AI and automation: Think strategy before identifying tech tools – Crain's Cleveland Business

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CEO at Box – The Content Cloud
The initial societal reaction to AI is often to think about what existing work could be replaced by AI. Yet, most conversations I have with enterprises are around what *new* things AI can do for that organization that they weren’t solving before. Yes, there’s certainly opportunity to automate some of the work that we currently do to drive efficiency, but the vast majority of work that we will bring automation to is the work that we just never got around to in the first place. This can be anything from areas of work where adding more humans to the problem just could never be financially justified, or where there’s a level of specialization necessary that most companies have access to. The classic mistake we make in evaluating any form of automation is looking at the size of the existing market and extrapolating the impact of a new technology on that same market. Economists would look at AI and say that there are a certain number of engineers in the economy, and if AI can do 50% of what they can, then the economy needs half the number of engineers. What these estimates don’t capture is what those engineers are spending their time on in practice, and if you added a new set of tools for them, what they would be delivering instead. This is a classic challenge, and most people making predictions about the labor force and AI are going to get this wrong over and over again. Where AI takes this fallacy much further is that there’s really no theoretical upper limit to the long-tail of use-cases that AI can solve. Prior forms of automation were either limited by narrow capabilities, or by significant physical build-out before any leverage can be generated. AI lacks both the narrowness of past forms of automation, and effectively only requires compute and power as its physical constraint. This means that companies can simply now attack the kinds of problems that just never were economically feasible to solve before. In marketing, this may mean bringing the level of design creativity to every asset that a company produces or customizing assets for every geography that would have been time prohibitive before. In legal, this could mean being able to analyze all contracts and their risks at a level of depth that never would have made sense before. In product and engineering, it would mean synthesizing insights on customer feedback that they never would have gotten around to. In media, it could mean improving the ability to match an advertiser with particular content or talent that would have otherwise been missed to generate more revenue. These are just a small number of the kind of examples enterprises are looking to solve with AI right now. Notably, in all of these cases, AI is not replacing existing work that’s being done, but adding new capabilities to the organization. This will be the classic pattern that we see, and it will produce the vast majority of opportunities in AI.
Freelancer – “Sales Development Professional | Turning Prospects into Partners” • Health & Life Insurance • Real Estate • Data Entry • Corporate Learning Development • Virtual Assistant • Revenue Cycle Management
This insightful perspective challenges the common narrative surrounding AI and automation, highlighting the transformative potential of AI to create new opportunities rather than simply replace existing tasks. By focusing on the innovative capabilities that AI can bring to organizations, such as enabling creativity, customization, and deeper analysis, this approach shifts the conversation from fear of job displacement to excitement about the possibilities for growth and efficiency. Moreover, it underscores the importance of recognizing the diverse range of problems that AI can address, from marketing and legal analysis to product development and media optimization. Rather than viewing AI as a threat to existing roles, this perspective emphasizes its role as a catalyst for innovation and value creation across various industries. Ultimately, this shift in mindset encourages organizations to embrace AI as a tool for augmenting human capabilities and unlocking new opportunities, leading to enhanced productivity, competitiveness, and success in the evolving digital landscape.
Director – Data Solutions | Data Strategy | AI/ML | Product Management | Ideation | Leadership
Aaron Levie The clash in this debate between economists and AI advocates is that both parties tend to use examples that favour one side and prove why the other side is not on the same page. Going with the conclusion being made here, the inference is AI can be deployed for use cases that were Not economically feasible or time prohibitive. Curious to know What would be the take of AI advocates on deploying AI for use cases that are both economically feasible and not time prohibitive?
Head of Software Engineering at MAB
Exactly this. The real danger to AI is the recent gimmicky demonstrations that seek to leverage too early for things that are more likely to cause a sudden backlash. This recall feature…..geez Louise…..who thought this was a good idea. To be fair similar things in other recent announcements recently AI shopping assistant advising you to not go hiking in wet weather with shoes designed for beaches. AI won’t be needed in shoe shops. Got to hope the inevitable Apple WWDC OpenAI demo next week does not go down a similar avenue. Because as an assistive technology even with todays capabilities if you are creative it has massive potential. Just don’t try and open a beer bottle with a marshmallow….actually never tried it, but suspect it won’t get investment on Shark Tank!
