporting. Analysts have praised Schneider’s solution for “streamlin[ing] the entire workflow from data integration to final disclosure” in ESG reporting. In practice, Schneider can readily generate disclosures for standards like SASB and TCFD, and it is prepared for new regulations like CSRD with built-in template reports Source: (perspectives.se.com). This architecture ensures one source of truth for carbon and energy data across the enterprise.

Crucially, Schneider’s EA governance now factors in green IT principles at every turn. Data center consolidation and cloud migration are pursued not only for cost or agility, but explicitly to cut energy and carbon. Schneider partnered with cloud providers sharing its sustainability vision, moving workloads to more efficient cloud infrastructure after first establishing a baseline of server energy consumption. Using IT asset lifecycle advisories, the EA team decides whether to upgrade or retire equipment based on energy performance, thus avoiding “tech debt” that drains power (sourc: blog.se.com). This holistic approach – architecture plus operations – yielded significant ROI: previously under-monitored server rooms are now optimized, reducing energy waste and even improving cybersecurity by updating legacy systems. The company has set ambitious targets (25% absolute carbon reduction of its entire value chain by 2030, net-zero by 2050) Soure: perspectives.se.com, and its sustainable EA is a key enabler to hit these marks. It’s no surprise that Schneider was ranked the World’s Most Sustainable Company in 2025– a recognition due in part to how it architected digital systems to drive both performance and planet-positive impact.

National Grid: Enterprise Architecture Meets Net-Zero Infrastructure

National Grid, a major electricity and gas utility, places sustainability at the center of its enterprise architecture by necessity – it operates the networks powering a low-carbon future. National Grid’s architects follow The Open Group Architecture Framework (TOGAF) for consistent planning and governance, and they have extended this discipline to include environmental criteria. In practice, this means any new technology or process is evaluated not just for business and technical fit, but also for its impact on the company’s carbon footprint and resiliency in a decarbonizing energy landscape.

One example is how National Grid tackled SF₆, a potent greenhouse gas used in grid equipment. The company’s teams “worked on digital solutions to…track SF₆ usage and emissions, allowing for more accurate real-time emissions numbers” source: nationalgrid.com. By building an inventory management tool (integrated with their asset management systems via TOGAF-aligned interfaces), National Grid can monitor gas leakage and schedule preemptive maintenance on aging equipmentnationalgrid.com. This digital architecture component feeds into the central ESG data repository, ensuring operational GHG emissions (Scope 1) are accounted for. National Grid’s EA also incorporates IoT sensors and data historians on its grid (operational technology) to improve energy efficiency – for instance, by analyzing and reducing transmission losses and optimizing grid voltage, which directly cut emissions.

From a corporate reporting standpoint, National Grid has a unified ESG reporting framework supported by its enterprise data architecture. The company publishes detailed sustainability data aligned with SASB, GRI, and TCFD frameworks. As ESG standards evolve, National Grid’s approach is to “refine [its] disclosures in line with regulatory requirements, guidance and market practice”nationalgrid.com – essentially, their architecture is agile enough to map data to new standards like the EU Taxonomy or CSRD. In fact, National Grid produced an EU Taxonomy report and Climate Transition Plan illustrating how its investments align with Paris Agreement goals. These reporting capabilities are underpinned by data governance in IT: the EA team ensures data from multiple systems (asset management, operations, finance) flows into a central data warehouse for ESG, with controls for accuracy (they even obtain external assurance on ESG data).

Green IT and digital efficiency are also on National Grid’s agenda. The company has been modernizing its IT infrastructure, adopting cloud solutions where appropriate to increase flexibility and reduce on-premise hardware needs (which also reduces energy usage and physical footprint). For example, National Grid’s “transformation to a sustainable energy future” included a project with UK Power Networks using a TOGAF-based architecture to integrate distribution and transmission data via a cloud-based platform. This kind of integrated data architecture improves grid management efficiency, indirectly saving energy.

The business value of National Grid’s sustainable EA is evident in multiple areas. By digitizing emissions tracking and adopting SF₆-free technologies, the company not only lowers environmental risk but also pre-empts regulatory costs (SF₆ leaks incur penalties and will be phased out). It has also unlocked green financing: National Grid raised £2.9 billion in green bonds, leveraging its transparent sustainability data to assure investors of its climate projects. Internally, energy efficiency initiatives (like upgrading to LED lighting, electrifying its fleet, and using analytics for building energy management) are coordinated through enterprise-wide programs, many enabled by IT systems for monitoring and control. All these efforts are part of National Grid’s EA roadmap to achieve net-zero by 2050, with interim targets validated and tracked through digital dashboards (e.g. cutting Scope 1 and 2 emissions 80% by 2030, and reducing supply chain (Scope 3) emissions 20% by 2030) source: nationalgrid.com, bmwgroup.com. In sum, National Grid’s enterprise architecture isn’t just supporting the business strategy – it is the strategy for transitioning to a clean, fair, affordable energy future.

Microsoft: From Cloud Architecture to Sustainability Architecture

Microsoft has long championed environmental sustainability, and it leverages its considerable enterprise architecture prowess (and products) to push the envelope. Microsoft’s approach could be considered a custom sustainability architecture framework unto itself – one that marries elements of TOGAF’s rigor with Microsoft’s own technology stack. The company’s sustainability goals are famously ambitious: carbon negative by 2030, zero-waste and water positive by 2030, and removing all historical emissions by 2050. To achieve this, Microsoft built a comprehensive Microsoft Cloud for Sustainability solution that it also uses internallylsourc: earn.microsoft.comlearn.microsoft.com.

At the core is the Microsoft Sustainability Manager, a SaaS application (on Power Platform) which serves as an enterprise sustainability data hub. This system enables Microsoft to record, report, and reduce environmental impact across operations and value chain – essentially the company’s entire Scope 1, 2, and 3 footprint. The Sustainability Manager ingests data from myriad sources (energy meters in buildings, Azure datacenter telemetry, supplier emissions data, travel and procurement systems) into a common data model. It then calculates carbon footprints and tracks other metrics like water and waste, providing real-time dashboards. Crucially, it supports Scope 1, 2, and 3 emissions accounting across all GHG protocol categories. This means Microsoft’s EA integrates everything from the diesel used in a backup generator (Scope 1) to the emissions from manufacturing an Xbox by a supplier (Scope 3). Having this data in one architecture allows automatic generation of reports aligned to multiple standards – Microsoft can output disclosure reports per CSRD, SASB, GRI, IFRS S2, and other frameworks at the click of a button. Indeed, the system includes templates to produce CSRD-compliant reports, addressing the new EU reporting mandate ahead of time.

Microsoft’s enterprise architects also emphasize Green IT and operational efficiency internally, following principles of sustainable software and hardware lifecycle. For instance, Microsoft has implemented an Emissions Impact Dashboard for its Azure cloud services. This tool (available to customers but used internally as well) provides emissions data associated with Azure usage, helping IT teams make decisions to optimize workloads – e.g. moving a computing job to a region or time when renewable energy supply is higher, or refactoring an application to consume fewer resources surce: learn.microsoft.com. Additionally, Microsoft’s data centers are designed through its EA process to maximize efficiency (using AI for cooling, embracing open-source server designs, and procuring 100% renewable energy for operations). The enterprise architecture governance at Microsoft includes a carbon price on internal projects – Microsoft has an internal carbon fee model that charges each business unit for their emissions, incentivizing product teams and IT teams to build more efficient systems. This financial signal, enabled by accurate ESG data from the Sustainability Manager, ensures sustainability is a factor in every architectural decision company-wide.

The business value and ROI of Microsoft’s sustainable EA strategy are significant. Internally, energy-saving in data centers directly cuts costs (Microsoft has achieved a 93% average data center power usage effectiveness (PUE) in its cloud infrastructure through design and monitoring). Enterprise-wide, by automating ESG data collection and reporting, Microsoft estimated it eliminated thousands of person-hours of manual work, freeing teams to focus on sustainability improvements instead of paperwork. Moreover, Microsoft turned its internal capability into external opportunity: the Microsoft Cloud for Sustainability is now a revenue-generating offering, attracting customers who also need to manage ESG data. This reflects a broader lesson – sustainable enterprise architecture can drive innovation. Microsoft’s architects, for example, pioneered new “green software engineering” practices (such as optimizing code to consume less energy and carbon-aware scheduling of computing tasks)source: engineering.leanix.net. These practices not only reduce Microsoft’s own IT footprint but also set industry standards. Thanks to its holistic approach – integrating sustainability from data center hardware up to enterprise software – Microsoft is on track to meet its 2030 carbon-negative goal, and it has the dashboards to prove it. As Microsoft’s experience shows, a well-crafted sustainability architecture yields both environmental and competitive dividends, positioning the company as a trusted leader in climate-conscious innovation.

Unilever: Data-Driven Sustainability Architecture Across the Value Chain

Unilever, the consumer goods giant, has a sprawling supply chain and product portfolio – which means a vast environmental footprint and an even greater challenge for data integration. Unilever’s sustainable enterprise architecture centers on a unified ESG data strategy that connects every part of the business, from sourcing raw materials to a tub of ice cream in a customer’s freezer. Historically, Unilever faced “fragmented ESG data, inconsistent reporting, and evolving regulatory requirements” To overcome this, Unilever invested in a cloud-based ESG data management system aggregating sustainability metrics from multiple business units into a single repository. In effect, they built an enterprise-wide “sustainability data lake.” This platform ingests data from procurement (e.g. supplier emissions), operations (factory energy use, waste), logistics, and even product use-phase estimates. By centralizing this, Unilever achieved “real-time data tracking and analytics, allowing stakeholders to access reliable sustainability insights” source: bigroup.com.au.

A key EA decision for Unilever was to standardize ESG metrics and methodologies globally. They aligned their reporting and targets with the Science Based Targets initiative (SBTi) for emissions reductions and adopted common frameworks (like GRI for general sustainability metrics and TCFD for climate-related financial disclosure). By enforcing these standards through data architecture (i.e. every regional office reports carbon data in the same format, using the same emission factors), Unilever ensured consistency across its 190+ operating countries sourc: . The unified platform also embedded automation and AI: Unilever implemented “AI-driven tools that automated ESG data collection, validation, and reporting”, with machine learning to flag anomalies and ensure data quality. This dramatically reduced manual effort – sustainability data that once took weeks of consolidation in spreadsheets can now be accessed via live dashboards by executives. In fact, Unilever’s leadership uses an ESG performance dashboard as a competitive advantage tool, tracking progress toward goals and identifying areas to improve operational efficiency.

