No more AI silos: The CIO integration playbook – TechTarget
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AI, more specifically generative AI (Gen AI), has increasingly become a standard element inside organizations of all sizes.
As is often the case with new technology, the rollout and integration of Gen AI has been fragmented. Different groups within organizations have adopted different tools that are used across a diverse number of applications.
While individual units and teams might recognize benefits, having fragmented AI deployments that are not unified across the organization creates AI silos. For CIOs and their organizations, these silos create duplication with multiple efforts across business units that are identical. There are also compliance risks, as unmanaged efforts might not meet organizational requirements. AI silos can also lead to poor data quality and missed opportunities for value.
Chief information officers (CIOs) have many responsibilities, including setting IT policy and defining the strategy for technology and its operation across an entire organization.
AI integration is a CIO priority for a variety of reasons.
Todd Loiselle, chief information officer at National Food Group, explained that AI is another tool for his organization, helping to increase revenue, reduce costs and make employees more effective.
“Everyone has parts of their day spent on repetitive, low-value tasks, and AI lets us handle those faster so our people can focus on higher-impact work,” Loiselle said.
For Chris Campbell, CIO at DeVry University, AI integration is a priority, as isolated pilots don’t deliver the needed value.
“Integration is critical because siloed experiments may generate pockets of insight, but they rarely deliver durable value,” Campbell said. “We want AI to accelerate outcomes across the institution, not just in a single function.”
Different CIOs tend to have various approaches to integration, but in general, the following key pillars are foundational elements of any AI integration playbook.
It’s critical to align AI to business goals, not isolated experiments. To do this, leaders should do the following:
Data is critical for any AI integration effort. Be sure to build a unified data fabric with clear governance by doing the following:
For integration to be effective, take a platform approach by standardizing tools and APIs across the organization.
Silos do exist when business units don’t collaborate. It’s essential to break down silos between IT, data science and business units by doing the following:
Integration is also about changing from an existing paradigm to something new. That’s why change management is critical to upskilling teams, managing cultural resistance and communicating value.
Embed risk management, transparency and ethical frameworks by doing the following:
The following examples show how leading CIOs have applied integration principles to break down AI silos and drive enterprise-wide value.
AI usage is already present in many organizations, often as a form of shadow AI, which is the use of AI tools or services without formal approval from the company or IT department. That was the case for
Conal Gallagher, CIO and chief information security officer at Flexera. He explained that his organization began its AI implementation plan by first examining the shadow AI tools that Flexera employees were already using. By polling staff to understand which AI tools were helping them with daily tasks, the company gained valuable insight into how teams were creatively and organically integrating AI into their workflows. That effort helped to highlight early use cases and opportunities to support what was already working well.
Gallagher noted that consolidating siloed efforts starts with acknowledging that AI will show up in pockets across the business, whether or not leadership has a strategy.
“Our first step was to bring informal AI use cases into the open by engaging employees,” Gallagher said. “From there, we could align tools to our broader ecosystem.”
Gallagher emphasized that for his organization, the ROI from AI isn’t just about direct savings, it’s about enabling teams to make smarter decisions, encouraging cross-team collaboration and eliminating waste.
“By identifying the needs of our organization, we were able to shift from reactive cost-cutting to proactive optimization, aligning with long-term business goals,” he said.
At DeVry, Campbell explained that while he saw employees experimenting with generative AI in pockets, real progress came once the organization set up a small AI enablement team and a governance model.
“By centralizing intake, we stopped duplicating work and started building reusable agentic AI patterns,” Campbell said.
He noted that the shift created faster deployment cycles, clearer accountability and measurable time savings in knowledge management and incident response.
“The role of the CIO is to make sure AI becomes a force multiplier across the enterprise, not another silo to manage,” Campbell said.
Elizabeth Hoemeke, CIO at digital payment network company, One Inc, has taken a bit of a different model, encouraging a bottom-up approach to AI integration, empowering teams to identify, experiment with and implement AI-driven tools and services.
Hoemeke said her company’s IT team established productivity benchmarks and now sees improvements monthly.
“Any AI capability we implement must include usage statistics and cost management, which are essential to driving adoption and responsible AI use,” she said. ” One Inc also recently established an AI Center of Excellence charged with cataloging all efforts company-wide to ensure minimal duplication of effort, sharing learnings and best practices, and establishing metrics to ensure the benefits of our investments in our AI program.”
AI success is no longer about experimentation; it’s about integration, scale and governance. CIOs are uniquely positioned to lead this transformation. Use this checklist to assess your current AI landscape, identify silos and implement an integration strategy.
Action item
Key metric or target
Audit all AI initiatives across business units
Map 100% of current AI tools and spending
Create an AI steering committee with C-suite participation
Secure executive sponsorship and decision authority
Consolidate redundant AI tools and vendors
Reduce tool sprawl by a measurable amount
Establish AI Center of Excellence
Create shared services and standards
Define integration of KPIs and success metrics
Target a specific ROI and time frame within 18 months
Launch an AI literacy training program
Achieve a high percentage of employee AI adoption
Create AI ethics and risk policies
Ensure compliance and responsible AI use
“Looking ahead, I believe the real differentiator won’t be who uses AI, it’ll be who integrates it seamlessly into everyday workflows, securely and responsibly,” said Loiselle.
Sean Michael Kerner is an IT consultant, technology enthusiast and tinkerer. He has pulled Token Ring, configured NetWare and been known to compile his own Linux kernel. He consults with industry and media organizations on technology issues.
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