Experience Senior Financial Planning, Analysis and Reporting SME seeking P/T or F/T job.
This ties nicely to this Blog – AI, Caramba – Productivity and Job Impacts – https://www.linkedin.com/pulse/ai-caramba-productivity-job-impacts-paul-young-jblec/
Multifamily Leasing & Marketing – Visit: Intentionalmktg.com 🌐 | Setting up tech X teams to get your occupancy up🚀 |
And watching to see how this information overload brought by the enormous scale of possibilities will play in our society’s mental well-being.
Operator & Advisor | Early Uber
I guess by replacing existing work with AI we free up humans to do the things they normally would never get to.

The potential of AI goes beyond replacing existing work. It’s about unlocking new capabilities and solving previously unsolvable problems. Aaron Levie
Veteran enterprise saas sales 5+ years (HrTech/AI/Analytics) | Published Author
Enterprises are shifting focus to what new capabilities AI can bring. Automated efficiency is key, but groundbreaking potential lies in uncharted territories and specialized tasks. The impact of AI transcends traditional limitations, offering unmatched problem-solving abilities across various industries. Aaron Levie
Top E-commerce Voice 🛍️ | Helping hundreds of brands bridge offline & online commerce @ Brij 🔁 | Mama x2 👶🏼| Omnichannel Queen 👑 | Omnichannel Marketer Podcast Host 🎙️ | CEO & Co-Founder @ Brij 🌉 | Angel Investor
It’s exciting to see how AI can unlock new possibilities rather than just replacing existing work.

Nice perspective – there’s really no theoretical upper limit to the long-tail of use-cases that AI can solve.
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Enterprise Account Executive at Box
Really great post from our CEO, and it reflects what we’re seeing in the field. When I talk to customers, especially those in IT, the things they are most excited about having AI do are things that were deemed too burdensome to do manually: applying metadata to years of old documents that didn’t have it from the start, making scans of old paper documentation searchable/indexable using AI, and finding insights across swaths of unstructured data.
CEO at Box – The Content Cloud
The initial societal reaction to AI is often to think about what existing work could be replaced by AI. Yet, most conversations I have with enterprises are around what *new* things AI can do for that organization that they weren’t solving before. Yes, there’s certainly opportunity to automate some of the work that we currently do to drive efficiency, but the vast majority of work that we will bring automation to is the work that we just never got around to in the first place. This can be anything from areas of work where adding more humans to the problem just could never be financially justified, or where there’s a level of specialization necessary that most companies have access to. The classic mistake we make in evaluating any form of automation is looking at the size of the existing market and extrapolating the impact of a new technology on that same market. Economists would look at AI and say that there are a certain number of engineers in the economy, and if AI can do 50% of what they can, then the economy needs half the number of engineers. What these estimates don’t capture is what those engineers are spending their time on in practice, and if you added a new set of tools for them, what they would be delivering instead. This is a classic challenge, and most people making predictions about the labor force and AI are going to get this wrong over and over again. Where AI takes this fallacy much further is that there’s really no theoretical upper limit to the long-tail of use-cases that AI can solve. Prior forms of automation were either limited by narrow capabilities, or by significant physical build-out before any leverage can be generated. AI lacks both the narrowness of past forms of automation, and effectively only requires compute and power as its physical constraint. This means that companies can simply now attack the kinds of problems that just never were economically feasible to solve before. In marketing, this may mean bringing the level of design creativity to every asset that a company produces or customizing assets for every geography that would have been time prohibitive before. In legal, this could mean being able to analyze all contracts and their risks at a level of depth that never would have made sense before. In product and engineering, it would mean synthesizing insights on customer feedback that they never would have gotten around to. In media, it could mean improving the ability to match an advertiser with particular content or talent that would have otherwise been missed to generate more revenue. These are just a small number of the kind of examples enterprises are looking to solve with AI right now. Notably, in all of these cases, AI is not replacing existing work that’s being done, but adding new capabilities to the organization. This will be the classic pattern that we see, and it will produce the vast majority of opportunities in AI.