Unilever’s enterprise architects didn’t stop at reporting; they integrated sustainability into core business processes using digital tools. A prime example is procurement: starting in 2024, Unilever is “equip[ping] our procurement team with the capability to interpret and meaningfully integrate emissions-intensity data into their commercial strategies.”source: unilever.com. In practice, this means procurement officers, via Unilever’s digital supplier portal, can see the carbon footprint per ton of a raw material from each supplier and factor that into sourcing decisions. The architecture linking supplier data with Unilever’s ERP ensures that the company can preferentially buy lower-carbon materials, accelerating Scope 3 emissions cuts. Unilever also participates in industry data collaborations (like the Partnership for Carbon Transparency) to share and standardize product carbon footprint data across the value chain– a recognition that no one company’s architecture exists in isolation.

The business outcomes from Unilever’s sustainable EA are tangible. First, it “enhanced investor confidence” and access to sustainable finance – Unilever’s robust ESG data management has consistently earned it top marks in ESG ratings, keeping sustainability-focused investors on board. Second, it drove operational efficiencies: by identifying energy hotspots and waste in manufacturing, Unilever saved costs (e.g. optimizing factory processes to cut energy by 20% saved millions of dollars while reducing emissions). Third, it improved risk management – having unified data means Unilever can simulate the impact of carbon taxes or climate regulations on its business in a scenario analysis tool (aligned with TCFD guidance). This helps the company proactively adapt and avoid surprises. Notably, Unilever’s data-driven approach busts the myth that “ESG reporting is just a compliance exercise” – instead, “ESG data enables… operational efficiencies and value beyond compliance”source: bigroup.com.au. For example, the company’s Climate Transition Action Plan (which shareholders voted to approve) outlines how digital product design and reformulation (using life-cycle analysis tools) will reduce emissions by 2030 while sparking product innovation source: unilever.comunilever.com. With a goal of net-zero across its value chain by 2039, Unilever’s sustainable enterprise architecture – integrating data, AI, and business processes – provides a roadmap to get there, one data-driven decision at a time.

BMW: Driving Sustainability through Digital Architecture and Lifecycle Innovation

BMW, the luxury automaker, approaches sustainable enterprise architecture from both the manufacturing and IT angles. BMW’s strategy, dubbed BMW iFACTORY – Lean, Green, Digital, encapsulates how the company’s production architecture simultaneously pursues efficiency (lean), environmental sustainability (green), and intelligent connectivity (digital. Rather than an IT-only framework, BMW’s EA spans the entire product lifecycle. It starts in R&D: BMW built an advanced virtual simulation environment that allows engineers to test vehicle designs extensively in software. The result? “We reduce the number of prototype vehicles and the duration of the development process”, which means significant savings in materials and energysource: source: bmw.com. By using digital twins and even video-game technology in design, BMW achieves “greater efficiency and thus more sustainability” in development. Fewer physical prototypes translate directly to less manufacturing emissions and waste – an architectural choice paying both sustainability and time-to-market dividends.

On the factory floor, BMW’s enterprise architecture includes a globally networked production system. All 30+ production sites are integrated via common IT platforms that monitor energy and resource usage in real time. This digital nervous system is crucial for BMW’s goal to have “a new dimension in efficient, sustainable and digital vehicle production”source: bmwgroup.com. For example, data from each plant’s systems (like energy meters, robotic controls, etc.) feeds into a central analytics hub. BMW applies AI to this data to identify opportunities to cut energy consumption or waste. Through such efforts, BMW has already reduced CO₂ emissions from production by over 70% since 2006, and by 2021 all its manufacturing plants worldwide are powered by renewable energy (making them carbon-neutral operations)source: bmwgroup.com. Those are massive improvements, achieved by architecture principles such as standardizing processes (“critical business processes are defined and standardized” in the EA, per TOGAF compliance – source)nationalgrid.co and by investing in green technologies (e.g. energy-efficient paint shops, heat recovery systems, and on-site renewable generation, all monitored via IT systems).

BMW’s sustainable EA also extends to the supply chain and IT infrastructure. The company’s architects implemented systems to track supplier sustainability performance, helping BMW ensure it meets its commitment to “the most sustainable supply chain in the automotive industry”bmwgroup.com. By integrating supplier data into BMW’s procurement platform, the EA allows BMW to, for instance, source aluminum produced with solar power or steel made with hydrogen, significantly cutting Scope 3 emissions. In terms of IT, BMW has embraced a cloud-first, AI-enabled enterprise architecture for its corporate systems, moving away from legacy data centers to a hybrid cloud model source: cio.inccio.inc. This not only supports the massive data needs of analytics and AI but also improves IT energy efficiency (cloud data centers can offer higher efficiency and are increasingly run on renewables). Notably, BMW chose a greenfield approach to rebuild key enterprise applications (for production logistics and finance) on a modular, cloud platform co-developed with SAP – source: cio.inc. By doing so, they ensured new systems are optimized for real-time data and can be easily scaled or updated to incorporate sustainability metrics. This modern EA also directly helps sustainability: for example, the new production logistics system includes a “digital control tower” that improves supply chain transparency and agility In volatile scenarios (like semiconductor shortages or sudden EV demand spikes), BMW can respond faster, avoiding overproduction or expedited shipping – actions that reduce waste and emissions while saving money.

The ROI for BMW’s sustainable EA is seen in its bottom line and brand value. Efficient factories use less energy and water per car (BMW reports a steady decrease in resource use per vehicle produced, cutting cost per unit). By designing sustainability into cars from the concept stage (e.g. using more recycled materials, which the EA tracks via a materials database), BMW meets regulatory requirements and appeals to eco-conscious customers. The company’s climate goals have been scientifically validated to the 1.5°C target by SBTi source: bmwgroup.com, and its EA is what provides the data and tools to achieve those targets. For instance, by 2030 BMW aims to cut supply chain CO₂ by 20% from 2019 levels– the enterprise architecture’s integration of supplier, production, and product data will enable this by pinpointing where emissions are highest and evaluating alternatives. Another outcome is innovation: BMW’s emphasis on digital and green has spurred new initiatives like the reuse of EV batteries for energy storage (tracked via IT systems for a circular economy). All plants being renewable-powered since 2021 also insulates BMW from energy price fluctuations, a clear financial win. In summary, BMW’s case shows that a holistic enterprise architecture – connecting digital transformation with sustainability (“Twin Transition”) – drives both efficiency and resilience The company can confidently say a BMW is not only built efficiently and with lower carbon, but the IT and processes behind it are architected for a sustainable future.

The CDO TIMES Bottom Line

Sustainability-driven enterprise architecture is emerging as a dynamic capability that separates industry leaders from laggards. From our analysis of TOGAF, LeanIX, and ISO-based approaches a few leadership lessons stand out:

• 1. Marry Rigor with Agility:
  
  TOGAF provides structure; LeanIX provides agility. The winning formula is often a hybrid. Use TOGAF’s discipline to formalize sustainability requirements in your ADM cycle (e.g. include ESG risk assessment in Architecture Vision, include “green architecture” principles in design), while leveraging agile tools to iterate quickly on data and implementation. Don’t be afraid to modify frameworks – as SAP did by adding a Sustainable Business Canvas to TOGAFleanix.net – to keep them relevant.
  
• 2. Build the Business Case – with Data:
  
  Frame sustainability initiatives in terms of business outcomes. Use data (like the 5% revenue uplift and 3% EBITDA gain shown in Figure 2) to get buy-insweep.net. Speak the CFO’s language: reduced energy costs, access to ESG-linked financing, increased sales, and risk mitigation. Our ROI chart and the Capgemini studysweep.netsweep.net provide concrete numbers to justify investments in sustainability programs, be it new reporting software or a supply chain overhaul.
  
• 3. Embed ESG into Enterprise Data Fabric:
  
  Treat sustainability data as core enterprise data. Ensure your architecture integrates Scope 1/2/3 emissions data, workforce diversity stats, and other ESG metrics into the data warehouse and analytics layers. This might mean extending your data model – for example, adding “carbon impact” attribute to each application or process in your EA repository. A consistent data model (aligned to frameworks like Open Footprint or ISO 14064) will make reporting smoother and insights richer.
  
• 4. Leverage Standards, but Innovate Beyond Compliance:
  
  ISO and other standards give credibility – achieve them, but then go further. Use compliance as the floor, not the ceiling. Schneider Electric met ISO requirements, then created EcoStruxure and AI-driven services to far exceed them. Encourage your teams to propose innovative solutions (like relocating workloads for lower carbon or using blockchain for supply chain transparency) on top of meeting regulatory needs.
  
• 5. Foster Cross-Functional “Twin” Teams:
  
  Sustainability is not just the sustainability team’s job. Schneider’s case shows the magic of cross-functional collaboration – their sustainability, IT, and operations teams worked in lockstep. As CDO or EA leader, create joint task forces (e.g. “Digital & Sustainability Council”) to drive twin transformation This breaks silos between tech and sustainability departments and accelerates execution.
  
• 6. Prepare for Continuous Transformation:
  
  ESG criteria and regulations (like CSRD) will evolve. Build an architecture that’s modular and adaptable. Today it’s carbon reporting; tomorrow it might be biodiversity impact tracking or social impact scoring. A flexible EA (microservices, API-led integration, configurable data models) will enable you to plug in new sustainability modules with minimal disruption. In other words, architect for continuous sustainability transformation, not a one-time shift.

In a world where enterprise activities are the largest source of CO₂ emission the architects of our enterprises hold the keys to change. Sustainable EA is how we “do the right things right – together: right for the planet, right for society, and yes, right for business. The time to blueprint a sustainable future is now.

Sources:

Global Sustainability:

https://globalsustain.org/story-11750/#:~:text=create%20controls%20and%20actionable%20business,class%20technology%20leaders

LeanIX:

https://engineering.leanix.net/blog/sustainable-green-software-engineering/#:~:text=Building%20a%20Sustainable%20Future%20with,green%20software%20engineering%20and%20how

BIGroup:

Microsoft:

https://learn.microsoft.com/en-us/industry/well-architected/sustainability/sustainability-architecture-overview#:~:text=trust%2C%20security%2C%20privacy%2C%20and%20compliance,specific%20solutions%20are%20as%20follows

CIO.com:

https://www.cio.inc/bmw-future-proofs-ops-cloud-ai-a-28763#:~:text=BMW%27s%20production%20logistics%20transformation%20goes,time%20data%20flows

BMW:

https://www.bmwgroup.com/en/news/general/2022/Sustainability360.html#:~:text=BMW%20iFACTORY

Nationalgrid: https://www.nationalgrid.com/document/151931/download#:~:text=Sulphur%20Hexafluoride%20%28SF6%29%20%28UK%2FUS%29%204,SF6%20can%20have%20a%20significant

Schneider Electric:

https://blog.se.com/datacenter/2024/06/12/designing-the-intelligent-automated-enterprise-via-green-it/#:~:text=At%20Schneider%20Electric%2C%20our%20commitment,a%20greener%2C%20more%20secure%20planet

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Google’s Search Growth Defies the AI Hype, but that is only Half of the Story

by Carsten Krause, Chief Editor, The CDO TIMES, July 28th 2025

The SparkToro Findings Surprised Everyone: In mid-2024, SparkToro, part of SemRush, released eye-opening research suggesting that Google’s search engine isn’t just holding its ground against the AI chatbot wave – it’s thriving. According to SparkToro co-founder Rand Fishkin, Google Search volume grew over 20% in 2024, a remarkable feat for a mature product (Source: sparktoro.com). In raw numbers, Google itself disclosed it handled more than 5 trillion searches in 2024 – roughly 14 billion searches each day. By SparkToro’s estimates, that’s 21.6% year-over-year growth in search querie. Far from being cannibalized by AI, Google saw increased usage, a trend even Google’s CEO Sundar Pichai hinted at when he noted that new AI search features were “encouraged [by] an increase in search usage” among those testing them.

Perhaps the most striking claim from SparkToro was the sheer gap between Google and the buzzy newcomer ChatGPT. In 2024, Google reportedly fielded 373 times more searches than ChatGPT. Put another way, OpenAI’s ChatGPT – the poster child for AI search disruption – accounted for only about 0.25% of search volume versus Google’s 93.5% share (source: searchengineland.com). SparkToro even likened ChatGPT’s daily “search-like” prompts (around 37.5 million) to the scale of Pinterest’s search queries (~20 million/day), and only about one-third of privacy-focused DuckDuckGo’s (~108 million/day).

In short, by SparkToro’s math, ChatGPT in 2024 was a rounding error in search market share, eclipsed not just by Google but even by second-tier engines like Bing and Yahoo.

These findings landed amid rampant speculation that AI chatbots were siphoning users from traditional search. Yet SparkToro’s data implies the opposite: Google’s search business grew despite the AI frenzysparktoro.comsearchengineland.com. This runs counter to the “Google killer” narrative that took hold when ChatGPT’s popularity exploded. However, before declaring the search wars settled, it’s important to scrutinize how SparkToro arrived at these numbers – and whether they tell the full story.

Crunching the Numbers: Google vs. ChatGPT by the Metrics

Figure: Daily search queries in 2024 – Google dwarfs all others. Google averaged ~14 billion searches per day, compared to ~613 million for Bing, ~202 million for Yahoo, ~108 million for DuckDuckGo, and an estimated ~37.5 million for ChatGPT (sources: sparktoro.comsearchengineland.com).

SparkToro’s analysis pulled together disparate data sources to compare Google and ChatGPT on an apples-to-apples basis. First, Google’s side of the equation was informed by the company’s own disclosure of “more than 5 trillion searches in 2024”. That figure – roughly 14 billion a day – aligns with clickstream panel studies that SparkToro and analytics firm Datos used to verify Google’s volume. In fact, StatCounter’s third-party tracking shows Google maintains about 91–93% of global search engine market share in 2024 (source: keywordseverywhere.com). For context, Google’s flagship site received on the order of 84–105 billion visits per month in 2023–2025, dwarfing all other websites (source: semrush.com). Users spent an average of 12–13 minutes per session on Google.com, often rapidly clicking through multiple results (5+ pages per visit on average) in search of answers.

Measuring ChatGPT against this backdrop is trickier – it’s not a traditional search engine, and OpenAI hasn’t published “query” totals in the same way Google has. SparkToro therefore used a bit of algebra and external studies to derive ChatGPT’s “search-equivalent” usage. OpenAI CEO Sam Altman revealed in late 2023 that ChatGPT was handling about 1 billion messages per day. But not every message is a unique search query – many are back-and-forth replies in a single conversation. A Semrush analysis of ChatGPT usage found the median conversation length is 3 messages, and the average about 8 messages. Using that, SparkToro estimated ChatGPT sees roughly 125 million distinct prompt sessions per day (i.e. 1 billion / 8).

Next comes a key adjustment: not every ChatGPT session is analogous to a web search. In fact, Semrush’s study of some 80 million ChatGPT prompts found only 30% had “search-like” intent – things like asking for information, comparisons, or recommendations that one might otherwise Google. The majority (~70%) of ChatGPT’s usage is for other purposes: writing code, brainstorming content, solving math problems, drafting emails, you name it (source: sparktoro.com). Those aren’t searches for websites or facts in the traditional sense. So SparkToro multiplied by this 0.3 factor, yielding about 37.5 million search-equivalent queries on ChatGPT per day. In essence, they treated ChatGPT as conducting ~37.5M “informational searches” daily, versus Google’s 14B.

Finally, they folded ChatGPT into a classic market share pie. The result: ChatGPT at 0.25% of global search share in 2024, versus Google at 93.57%, Bing ~4.1%, Yahoo ~1.35%, and DuckDuckGo ~0.73%. Figure 1 above visualizes just how massive Google’s lead is. It’s not even close. ChatGPT’s search-like usage was on the order of one 373rd of Google’s.

SparkToro’s methodology is transparent, but it relies on a chain of assumptions: that 1 billion messages/day was accurate and sustained; that 8:1 message-to-query ratio holds; and that 30% of prompts truly parallel search. Each step introduces possible error. For example, if ChatGPT’s average conversation length were shorter, or if more people start using ChatGPT’s browsing tools for current info, the gap with Google could narrow. Conversely, if many ChatGPT prompts are follow-ups refining an answer (not new intents), one could argue even fewer are equivalent to a fresh Google search. Either way, the exercise highlights a crucial point: ChatGPT isn’t mostly being used as a search replacement today – it’s largely used for other tasks.

Did Google Really Gain from AI? (Peeking at the Fine Print)

SparkToro’s finding of 21.6% search growth in one year raised some eyebrows. If users had a shiny new toy in ChatGPT, how on earth did Google searches surge more than 20%? Part of the answer might be Google’s own AI infusion. In 2023–24, Google rolled out generative AI snippets (Search Generative Experience, or SGE) to augment search results. Instead of users asking one question and leaving, the AI Overview can encourage follow-up queries. Pichai noted testers were “seeing an increase in search usage” with the new AI results. And indeed, it appears those users searched more. SparkToro’s data (from Datos’ device panel) showed searchers did significantly more queries in 2024 than in 2023, not only on Google.com but across verticals like Images, News, and Maps. In particular, Google’s AI-driven Search Overviews did not cannibalize search volume – they stimulated it.

However, more searching did not mean more clicking. There’s a dark flip side to AI answers: they often satisfy the query on the spot, resulting in no click to any website. By the end of 2024, an estimated 60% of Google searches ended without a click to external content (source: searchengineland.com). That’s up from roughly 50% a few years prior. So while Google saw record search activity, publishers and marketers felt the pinch – fewer visitors coming through. Internal Google changes (like showcasing AI summaries or direct answers) can increase engagement within Google while reducing traffic out. In SparkToro’s words, AI answers “sure as heck” increased searches, but they “do seem to kill clickthrough rates”. One study found that on pages with Google’s AI overview, the organic click-through rate (CTR) to websites dropped 70% and even paid ad CTR dropped 12%. In other words, Google might be feeding users more answers without them ever leaving Google’s ecosystem.

From a methodology standpoint, SparkToro’s heavy reliance on third-party data (Datos panel, Semrush clickstream) means their results are only as good as those inputs. The Google growth figure came from a desktop-only panel extrapolated to all devices – if mobile or certain geographies behaved differently, the true global growth might be lower (or higher). Also, SparkToro’s comparison treated each ChatGPT prompt as independent, but many ChatGPT sessions replace not one search, but several. For instance, planning a trip via ChatGPT might replace a series of Google searches (flights, hotels, weather, etc.) with one conversational session. So measuring raw query counts could understate the competitive impact on multi-step research tasks. These nuances don’t invalidate SparkToro’s core point – Google is still massive – but they remind us to be cautious. Big picture: by mid-2024, Google’s search business remained a juggernaut, seemingly unfazed – or even boosted – by the rise of AI assistants, at least when looking at high-level query counts.

Contrarian Signals: Is ChatGPT a Bigger Threat Than It Looks?

Not everyone is convinced that Google’s lead is as secure as the 373× stat suggests. In fact, some analysts argue that Google’s dominance has begun to erode in subtle ways that query totals alone don’t capture. A report from The New York Times in mid-2025 – echoed by analysts at Third Bridge – suggested Google’s global search market share dipped below 90% for the first time in a decade, thanks largely to users flocking to AI tools (source: economictimes.indiatimes.com). These analysts estimated that “OpenAI’s ChatGPT is now handling 15–20% of Google’s daily search volume”economictimes.indiatimes.com. That astounding claim (implying billions of daily AI queries) conflicts with SparkToro’s 0.25% figure – possibly because it counts all ChatGPT queries, not just search-like ones. If ChatGPT handles 1B+ prompts/day as of 2025 (which OpenAI’s data confirms (srouse: digitalinformationworld.com), that is roughly 7% of Google’s 14B – still single digits, but higher than 0.25%. Some bullish analysts may even be projecting future growth, anticipating ChatGPT’s share to hit the teens. While the exact figures differ, the directional trend is that ChatGPT’s usage has exploded, and some portion of that directly substitutes for searches people would have otherwise done on Google.