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I help Regional QLD SMEs AUTOMATE Phone Calls using AI. Entrepreneur | Content Creator | Innovator
🚀 “The Future is Here and It’s powered by GenAI!” 🚀 Did you know Generative AI is making waves across enterprises, transforming industries just like the internet did? 📈 Imagine your business operating at unprecedented efficiency, with AI-driven workflows, personalized customer interactions, and predictive analytics guiding your every move. Here’s the kicker: Enterprises leveraging GenAI are seeing not just incremental improvements, but transformative advancements.🔍 Want to transition from theory to practice? Dive into Crain’s article on implementing GenAI at scale and uncover how you can revolutionize your business. 🌐 🎥 Check out the full story here: [💡 Read More](https://lnkd.in/g6XWVTeF) Ready to take the leap and transform your enterprise with GenAI? Share your thoughts or questions below! 💬 — Need help with AI solutions? Contact A-Tech AI, your automation partner. Let’s make your AI dreams a reality. 🚀 #AI #GenerativeAI #BusinessTransformation #TechInsights
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Virality that converts for founders/execs | Co-founder @ R8A
“Will AI replace you?” remains the $100M question. It’s the wrong question though. It’s asked based on a misdiagnosis of the problem. This is particularly for the AI skeptics. The skeptics fall in two camps: (1) Want AI to replace their work. Those who expect any new technology to be at 100% capability from the get-go, but see tools which aren’t there yet. “AI unfortunately can’t — and never can — do the work”. (2) Doesn’t want AI to replace their work. Those who think if AI is ever capable of doing their current tasks, they will be out of a job. The common concern? Both just want smarter, more efficient work environments. Their misdiagnosed problem boils down to: (1) Tech isn’t designed to (entirely) discard humans. It’s designed to empower humans. Technology = leverage. (2) Whether you yearn for “an AI takeover” or are scared of it, you’re commoditizing your work = you’re easily replaceable. The answer is adaptability. Zoom out. See the big picture. Where is the future of work? Spoiler: AI is a core component of any job. Which means: AI *will* increasingly replace your current tasks. But note: “replace your current tasks” ≠ “replace you” BIG difference. HUGE difference. Let’s zoom out to explain this. All technology is, is a lever. It’s a tool to give humans leverage. As Archimedes put it “Give me a lever long enough and a fulcrum on which to place it, and I shall move the world”. → The wheel was a lever to transport more effectively. → The plow was a lever to cultivate crops more effectively. → The printing press was a lever to disseminate information on a mass scale. → The internal combustion engine was a lever to transport even more effectively. → The personal computer was a lever to democratize information and computation. All levers to increase humans’ productivity. All refined countless times. All technology. Same thing goes for AI. Used in automations: → it’s the lever to enable the reallocation of your human creative input to where it’s needed — rather than *wasting* it on tedious but necessary tasks. Used as an assistant: → it’s your “god-like knowledge” assistant who won’t complain about a thing, is available 24/7 and with its infinite capabilities merely relies on the quality of your instructions to perform. So for most of you — you’re looking to use it as an assistant. Use AI to work in tandem with human brilliance to augment your operation. As for automations, you can leave that to those specializing in that — like we do at R8A. Do that — and that’s where real progress exists for your business. Don’t get subscribed to the inflated narrative. The reality is AI is replacing our tasks, which means eliminating certain jobs — BUT while *creating* new jobs; new jobs where human creativity is actually needed. ↓ ↓ ↓ ♻ Repost to keep your network up to speed with all things AI #ai #generativeai #artificialintelligence #futureofwork #innovation #technology