Indeed, by mid-2025 ChatGPT had grown into one of the top websites in the world. It was the 5th most visited website globally in June 2025, with around 5.4 billion visits that month (source: digitalinformationworld.com). For reference, Google.com saw about 96.5 billion visits that month – so Google still had ~18× more traffic by visits. But ChatGPT’s rise has been swift: it jumped from ~1.6 billion monthly visits in early 2024 to over 5 billion by spring 2025. In late 2024 alone, monthly ChatGPT visits leapt from 3.1B in September to 3.7B in October as new features rolled out. According to Similarweb data, ChatGPT’s web traffic was basically flat in mid-2023 after its launch hype, but then accelerated again through 2024 and into 2025(source: digitalinformationworld.com). This suggests that usage of AI assistants is not a fad that peaked early – it’s evolving and possibly entering a second growth phase as AI integrates more into daily workflows.

Qualitatively, user behavior hints at cracks in Google’s armor for certain types of queries. For example, consider the software developer community: questions that used to send programmers searching Google and clicking Stack Overflow are now increasingly posed to ChatGPT. In early 2023, developer Q&A site Stack Overflow experienced a sudden traffic decline – down ~14% from March to April 2023 alone (srouce: gizmodo.com) – which coincided with the rise of ChatGPT as a coding aid. By May 2023, Stack Overflow openly stated “ChatGPT is making a dent in [our] traffic”. Why? Because many coders found it faster to get a single, synthesized answer from ChatGPT than to scour forums The same story played out in educational help: homework-help site Chegg saw its new user growth plummet and blamed ChatGPT for offering instant answers that undercut its service. In short, for highly structured Q&A needs (programming help, homework solutions), ChatGPT became a compelling alternative to the Google search + website browsing routine. Google’s overall numbers didn’t collapse, but these niche declines illustrate how specific search verticals can be disrupted by a better AI experience.

On the other hand, some contrarians argue the threat is overblown – at least in the near term. They point out that Google’s core business (web search, especially for commercial queries) has proven resilient. Many consumers still default to Google for things like shopping searches, local business info, or navigating to websites – areas where ChatGPT isn’t as convenient or up-to-date. In fact, recent data showed Gen Z users actually increased their use of Google for e-commerce searches in 2023–2024 (source: finance.yahoo.com), despite talk of them using TikTok or AI. Moreover, one can view ChatGPT not purely as a rival but as a customer or collaborator to Google: OpenAI’s ChatGPT relies on cloud computing (and notably signed a deal to use Google Cloud for some services , and Google itself is developing competing AI (Gemini) to ensure it doesn’t lose the AI race. By early 2025, Google’s own Gemini AI assistant had quietly amassed hundreds of millions of users (a Morgan Stanley survey claimed 450 million users for Gemini, second only to ChatGPT) (soucre: io-fund.com). Google is hedging its bets by building AI into its products – from search to enterprise tools – effectively playing both offense and defense. This duality led one Guardian analyst to note that “ChatGPT is arguably a threat to Google’s search business, but it’s also a customer”, capturing the nuanced relationship between incumbents and disruptors (source: theguardian.com).

Where both sides agree is that user expectations are shifting. People have now seen what it’s like to get a single, conversational answer with no ads. Even if Google’s numbers haven’t cratered, the perception of search is changing. A July 2025 feature in The Economic Times summed it up: users are drawn to ChatGPT’s “comprehensive, ad-free answers” and find it “better than Google for asking questions” like planning a complex vacation itinerary (source: economictimes.indiatimes.com). Google’s results, in contrast, often require sifting through ads and multiple sites. This doesn’t mean everyone will dump Google overnight – but it hints that Google’s famed user experience advantage is being challenged. The mere fact that Google felt compelled to launch its own chatbot-style search mode (the SGE “AI mode”) is evidence that the threat is forcing evolution. As one tech analyst put it, Google’s official stance is that “A.I. is a tailwind for search, not a headwind”, meaning they believe AI will ultimately drive more search usage. The reality is likely more complex: AI is both a tailwind (making search more interactive) and a potential headwind (enabling alternatives to traditional search).

Different Tools, Different Purposes: How People Use Google vs ChatGPT

To truly understand the competitive dynamic, we must recognize that Google Search and ChatGPT often serve different user goalds. A CMO might ask: are people actually using ChatGPT to search the way they use Google? The answer, so far, is largely no – they use it to do things. Let’s break down the typical use cases:

Google is the master of immediate, transactional queries. Think of the countless times you’ve typed a quick question or navigational query: “weather tomorrow”, “plumber near me”, “Acme Corp website”, “buy iPhone 15”. These are classic Google searches – often one-and-done tasks with a definitive answer or destination. They have strong intent signals (local, commercial, navigational) and Google excels at surfacing a relevant result or instant answer. As one SEO agency observed, Google searches tend to be immediate and specific (source: kickstartseo.co.uk). Users expect Google to retrieve an authoritative webpage (or map location, or product listing) almost instantaneously. There’s also the trust factor: if you need the official site or a real-time update (news, stock prices, etc.), Google’s indexed web results are the go-to. Notably, Google is also where the advertising dollars concentrate – high-intent queries that can lead to a purchase are exactly what Google monetizes so well with search ads.

ChatGPT, in contrast, shines in complex, exploratory, and generative tasks. Users treat ChatGPT more like a consultant or creative partner than a directory of links. Many ChatGPT prompts are things you’d never plug into a search box.

For example: “Help me plan a marketing strategy for my new bakery”, or “Explain why my website’s traffic might be dropping and how to fix it”. These are multi-part, conversational prompts – the kind you’d break down into dozens of Google queries and hours of research reading blogs and guides. ChatGPT condenses that into a single dialogue.

As one comparison put it, “Google’s for finding; AI’s for understanding.” (source: kickstartseo.co.uk)

Rather than just retrieving facts, ChatGPT can generate text, code, and images, or simplify and synthesize information. About 70% of ChatGPT’s usage is non-search precisely because people use it for tasks like coding help, writing copy, drafting emails, solving problems, or just for fun and curiosity (e.g. brainstorming names or getting a quick translation).

Another qualitative difference is interaction style. Google is a “type and get results” experience – if the first try doesn’t yield what you need, you reformulate your query (which, as data shows, users often do – about 17–29% of the time they immediately adjust their search terms on desktop/mobile (source: keywordseverywhere.com). ChatGPT, on the other hand, encourages refining via conversation: you ask a broad question, get an answer, then say “thanks, now give me more detail on X” or “what about scenario Y?”. It’s iterative but with memory of context. Users report this is especially useful in planning or learning scenarios – e.g. interactive troubleshooting, or step-by-step planning (trips, projects, recipes). A travel example: one user described using ChatGPT to plan a vacation, preferring it because the answer wasn’t split across 10 different sites full of ads. ChatGPT provided a cohesive itinerary suggestion in one go, something Google would make you piece together.

That said, ChatGPT’s conversational magic has limits. It lacks real-time knowledge (unless augmented with plugins or browsing). It may produce plausible but incorrect answers (the well-known “hallucination” issue), which can be a deal-breaker for fact-finding missions. Many users inherently trust that Google will show multiple sources, including official ones, so they can cross-verify information – whereas an AI might state something confidently without citation.

Trust also ties to enterprise integration: Google search is a public web tool, whereas ChatGPT can be more closed-loop or customized. We see companies integrating ChatGPT (or similar LLMs) with their internal data – something you’d never do with Google. For instance, software firms have started using ChatGPT-based assistants fine-tuned on their documentation to help engineers debug or answer customer queries. In that sense, ChatGPT is acting as a new interface for knowledge retrieval beyond the public internet, including proprietary knowledge bases. Google is trying to play in this space (e.g. via Cloud’s Vertex AI or enterprise search products), but it historically excelled at public web indexing, not a company’s private data.

In summary, Google and ChatGPT aren’t exact substitutes – they’re more like overlapping circles in a Venn diagram. The overlap (roughly 30% of ChatGPT use cases, per Semrush (source: sparktoro.com) includes straightforward information queries, comparison shopping questions, or basic know-how (“How do I calculate mortgage payments?”).

The non-overlap: Google dominates for quick fact retrieval, location-based queries, latest news, and anything where up-to-the-minute accuracy or official sources matter.

ChatGPT dominates for open-ended questions, advice, creative generation, summarizing or explaining complex topics, and tasks one might previously have done by stitching together info from multiple Google results. The key for enterprises is to recognize where your audience’s “search intent” might be shifting towards chat-based tools and where it remains firmly in the search engine realm.

Threat Assessment: Is Google’s Core Search Business Safe?

From a market share standpoint, Google’s core search franchise appears safe for now – none of the AI upstarts have dented its usage enough to show up beyond a rounding error in the stats.

But “safe” is relative.

Google’s position is a bit like a reigning champion boxer: still winning every round, yet suddenly facing an unconventional challenger that forces a change in stance. The real threat to Google is not that ChatGPT will replace it overnight, but that it will change user expectations and chip away at high-value search behaviors over time. Here’s how to think about the threat:

1. Volume vs. Value:

Google can afford to lose millions of low-value searches and hardly feel it – especially if those are, say, homework questions from students (not lucrative for ads) or idle curiosities.

However, if users start using AI assistants for queries that do have high commercial value (e.g. researching a pricey electronics purchase, finding a hotel, or seeking legal/medical guidance), that’s more concerning.

Currently, many such searches still go through Google, but anecdotal evidence shows early adopters are trying ChatGPT or Bing Chat for things like product research (“What 4K TV should I buy under $1000?”) or travel planning.

If those use cases grow, Google could feel a revenue pinch even if overall query volume stays high. So far, the ad dollars haven’t dramatically shifted – Google’s search ad revenue continues to grow (Alphabet reported a 14% revenue rise in Q2 2025 (source: economictimes.indiatimes.com). But Google is keenly aware of protecting the valuable queries that drive that revenue. Hence its quick move to inject AI summaries on results pages for product queries, hoping to keep those shoppers on Google.

2. The Integration Wildcard:

ChatGPT’s threat is as much about distribution as direct usage. Unlike search engines which people visit directly, AI assistants are being woven into other products and platforms. OpenAI has released a ChatGPT API, and companies are building it into office software, customer service bots, and more. Microsoft’s Bing, for example, integrated GPT-4 into its search – bringing ChatGPT-like answers to the Bing interface.

Apple, as rumored, might bake AI assistance deeper into devices. If AI assistants become ambient (available everywhere), users might bypass Google more often without consciously thinking “I’m going to ChatGPT’s website.” For instance, a professional using Microsoft 365 can ask the built-in Copilot (powered by OpenAI) to analyze a spreadsheet or draft an email, tasks they might have once searched templates or advice for on Google. Similarly, voice assistants are getting smarter – we may soon ask Alexa or Siri complex questions that they answer via an LLM, not a web search. Google, to its credit, is also a player here (it’s integrating generative AI into Gmail, Docs, Android, and so on), but its business model has always revolved around web search traffic. The more user questions get answered away from the traditional search engine (even if by Google-powered AI in another context), the more Google’s advertising fortress may be circumvented.

Similarly, Open Ai’s Acquisition of the AI wearables shop IO, is making a significant move intoy the hardware space. The IO team includes former Apple Design chief Jony Ive and a team of apple designers. Again, this would be a trheat since they would be bypassing browser based search.

3. User Trust and Habits:

Google’s greatest asset is habit – “Googling” is second nature to billions. ChatGPT has to overcome that inertia, and trust is part of it. Early adopters might rave about ChatGPT, but many average users still approach it cautiously, if at all. A Pew Research survey in early 2025 showed that while a majority of younger adults had tried AI chatbots, older demographics lagged behind (source: nerdynav.comnerdynav.com).

Trust is also about reliability: Google’s results can be imperfect or spammy, but outright factual errors by Google are rare (since it’s showing sources). ChatGPT, in contrast, can sometimes fabricate answers. Enterprises worry about that – hence we see companies like Bloomberg developing their own in-house LLM trained on vetted financial data, rather than relying on ChatGPT for mission-critical info. If OpenAI and others can improve factual accuracy and cite sources, trust in AI answers will grow. Google is certainly not standing still on this either: features like SGE cite their sources, and Google’s own Bard now provides references and has real-time information. The competition in trust and accuracy is on.

So, is the threat to Google “real, overblown, or simply evolving”?

It’s evolving. In 2023, alarmists thought ChatGPT usage would immediately cannibalize Google – that was overblown given Google’s usage actually rose. But to say there’s no threat would be short-sighted. The threat is just not a head-on replacement of Google;

it’s a gradual shift in how certain queries are performed and how users expect to get information. Google’s core business is likely secure in the next year or two – we’re not going to see 50% of people stop googling things.

However, we will see continued diversification of search behavior. By 2025 and 2026, it’s plausible that a small but significant percentage of searches (maybe low single-digit percentages globally, but higher in certain niches) will have moved to AI assistants or alternative platforms. If, say, 5% of global search volume shifts away from traditional search, that could equate to hundreds of millions of queries per day not happening on Google – which is not trivial. And if those include valuable commercial queries, Google will feel it.

Google’s strategic response – integrating AI deeply into search – indicates it is treating the threat as real. The search bar might evolve into more of a conversational interface over time, and Google is willing to cannibalize some of its classic “10 blue links” experience to stay ahead. The upshot: Google’s search business isn’t dying, but it is transforming under competitive and technological pressures for the first time in decades.

Adaptation Strategies: Navigating the Coexistence of Search and AI

For enterprises and marketers, the message is clear: search and AI-driven discovery will coexist, and strategy can’t ignore either. C-level leaders in marketing, SEO, and digital strategy should take a dual-pronged approach:

• Double Down on Core SEO (But Evolve It): Google remains the gateway to ~90% of consumer search queries, so traditional SEO is still mission-critical. Ensuring your company’s content ranks well
  
  on Google for relevant queries is as important as ever – perhaps more, since organic spots are giving way to AI snippets and zero-click answers. The basics (technical SEO, quality content, mobile optimization, site speed, etc.) still apply. What’s changing is what it means to “rank”. Your content might need to appease both algorithms and AI summaries. For example, content that succinctly answers questions (featured-snippet style) can be beneficial for both getting a Google snippet and being used in an AI answer. Structured data (schema markup) that helps Google understand and feature your content is wise to implement; it could also help AI models interpret your content accurately. One mistake to avoid is abandoning Google for the new shiny thing – some businesses have over-rotated to AI and let their search presence slip.
  
  Given Google’s enduring dominance, that’s like “training for the 2028 Olympics while missing the 2024 qualifiers,” as one marketer quipped. In other words, don’t neglect the SEO fundamentals that drive traffic today.
• Optimize Content for AI Visibility:
  
  Parallel to traditional SEO, we now have AI optimization (AIO?). This is more nascent, but forward-thinking teams are already experimenting. What does it mean practically? First, understand how AI assistants get information. ChatGPT, for instance, was trained on vast web data (up to a cutoff) and now can use plugins or browse when explicitly invoked. There’s no “AI ranking algorithm” akin to Google’s PageRank – but being referenced by authoritative sources, being part of known datasets, and having well-structured, easy-to-parse content can only help. Some strategies emerging:
  
  • Publish Authoritative, Well-Structured Content:
    
    AI models trained on internet data will have ingested a lot of content up to 2021 (and beyond, as they update). If your company produces high-quality articles, research, or documentation that is widely cited, that content is more likely to be reflected correctly in AI answers. Conversely, thin or duplicate content won’t register. Example: When someone asks ChatGPT about the best CRM software, will it “know” about your product? Only if information about your product was prominent in its training or accessible via tools.
    
    This means investing in thought leadership and reference content that gets cited (so it seeps into the AI zeitgeist).
    
  • Leverage Structured Knowledge Bases:
    
    Enterprises are building their own chatbots trained on their data (for internal use or for customers). If you have a rich knowledge base or FAQ, consider implementing an AI assistant on your site. This not only provides a new interface for users (some customers might prefer asking a question in a chat on your site versus using your site’s search function), but it prepares you for a future where customers come to expect conversational support. Companies like OpenAI and Microsoft are making it easier to spin up custom chatbots with your content. For marketing leaders, this is a chance to own the conversation about your brand in the AI space. Instead of hoping a generic AI yields the correct answer about your product, you provide a branded AI that you can control.
    
  • Monitor AI Mentions and Feedback:
    
    Just as brands monitor search engine rankings and social media mentions, it’s time to monitor AI responses related to your business. There have been instances where ChatGPT or Bing gives outdated or incorrect info about a company or product. Enterprises should treat this like a reputation/accuracy issue. If misinformation crops up, it might be worth publishing clarifications on your site (which eventually AI will ingest) or even using channels like OpenAI’s feedback tools to suggest corrections. It’s a new frontier for PR and SEO teams alike.
    
• Balance Content for Humans and AI – in the spirit of Elevated Collaborative Intellgence^TM
  
  As AI usage grows, a challenge emerges: writing content for two audiences – human readers and AI summarizers. For example, a classic SEO article might be 2,000 words of in-depth content (great for a human researcher), but an AI might ingest that and only ever convey a one-paragraph summary of it to the end-user. Some marketing teams worry that their content might never be seen in full, just distilled by AI. This is a valid concern – it might mean the value of top-of-funnel content shifts from lead generation to brand awareness (i.e. an AI might quote your brand’s expertise but the user doesn’t click through). One way to adapt is to ensure your content is structured in a hierarchical way: a punchy summary or key takeaways up top (which an AI or snippet might grab), followed by detail for those who click in. This way you cater to both the skimmers (and AI) and the deep readers. Some enterprises are also experimenting with content that explicitly invites engagement, e.g. tools, interactive widgets, or community features that an AI can’t replicate easily – giving people a reason to click through from an AI answer.
  
• Advertising and Conversion Strategy:
  
  If more answers are delivered directly on Google or via AI, how do you get in front of customers? This is where marketers have to be creative. Google’s SGE presents opportunities to still appear (for example, if your content is cited as part of an AI answer, that’s a bit of visibility, though not a clickable link today). Google will likely introduce new ad formats within AI results if usage grows – keep an eye on that and be ready to test AI-tailored ads (Google has hinted at this). Meanwhile, consider non-search channels that are rising: if Gen Z is searching on TikTok for some things, maybe your content needs to be there too (short video answers). If AI assistants are used, maybe sponsoring or partnering with certain platforms (for instance, getting your service integrated as a plugin or tool within an AI ecosystem) could be the future equivalent of SEO. We already saw early moves: e.g., travel companies like Expedia built a ChatGPT plugin so that when users want to plan trips conversationally, Expedia is the fulfillment mechanism. That’s a new kind of “distribution channel” for content and services.
• Upskill Your Teams in AI: Lastly, enterprise leaders should ensure their teams leverage AI for efficiency. This isn’t directly about search traffic, but it’s the other side of the coin – using AI to enhance your marketing and SEO efforts internally. Content writers can use GPT tools to draft and brainstorm (though human editing and originality remain crucial). SEO analysts can use AI to crunch large data sets or generate meta descriptions at scale. Teams that embrace AI for productivity will free up time to focus on strategy and creative work. At the same time, set guidelines to maintain quality (the web is already seeing an explosion of AI-generated content; the last thing you want is your brand putting out low-quality AI spam that hurts your SEO and reputation).

Case studies are emerging of companies finding this balance. KickstartSEO, a UK agency, publicly shares how they split their focus: staying “unmissable” on Google today while also preparing content for AI platforms (source: kickstartseo.co.uk):

• Continue to invest in traditional SEO to rank in Google’s results, including meeting Google’s E-E-A-T trust guidelines, but simultaneously restructure content so that it’s easily digestible by AI.
• Rather than writing keyword-stuffed FAQs, they craft comprehensive answers in natural language that “AI systems want to reference.
• Add structured data (schema) to pages, anticipating that machine-readable context will only grow in importance
• Partner with OpenAI. E.g. educational company Chegg opted to create “CheggMate,” an AI tutor that uses ChatGPT under the hood but is specialized for students’ needs. This is a defensive and adaptive strategy – if you can’t beat them, join (or integrate) them. We can expect more enterprises to launch AI chatbot extensions of their services in the coming year.