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CCO Spot Ship | Top 100 Inspirational Females of Ocean & Energy | Maritime Commentator – digitalisation, decarbonisation, commodities
Developing an #AI based solution is challenging, to say the least. For those businesses relying on AI for their commercial strategy, it can be a crippling element if done wrong. There must be in fact a reason why only 11% of companies have adopted (generative) AI at scale. Here are McKinsey & Company’s 7 potential pitfalls to bear in mind, a refreshingly realistic account of what needs to be done to get things right – which our experience in developing Spot Ship can humbly confirm: 1. Eliminate noise, focus on the signals (avoid the rabbit hole of constant experimentation, focus on successful pilots and real problems)  2. It’s about how the pieces fit together, not about the pieces themselves (don’t evaluate individual components of the AI engine- if it’s working well together, it’s working)  3. Get a handle on developmental costs (very important- the actual model will probably only cost ~15% of the total) 4. Don’t attempt a scaled rollout (focus on specific wings of the business that can benefit most- when it comes to adoption and implementation) 5. Gather a team that can create value out of AI, not just models. A cross-functional, cross-sector expertise can help create a robust, well-rounded function 6. Go for the right data: know what category of data matters most, and start with a model that collates, interprets and presents that.  7. Finally- reuse code where possible. Say you want to measure another data point in a similar way. Using existing code can increase development speed by 30-50%, which can directly affect the amount of capital used. #shipsandshipping #generativeai #commercial
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Senior Enterprise Architect | Salesforce Transformation & Governance Expert | Driving Operational Efficiency & Growth through Scalable Solutions
Is Generative AI the New Gold Rush in the Tech Industry? Let’s dive in! Everywhere we look, the buzz around generative AI has been impossible to ignore. From streamlined content creation to highly personalized customer experiences, its potential seems boundless. But what does this mean for businesses and, more importantly, for their enterprise architecture? The transformative power of generative AI is undeniable. Imagine automating tedious tasks that once took hours, now completed in mere moments with precision. Picture CRM systems so intuitive that they not only manage your existing customer relationships but predict future opportunities with uncanny accuracy. Yet, as we navigate this new frontier, questions arise. How do we ensure data integrity and privacy when AI is generating content at lightning speed? What are the ethical considerations of AI-generated outputs? And perhaps most crucially, how can businesses leverage generative AI not just for efficiency, but to truly innovate and stay ahead in their market? As we ponder these questions, one thing is clear: the integration of generative AI into our systems and platforms isn’t just an upgrade; it’s a revolution. A revolution that demands thoughtful strategy, robust ethical frameworks, and an unwavering commitment to leveraging technology for the greater good. Like any gold rush, there will be winners and losers. The winners will be those who recognize not just the potential of generative AI but understand how to navigate its complexities to create genuinely innovative solutions. The journey into this brave new world of tech is exhilarating—and we’re just getting started. As we explore the vast potentialities of generative AI together, let us do so with both ambition and caution, ensuring that as we reach for the stars, we remain grounded in our values and principles. Let’s embrace this journey with open minds and a steadfast commitment to excellence. The possibilities are limitless.