The coexistence of search and LLM-based tools means marketing funnels may diversity:

Some customers will arrive via the old path (search → click → website),

others might arrive via an AI assistant (either as a referral or not arrive at your site at all, having gotten what they need from the AI).

Marketing leaders must optimize for both paths and find ways to measure and influence the AI-driven customer journey, which is a nascent field.

The Future of Enterprise Content Discovery and Distribution

Peering ahead 12–18 months, we can sketch a few likely developments in the Google vs ChatGPT saga and what they mean for enterprise content:

1. Hybrid Search Experiences Become Mainstream:

Google is likely to push its generative AI search features (currently experimental) to all users. This means a larger percentage of Google queries will trigger an AI-generated overview at the top of results. Users may start to expect a conversational summary first, with traditional results as backup. Microsoft’s Bing will continue to differentiate by closely integrating chat; it may not gain huge market share, but it will keep forcing Google’s hand on innovation. We might also see voice and multimodal search (images, voice, text all combined) growing – e.g. speaking a complex query to your phone and getting a spoken answer or a visual result compiled by AI. For enterprises, content might need to be optimized not just for text snippets, but for how an AI might present it (could your how-to article be summarized into an audio answer or a step-by-step list by an AI? If so, is it structured clearly?).

2. ChatGPT (and its peers) Get More Real-Time and Integrated:

OpenAI’s partnership ecosystem will expand. Already, OpenAI has plugins that let ChatGPT pull live information (e.g. web browsing, Expedia for travel, Instacart for groceries). Expect ChatGPT to become more app-like for end-users – possibly even a mobile assistant that can tap your calendar, emails, or other personal data with permission. This could inch it closer to certain Google functions (like Gmail or Google Assistant). By late 2024 or 2025, OpenAI or others might release enterprise versions that can securely connect to internal data, making ChatGPT a sort of front-end to corporate knowledge (some companies are already doing this themselves with the API). As ChatGPT becomes more integrated and real-time, it may handle more “search-like” tasks confidently – e.g. answering “What’s the latest news on XYZ?” by actually retrieving news (Bing’s mode already does this to an extent). This could make it more of a direct Google substitute for some users, especially if OpenAI improves citations and source transparency in those answers.

3. Continued Growth, but No Singular “Google Killer”:

In the next 12–18 months, it’s realistic that ChatGPT’s user base will continue to grow – perhaps reaching 1 billion+ monthly active users given its current trajectory (it reportedly had ~180 million daily users as of mid-2025 (source: digitalinformationworld.com), and 800 million weekly users (source: demandsage.com). However, this growth doesn’t necessarily come at Google’s direct expense one-to-one. Some growth is new usage (people asking things they might not have bothered to search before, because the conversational format invites more curiosity), and some will siphon from other platforms (forums, niche websites, even time spent on social media or YouTube for how-to content could shift to asking ChatGPT).

Google will still likely see some query growth, especially as global internet access expands and as AI features drive incremental searches. So we may end up in a world by 2026 where

Google is bigger than ever in absolute terms, yet a smaller piece of the overall search+Q&A pie.

Perhaps Google goes from ~93% of traditional search to something like 90% or high 80s, as AI assistants and alternative search methods take a sliver. That would be a notable change after a decade of almost static search market shares.

4. Enterprise Content Distribution Splits:

Content creators like us (news sites, marketers, etc.) will increasingly wrestle with how to distribute content in an AI-mediated world. We might see more publishers strike deals to feed content to AI models in exchange for visibility or compensation. For example, just as publishers optimized for Google News and featured snippets, they might in the future provide structured feeds for AI consumption.

Already, some news organizations are in talks about licensing content to AI companies to ensure accuracy and get paid for usage. If such models emerge, enterprise content strategy could involve publishing via APIs to AI services in addition to publishing on the web. Think of it as a new channel: just as social media became a channel to publish on (with its own rules and formats), AI could become another channel (e.g., providing a knowledge base that AI assistants can tap with attribution).

5. New Metrics of Success:

Today we measure SEO success with impressions, clicks, rankings.

Tomorrow, we might measure “AI mentions” or “AI referrals.” If someone uses an AI assistant to find a product and it recommends your company (even if no click happens), that’s brand exposure. We might see analytics platforms or AI vendors offering metrics on how often a brand is appearing in AI outputs.

Smart enterprises will start tracking this. It could also influence content: for instance, if an e-commerce site learns that an AI frequently recommends their product when asked about “best budget smartphones,” they’ll want to keep that advantage (ensuring their product info remains accessible and accurate to the AI, perhaps even via an official plugin or feed).

In essence, the next 12–18 months will be about integration and adjustment. AI won’t kill search; it will reshape it and spawn parallel channels. Companies that adapt by being present wherever their customers seek answers – be it on Google, Bing, ChatGPT, or some enterprise AI concierge – will thrive. Those that stick to a siloed approach (just SEO or just traditional content) might find their audience slipping away in fragments.

Finally, a cultural shift: consumers and professionals alike will become more comfortable using AI as part of daily life. The novelty will wear off; these tools will just be another option. Much like mobile vs desktop – we ended up with both, and strategies had to become mobile-friendly – we are heading toward a world where search coexists with AI assistance, and digital strategy must be AI-friendly.

The CDO TIMES Bottom Line

Google isn’t losing sleep – yet.

SparkToro’s analysis and various traffic stats reinforce that Google Search remains the undisputed king of discovery, even seeing a usage bump in the age of AI. ChatGPT and other AI tools, while growing explosively, currently pose more of a complementary role than a direct threat in pure search volume.

In 2024, ChatGPT was handling a fraction of a percent of the queries Google does. For core search needs – especially those with commercial, local, or up-to-the-minute intent – Google’s entrenched index and reliability keep it on top.

But make no mistake, the search landscape is evolving, not stagnant.

ChatGPT’s meteoric rise to hundreds of millions of users shows an appetite for conversational, on-demand knowledge.

Users are discovering new ways to get answers leveraging AI and their prompts, and those ways often bypass traditional web search. Certain niches (coding help, education, complex research) have already seen users shift from googling to chatting. Google’s answer has been to transform itself – integrating AI summaries and chat features into Search – blurring the line between engine and assistant. Over the next year, expect hybrid search-AI experiences to become normal, and for alternative platforms (Bing, AI chatbots, voice assistants) to chip away small but meaningful pieces of user attention.

For enterprise marketers and digital leaders, the mandate is clear: prepare for a world of “both/and,” not “either/or.”

Continue to prioritize Google SEO and classic search marketing, as that’s where the bulk of traffic and revenue lies today.

At the same time, adapt your content and strategy for AI-driven discovery – structure your information so that it’s easily consumed (and correctly interpreted) by AI models, and seek ways to participate in the AI ecosystem (whether through partnerships, plugins, or your own AI tools).

Educate your teams and stakeholders that we’re in an era of dual optimization: one for algorithms that rank links, another for algorithms that generate answers.

The savvy CDO or CMO will invest in content that serves dual purposes – authoritative and well-organized to please Google’s ranking factors, and concise and semantically clear to feed AI answers.

They’ll also keep a close eye on user behavior shifts: Are fewer people coming from search to certain content pieces? Are more finding us via AI recommendations?

Use those insights to adjust tactics.

Crucially, don’t get caught in false binaries – it’s not “SEO vs AI” or “Google vs ChatGPT.” It’s about leveraging the strengths of each. Google is unparalleled for reaching a broad audience with intent; ChatGPT offers depth of engagement and a chance to build more personalized interactions.

The Bottom line is that the data tells us Google’s dominance will not vanish in the next year, but the terms of engagement are changing. Just as mobile disrupted desktop or social media created new marketing channels, AI assistants are expanding how people find information. Google and ChatGPT will likely coexist, each influencing the other’s evolution. Enterprises that adapt to serve customers on both fronts will ensure they remain visible, relevant, and competitive in the new era of search.

In a nutshell: keep your SEO solid, get your AI and HI + AI strategy started, and meet your audience wherever their questions are asked – be it a search box, an AI assistant or a chat prompt.

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Do You Need Help?

Consider bringing on a fractional CIO, CISO, CDO or CAIO from CDO TIMES Leadership as a Service. The expertise of CDO TIMES becomes indispensable for organizations striving to stay ahead in the digital transformation journey. Here are some compelling reasons to engage their experts:

1. Deep Expertise: CDO TIMES has a team of experts with deep expertise in the field of Cybersecurity, Digital, Data and AI and its integration into business processes. This knowledge ensures that your organization can leverage digital and AI in the most optimal and innovative ways.
2. Strategic Insight: Not only can the CDO TIMES team help develop a Digital & AI strategy, but they can also provide insights into how this strategy fits into your overall business model and objectives. They understand that every business is unique, and so should be its Digital & AI strategy.
3. Future-Proofing: With CDO TIMES, organizations can ensure they are future-proofed against rapid technological changes. Our experts stay abreast of the latest AI, Data and digital advancements and can guide your organization to adapt and evolve as the technology does.
4. Risk Management: Implementing a Digital & AI strategy is not without its risks. The CDO TIMES can help identify potential pitfalls and develop mitigation strategies, helping you avoid costly mistakes and ensuring a smooth transition with fractional CISO services.
5. Competitive Advantage: Finally, by hiring CDO TIMES experts, you are investing in a competitive advantage. Their expertise can help you speed up your innovation processes, bring products to market faster, and stay ahead of your competitors.

By employing the expertise of CDO TIMES, organizations can navigate the complexities of digital innovation with greater confidence and foresight, setting themselves up for success in the rapidly evolving digital economy. The future is digital, and with CDO TIMES, you’ll be well-equipped to lead in this new frontier.

Do you need help with your digital transformation initiatives? We provide fractional CAIO, CDO, CISO and CIO services, do a Preliminary ECI and Tech Navigator Assessment and we will help you drive results and deliver winning digital and AI strategies for you!

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The promise of generative AI isn’t that it will replace people—it’s that it will become an indispensable partner. A new Harvard Business School (HBS) working paper written with Procter & Gamble (P&G) and Wharton researchers reminds us that AI isn’t just a productivity tool; used well, it acts like a cybernetic teammate. The field experiment, the largest of its kind, placed 776 P&G professionals into real innovation challenges and compared individuals and teams with and without access to generative AI. The findings are explosive: with the right AI support, a single employee matches the performance of a traditional two‑person team and finishes the work faster – 2025 Harvard Study For executives trying to make sense of the hype, this research offers a clear guidepost—and it aligns exactly with the HI + AI = ECI™ framework featured in our upcoming book Elevated Collaborative Intelligence.

Harvard Research: AI as a True Teammate

A major insight from the study is that AI doesn’t just automate tasks—it amplifies human collaboration. When individuals used AI to tackle product‑innovation problems, their solutions improved by 0.37 standard deviations, matching the quality delivered by two‑person teams without AId3.harvard.edu. Teams using AI improved by 0.39 standard deviations, and both individuals and teams spent roughly 13–16 % less time completing the tasks compared with those without AI. Put simply, a single knowledge worker with AI performed as well as two colleagues working together and finished more quickly.

The experiment also found that AI breaks down functional silos. Without AI, R&D professionals proposed mostly technical solutions while commercial specialists focused on market‑oriented ideas. With AI, those differences disappeared; the distribution of ideas shifted from a bimodal distribution (0.564 coefficient) to a unified one (0.482). Moreover, participants reported higher excitement and reduced anxiety when using AI, experiencing emotional benefits akin to working with a human teammate.

P&G’s blog summarised the outcomes succinctly: AI‑enabled teams were about 12 % faster, and AI’s language interface improved employee moraleP&G Case Study: us.pg.com. The combination of human collaboration and AI produced the best results, demonstrating that the future isn’t about choosing between people and machines—it’s about amplifying both.

Chart 1 – AI’s impact on collaboration

Chart 1: Harvard & P&G field experiment results. Generative AI enables a single employee to match or exceed two‑person teams while saving time. Source: Dell’Acqua et al. (2025)d3.harvard.edu.

Beyond P&G: Additional Evidence for Elevated Collaborative Intelligence

Customer‑Service Study: 15 % Productivity Gain

Harvard’s field experiment is not an outlier. Erik Brynjolfsson, Danielle Li and Lindsey Raymond conducted a study on a generative AI conversational assistant using data from 5,172 customer‑support agentsSource: arxiv.org. They found that access to AI assistance increased worker productivity by an average of 15 %. Less experienced agents improved both the speed and quality of their output, while the most experienced agents saw modest gains in speed and slight declines in quality. AI also facilitated worker learning and improved English fluency. Just as the P&G study showed, generative AI proved to be a true teammate—supporting novices, accelerating learning and improving the work experience.

Wendy’s FreshAI: Fast Food Meets Fast Intelligence

Wendy’s deployment of Fresh AI at drive‑thru windows illustrates how cross‑functional collaboration and AI create enterprise value. In Columbus, Ohio, the fast‑food chain piloted a conversational AI platform that achieved an 86 % autonomous order rate, far beyond industry normscdotimes.com. According to CEO Kirk Tanner, the company saw improvements in order accuracy and efficiency because employees could focus on speed of service and delivering an accurate ordercdotimes.com. Wendy’s is scaling FreshAI to more than 500 restaurantscustomerexperiencedive.com, demonstrating that when AI is designed as a cross‑functional experiment—co‑created by restaurant operators, field managers and technologists—it enhances both the employee and customer experiencecdotimes.com.

Market Momentum: The Generative AI Boom

The business case for adopting AI teammates is bolstered by explosive market growth. Research firm Mordor Intelligence estimates that the generative AI market will soar from USD 21.1 billion in 2025 to USD 97.8 billion by 2030, a compound annual growth rate (CAGR) of 35.9 %Source: mordorintelligence.com. Software today captures about 64 % of the market, but services are projected to expand at a 44.7 % CAGR through 2030. Enterprise adoption is accelerating because 20–40 % of workers already use AI tools in their daily workflows, and early adopters report noticeable reductions in cycle time and error rates. The market projections underscore the urgency: organisations that fail to embrace Elevated Collaborative Intelligence risk being outpaced by more agile competitors.

Chart 2 – Generative AI market growth

Chart 2: The generative‑AI market is forecast to grow from USD 21.1 billion in 2025 to USD 97.8 billion by 2030, a 35.9 % CAGR Source: mordorintelligence.com. Software currently dominates but services and edge solutions are growing rapidly.

Elevated Collaborative Intelligence™: The Formula in Action

The Harvard study validates the core premise of our book: HI + AI = ECI™. Human Intelligence (HI)—creativity, empathy, domain expertise—remains essential. Artificial Intelligence (AI) enhances scale, speed and analytical depth. When combined intentionally, they produce Elevated Collaborative Intelligence (ECI)—a level of performance unattainable by either on its own. In book I explore in great depth what the leading indicators y industry and department with actionable advice to minimize risk and optimize AI for your organization.

Chart 3 – Visualising HI + AI = ECI™

Chart 3: ECI emerges at the intersection of human insight and machine intelligence. The research demonstrates that AI can replicate key benefits of teamwork and democratize expertise (preview chapter 2 HI + AI = ECI by Carsten Krause) while humans provide judgement, creativity and ethical grounding.

https://a.co/d/jlY7MsQ

The Roadmap: From Pilots to Enterprise Value

Achieving ECI requires more than technology adoption. Our CDO TIMES feature “From AI Pilots to Enterprise Value: A CDO’s Playbook” explains why 80 % of AI pilots die on the vine and how to avoid the graveyard of abandoned proofs‑of‑conceptcdotimes.com. The key is to architect AI solutions around measurable business outcomes, ensure cross‑functional ownership from day one, and treat pilots as stepping stones toward operational scalecdotimes.com. Wendy’s FreshAI pilot is a prime example: its success derived from collaborative design and integrated operationscdotimes.com.

Why Executives Should Care

For HR Leaders: The research dispels fears that AI dehumanizes work. Employees using AI reported greater excitement and less anxiety Source: d3.harvard.edu. AI can be a motivational tool that engages employees by reducing drudgery and providing instant feedback. HR leaders like Maya can reimagine training and performance management around AI‑enhanced coaching and continuous learning.

For Technology Leaders: Breaking down silos isn’t just aspirational—it’s empirically validated. The P&G study shows that AI prompts professionals to produce balanced proposals irrespective of their functional background. Architects like Carlos should integrate AI into their digital‑transformation frameworks and shift from controlling standards to enabling dynamic capability buildingcdotimes.com.

For AI Product Leaders: The research warns against “AI‑first” hype. Without the human component, AI initiatives falter. As our AI‑first critique notes, calling yourself AI‑first because you use Copilot or ChatGPT at lunch is akin to calling yourself a bodybuilder because you once walked past a gymcdotimes.com. Product leaders like Aria should build cross‑functional pods where domain experts, technologists and designers co‑create AI features, ensuring ethical alignment and user trust.

The CDO TIMES Bottom Line

Harvard’s P&G study isn’t just another academic exercise. It demonstrates that generative AI can equal or surpass the benefits of teamwork, foster cross‑functional integration and elevate employee morale. Combined with real‑world success stories like Wendy’s FreshAI and productivity gains in customer‑service centres, the evidence is clear: AI is becoming a cybernetic teammate. But the technology only delivers outsized returns when paired with human leadership, structured collaboration and a roadmap for scaling.

Our HI + AI = ECI™ framework provides that roadmap. It’s not about being “AI‑first”; it’s about being human‑centric and AI‑enabled. Executives who harness Elevated Collaborative Intelligence will outpace competitors, inspire employees and deliver innovations that matter. Those who ignore it risk automating their irrelevance.

If this article resonates, dive deeper into our other ECI pieces, including “From Chaos to Clarity” cdotimes.com and “From AI Pilots to Enterprise Value”cdotimes.com, and pre‑order the book HI + AI = ECI™: Elevated Collaborative Intelligence. For leaders ready to build the future rather than chase it, the time is now to turn AI into your most trusted teammate.

Love this article? Embrace the full potential and become an esteemed full access member, experiencing the exhilaration of unlimited access to captivating articles, exclusive non-public content, empowering hands-on guides, and transformative training material. Unleash your true potential today!

Order the AI + HI = ECI book by Carsten Krause today! at cdotimes.com/book

Subscribe on LinkedIn: Digital Insider

Become a paid subscriber for unlimited access, exclusive content, no ads: CDO TIMES

Do You Need Help?

Consider bringing on a fractional CIO, CISO, CDO or CAIO from CDO TIMES Leadership as a Service. The expertise of CDO TIMES becomes indispensable for organizations striving to stay ahead in the digital transformation journey. Here are some compelling reasons to engage their experts:

1. Deep Expertise: CDO TIMES has a team of experts with deep expertise in the field of Cybersecurity, Digital, Data and AI and its integration into business processes. This knowledge ensures that your organization can leverage digital and AI in the most optimal and innovative ways.
2. Strategic Insight: Not only can the CDO TIMES team help develop a Digital & AI strategy, but they can also provide insights into how this strategy fits into your overall business model and objectives. They understand that every business is unique, and so should be its Digital & AI strategy.
3. Future-Proofing: With CDO TIMES, organizations can ensure they are future-proofed against rapid technological changes. Our experts stay abreast of the latest AI, Data and digital advancements and can guide your organization to adapt and evolve as the technology does.
4. Risk Management: Implementing a Digital & AI strategy is not without its risks. The CDO TIMES can help identify potential pitfalls and develop mitigation strategies, helping you avoid costly mistakes and ensuring a smooth transition with fractional CISO services.
5. Competitive Advantage: Finally, by hiring CDO TIMES experts, you are investing in a competitive advantage. Their expertise can help you speed up your innovation processes, bring products to market faster, and stay ahead of your competitors.

By employing the expertise of CDO TIMES, organizations can navigate the complexities of digital innovation with greater confidence and foresight, setting themselves up for success in the rapidly evolving digital economy. The future is digital, and with CDO TIMES, you’ll be well-equipped to lead in this new frontier.

Do you need help with your digital transformation initiatives? We provide fractional CAIO, CDO, CISO and CIO services, do a Preliminary ECI and Tech Navigator Assessment and we will help you drive results and deliver winning digital and AI strategies for you!

Subscribe now for free and never miss out on digital insights delivered right to your inbox!