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Building low-code AI automations & Conversational AI
𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗮𝗻𝗱 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Agentic AI stands out as a transformative force redefining the landscape of business operations, customer engagement, and decision-making processes. As we embrace the potential of machine learning, we are witnessing the emergence of advanced AI agents capable of performing complex tasks, learning from experience, and ultimately enhancing productivity and efficiency. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜? Agentic AI refers to artificial intelligence systems designed to operate autonomously, making decisions and taking actions on behalf of users. Unlike traditional AI, which typically requires human intervention, Agentic AI can analyze data, draw insights, and execute tasks independently. This paradigm shift not only optimizes workflows but also empowers businesses to focus on strategic initiatives rather than mundane operational tasks. 𝗧𝗵𝗲 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 At the heart of Agentic AI’s rise is machine learning – a subset of AI that enables systems to learn from data and improve over time without explicit programming. Modern machine learning algorithms can analyze vast datasets, identify patterns, and make predictions with remarkable accuracy. This capability allows AI agents to refine their performance continuously, becoming more effective in their roles as they gather insights from real-world interactions. 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 – 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Agentic AI can automate repetitive tasks such as data entry, scheduling, and customer inquiries. By streamlining these processes, businesses can significantly reduce operational costs and free up human resources for high-value activities. – 𝗗𝗮𝘁𝗮-𝗗𝗿𝗶𝘃𝗲𝗻 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: With the ability to analyze large volumes of data in real time, AI agents can provide actionable insights that inform decision-making. This leads to more informed strategies, optimized marketing campaigns, and ultimately better business outcomes. – 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: As businesses grow, so do their operational demands. Agentic AI can scale effortlessly, accommodating increasing workloads without compromising quality or performance. This scalability is essential for businesses aiming for rapid growth and expansion. – 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗘𝗱𝗴𝗲: By integrating advanced AI agents into their operations, businesses can stay at the forefront of innovation. This not only differentiates them in a crowded marketplace but also enables them to pivot quickly in response to market changes. Book an appointment and see how we can help your business by utilizing AI.
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The business world is at the forefront of a revolutionary shift, characterized by the rapid integration of artificial intelligence. #Flatiron Software recognizes this transformative era, where #AI is no longer just a tool for automation but a central element in strategic planning. As businesses adapt, our expertise ensures a smooth integration of AI, simplifying processes and enhancing #efficiency. Entrepreneur Media #AIIntegration #BusinessRevolution #FlatironSoftware https://shorturl.at/sMOQU
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Sales Leader | Driving Cloud Adoption, Automation, and Innovation | “Passionate About Building Collaborative Teams and Customer Success” – Vice President | Cloud Sales & Services at Citius Cloud Services LLP
ContexIQ is a pioneering AI product and research startup based in the US and India, specializing in the creation of enterprise-grade analytics and AI solutions. We focus on delivering highly customized, secure, and scalable analytics and AI solutions tailored to the specific needs of enterprises across various industries. Given the vast opportunities within the enterprise market alongside the significant challenges in harnessing these opportunities, our tailored AI solutions are designed to meet the specific demands of this evolving space. By addressing these challenges and leveraging these opportunities, we are positioned to drive substantial advancements in AI application and integration across industries.   ContextIQ is focused to build solutions and provide services in the following three key areas for various industries and partners. At contexIQ, we are dedicated to ensuring the highest standards of data security and governance. Our goal is to empower enterprises with the tools and insights they need to thrive in a data-driven world. As AI continues to revolutionize industries, contexIQ is ideally positioned to bridge the gap between advanced AI capabilities and enterprise needs, simplifying the path to transformative AI adoption. Given the vast opportunities within the enterprise market alongside the significant challenges in harnessing these opportunities, our tailored AI solutions are designed to meet the specific demands of this evolving space. By addressing these challenges and leveraging these opportunities, we are positioned to drive substantial advancements in AI application and integration across industries.  CitiusCloud Services LLP About | ContexIQ