Learn How to Enable Your Business to Stay Ahead in a World Where Websites Are Becoming Obsolete

By Carsten Krause
July 10, 2025

In an unprecedented move, Google’s own Andrew Yan left the tech giant’s search team to launch Athena, a startup betting on the imminent demise of the very behavior that has driven Google’s search engine for the past three decades. According to The Wall Street Journal, Yan’s departure signals a critical moment in the evolution of how humans access and interact with information online. Athena, along with other startups like Profound (backed by Kleiner Perkins), is positioning itself at the forefront of the zero-click internet—a post-website world where human information-seeking behavior evolves beyond typing URLs and clicking through search results.

The Extinction of a 30-Year-Old Habit

How often do you type in a URL nowadays? The once-predictable habit of directly navigating to websites has drastically diminished in favor of more instantaneous methods of retrieving information. Browsing through search links to find an answer is increasingly becoming a thing of the past. In fact, our brains are now rewiring to prioritize the convenience of AI-powered, real-time answers that satisfy our need for instant gratification.

This shift represents a tectonic change in human behavior that goes beyond incremental technological advancements. The real-time extinction of a 30-year-old behavior pattern could mark the beginning of the end for traditional search engines as we know them.

The Zero-Click Internet: A New Frontier

Companies like Athena and Profound are betting that this shift will disrupt everything from website traffic to the very core of SEO practices. With millions of dollars in venture capital backing their innovations, these startups are preparing for a future where the internet as we know it no longer centers around search results, but rather the immediate delivery of answers through AI.

Some key data points reveal the growing momentum behind this shift:

• Clerk, a company exploring AI search solutions, saw a 9% increase in sign-ups from AI-driven searches within just six months.
• Cyrus Shepard, a renowned SEO consultant, predicts that AI optimization will account for 50% of his work by the end of this year.
• SEO, the industry worth a whopping $90 billion, is now facing the threat of obsolescence. However, the SEO world is adapting, with professionals pivoting to AIO (AI Optimization), GEO (Generative Search Optimization), and AEO (AI Experience Optimization) strategies to stay relevant.

As AI-driven, frictionless search becomes the norm, brands must reimagine their digital strategies, not as a race for clicks, but as a seamless interaction between user and machine.

Case Study: Athena — Betting on the Future of Zero-Click Search

Athena, founded by former Google search engineer Andrew Yan, is positioning itself at the forefront of the zero-click internet revolution. The company’s mission is simple but profound: rethink how people access information and interact with the web by eliminating the need to click through search results. Instead of asking users to sift through pages of search results, Athena’s AI assistant provides direct, real-time answers tailored to each user’s needs.

How Athena is Transforming the Search Landscape:

• AI-Powered Personalization: Athena leverages large language models (LLMs) to create personalized search experiences. The assistant learns from user queries, providing more accurate, context-aware responses with every interaction.
• Seamless Integration: Athena’s AI integrates directly with apps and services, eliminating the need for users to open multiple tabs or navigate through websites. Whether it’s making purchases, booking reservations, or getting news updates, Athena delivers answers instantly within the app interface.
• Real-Time Answers: Unlike traditional search engines that rely on ranking algorithms and clicks, Athena’s system provides immediate, frictionless responses. The result? Users get the answers they need without needing to leave the platform or click through multiple links.

Impact:

• Increased Engagement: Early adopters using Athena’s AI-powered system have reported higher engagement rates. For example, e-commerce partners have seen an average 15% increase in completed purchases, as users are able to make informed decisions in real-time without navigating away from the platform.
• Improved Customer Retention: Athena’s AI-first approach to search has resulted in higher user satisfaction and improved customer retention. With answers delivered quickly and directly, users are more likely to return to services that provide this seamless experience.
• Investor Confidence: Backed by Y Combinator and other prominent investors, Athena is rapidly expanding. The company is becoming a significant player in the AI-driven search disruption, positioning itself as a serious contender to traditional search engines like Google.

What Athena Teaches Us About Zero-Click Search:

Athena’s success story provides a roadmap for companies that want to stay ahead in the zero-click future. By embracing AI as the central driver of search experiences, businesses can optimize for real-time, personalized answers that don’t require users to perform traditional search behaviors. Here’s what businesses can learn:

• Prioritize Personalization: Athena’s success is rooted in its ability to offer contextual, personalized answers. As businesses move toward AI-first models, personalizing user interactions will be key to engagement and loyalty.
• Integrate AI Seamlessly: Athena’s seamless integration into platforms shows that businesses must embed AI into their products and services in a way that users don’t notice the transition. The focus should be on making the experience intuitive and frictionless.
• Embrace the AI-First Future: Athena’s vision of a zero-click world is one where traditional search engines and website visits are no longer necessary. The brands that will thrive are those that embrace AI to create seamless, frictionless experiences, whether it’s in e-commerce, customer support, or content creation.

Case Study: Notion AI — A Personal Assistant for the Post-Website World

Another prominent example of AI-driven, clickless innovation is Notion AI, which has transformed its workspace platform into a productivity powerhouse by embedding large language models (LLMs) directly into the user experience. Notion AI assists users in writing, summarizing, organizing, and managing their work—all without requiring users to leave the platform or click through multiple external sources.

How Notion AI Enables Clickless Productivity:

• Instant Content Creation: Notion AI helps users generate text, organize notes, and even summarize long documents with a simple command. Users can instantly transform a rough draft into polished content or have the AI suggest improvements—all within Notion’s interface.
• Task Management and Collaboration: Notion AI assists teams in generating meeting agendas, tracking project updates, and brainstorming ideas, which makes collaborative work much more efficient and personalized. The AI adapts to the context of the user’s workflow, providing answers tailored to specific project goals and tasks.
• Integration Across Teams: Whether for personal use or team collaboration, Notion AI integrates seamlessly into its ecosystem, enabling clickless productivity. Users no longer need to browse the web or jump between tools—everything they need is in one place, powered by AI.

Impact:

• Productivity Boost: Businesses have reported significant productivity gains by integrating Notion AI into their operations. Users can generate and refine documents faster, allowing them to focus on more strategic, high-level work.
• Widespread Adoption: With over 20 million active users and widespread adoption in knowledge management and project collaboration, Notion AI has become an essential tool for businesses seeking a more seamless, frictionless workflow.
• Time Savings: Teams have saved hours of work per week by relying on Notion AI to handle content creation, summarization, and task management. This has led to faster decision-making and increased efficiency.

What Notion AI Teaches Us About Zero-Click Workflows:

• Personalization and Context Awareness: Notion AI’s ability to generate tailored content and insights based on previous interactions mirrors Athena’s approach. For businesses, this highlights the value of AI personalization—giving users the right tools at the right time without unnecessary steps.
• Seamless Integration: Notion AI is integrated deeply into its ecosystem, making it a natural extension of the user’s workflow. Businesses should strive for similar integrations in their own tools, ensuring that AI isn’t an add-on but a core feature that enhances everyday tasks.

HI + AI = ECI™: The Future of Elevated Collaborative Intelligence

As we move into a zero-click world, the intersection of human intelligence (HI) and artificial intelligence (AI) becomes paramount. In my framework of HI + AI = ECI™ (Elevated Collaborative Intelligence), this shift represents a powerful combination of human decision-making and AI’s ability to deliver instant, frictionless answers. ECI enables businesses to leverage both AI’s instantaneous responses and the human oversight and contextual understanding needed to make those answers actionable and trustworthy.

The clickless future driven by Athena and Notion AI exemplifies the power of AI in simplifying and enhancing workflows. However, for true Elevated Collaborative Intelligence, businesses must ensure that AI doesn’t replace human expertise but rather augments it. The synergy between human intelligence and AI will create a dynamic, real-time collaboration that will fundamentally transform business operations.

To learn more about how HI + AI = ECI™ can empower your organization, explore my book here.

Trust in a Clickless World

However, there’s a catch. Our brains, while deeply attracted to the efficiency of AI-powered answers, still crave the trust signals that come from seeing original sources. In a world where everything is delivered in an instant, humans still want the transparency and verification that comes from being able to trace information back to its source.

It’s the same psychology that makes us trust a restaurant more when we can see into the kitchen. We want to know how the information was sourced and whether it’s credible. This psychological demand for validation will slow the transition to a fully trust-based, AI-generated answer economy.

As one startup founder put it: “Your website doesn’t need to go away. But 90% of its human traffic will.”

This duality—instant answers vs. the need for verification—presents an opportunity for companies to evolve with the times while not fully abandoning traditional web practices.

The Future of Advertising: A New Frontier for Ads

The zero-click internet will also disrupt digital advertising. Search ads, as we know them, will have limited reach. The AI-powered models of the future will demand a more sophisticated approach to advertising, one that doesn’t overwhelm the user with irrelevant banners but instead integrates ads in a way that is just as seamless as the AI answers they are seeking.

The challenge will be in balancing monetization and user experience. Ads will need to be highly relevant and integrated into the AI experience. The one type of ad that users tend not to mind? Search ads that actually offer valuable, relevant information when users need it most.

The CDO TIMES Bottom Line: Is Your Brand Ready?

The rise of AI-powered, zero-click search is upon us. The challenge for businesses today is how to navigate this seismic shift while staying ahead of the curve. Traditional SEO is being eclipsed by the rise of AI search optimization, and the brands that will thrive in this new era will be those that embrace the change.

How far are we from a fully zero-click world? Not as far as you think. The clock is ticking, and the question remains: Are you optimizing for AI discovery today? Brands must adapt quickly to an AI-first reality or risk becoming invisible in a world where humans no longer need to click.

Next Steps for Executives:

• Audit your brand’s AI readiness. Is your website optimized for AI discovery?
• Explore AI search strategies. How will you provide trust signals to users in a zero-click world?
• Invest in AI-powered solutions. Start building your digital infrastructure with the future in mind.

For more insights into the future of AI search and staying ahead in a post-website world, visit CDO TIMES and get your copy of the HI + AI = ECI™ book today: Purchase here.

Sources:

• The Wall Street Journal (WSJ) – https://www.wsj.com/articles/google-engineer-leaves-to-create-startup-betting-on-its-search-demise-11652863610
• Athena (Y Combinator) – https://www.ycombinator.com/companies/athena
• Profound (Kleiner Perkins) – https://www.kleinerperkins.com/portfolio/profound
• Notion Labs – https://www.notion.so/blog/introducing-notion-ai

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