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We’re kicking off tl;lc (too long; lowercased it) this month by killing the complexity around AI. AI becomes ‘ai’ – small, simple and tangible. Check out the first content to drop here, via Mi3Australia: https://lnkd.in/gub9Pp4R The tl;lc version? Here you go: Headline Hype: • AFR claims “AI could claim 30% of executive jobs in two years,” inciting fear. • Reality: AI, when simplified, reveals opportunities, not threats. Challenge 1: Time Management • Issue: CEOs often feel there aren’t enough hours in the day. • AI Solutions: Custom GPTs can be trained on company-specific data, serving as a decision-making partner for consistent company-wide decisions. Challenge 2: Data Overload & Decision Paralysis • Issue: CEOs want real-time customer data but are overwhelmed by the volume. • AI Solution: AI business intelligence tools like TBX can sift through vast data to find actionable insights. • These tools offer the depth of traditional one-on-one interviews but are faster, cheaper, and scalable. Challenge 3: Boosting Revenue While Lowering Costs • Issue: Balancing revenue growth with cost reduction remains a critical goal. • AI Solution: GenAI can significantly reduce costs, such as by building brand image libraries instead of relying on expensive stock subscriptions. • Seek AI partners with proven quality, transparent processes, and clear pricing models. Matt Morgan Carly Pelham #lowercasedit
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| Digital Marketing Professional | CRO & SEO Specialist | E-commerce Enthusiast | AI in Marketing Explorer
𝐓𝐡𝐞 𝐁𝐥𝐮𝐞𝐩𝐫𝐢𝐧𝐭 𝐟𝐨𝐫 𝐚 𝐒𝐮𝐜𝐜𝐞𝐬𝐬𝐟𝐮𝐥 𝐀𝐈 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲 🚀 In our journey towards digital transformation, the role of Artificial Intelligence (AI) has transitioned from a mere buzzword to a cornerstone of strategic innovation. As we delve into the essence of crafting a successful AI plan, it’s crucial to understand the interplay between strategy, feasibility, and real-world application. 🔍 𝐒𝐭𝐞𝐩 𝟏: 𝐅𝐞𝐚𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐖𝐨𝐫𝐤𝐬𝐡𝐨𝐩𝐬 & 𝐆𝐨𝐚𝐥 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 The foundation of any AI strategy begins with rigorous feasibility workshops. Here, AI strategists collaborate closely with stakeholders to ensure goals are not only achievable, but are well-aligned with the organization’s vision. It’s about setting the stage for AI to solve real problems, tailored to the specific needs of the business. 🎯 𝐒𝐭𝐞𝐩 𝟐: 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲𝐢𝐧𝐠 𝐀𝐈-𝐬𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬 As per insights from Chip Huyen’s “Designing Machine Learning Systems,” AI thrives in environments where it can learn from complex, evolving patterns, especially in repetitive tasks. The key is to design systems capable of generating predictions at scale, mindful of the investments required. Equally, weighing the benefits against the potential costs of incorrect predictions ensures the venture into AI is grounded in economic sense. 📦 𝐂𝐚𝐬𝐞 𝐒𝐭𝐮𝐝𝐲: 𝐎𝐚𝐤𝐌𝐢𝐧𝐠𝐥𝐞❜𝐬 𝐒𝐮𝐩𝐩𝐥𝐲 𝐂𝐡𝐚𝐢𝐧 Consider OakMingle, a furniture manufacturer navigating the complexities of its supply chain. By assessing factors like raw material availability, order demand, and production operations, we can pinpoint where AI can optimize efficiency and reduce shortfalls, thereby ensuring timely deliveries. 🔄 𝐀𝐈 𝐯𝐬. 𝐓𝐫𝐚𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐌𝐞𝐭𝐡𝐨𝐝𝐬 Before jumping on the AI bandwagon, it’s wise to evaluate traditional methods. ✔️ 𝐈𝐬 𝐀𝐈 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐅𝐢𝐭❓ For OakMingle, the introduction of AI to predict and manage supply chain shortfalls could revolutionize efficiency. The ability of AI to analyze and learn from data at scale means that planners are better equipped to anticipate and mitigate potential issues, making AI not just a fit but a strategic necessity. 🔗 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐭𝐨 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐌𝐚𝐩𝐩𝐢𝐧𝐠 The transition from a business problem to a technical solution hinges on the quality of labeled data. By involving subject-matter experts in the data labeling process, AI models become more adept at identifying potential shortfalls, thus enhancing the decision-making process. 💡  𝐄𝐜𝐨𝐧𝐨𝐦𝐢𝐜 𝐕𝐚𝐥𝐮𝐞 𝐀𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭 Finally, the decision to fund an AI project must be backed by a thorough economic value assessment. By streamlining operations and minimizing human errors, AI enables planners to focus on more complex issues, thereby delivering tangible ROI through improved efficiency and reduced operational costs.
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