"Often, the real challenge is figuring out where to begin," Dan Lausted stated, leaning forward to emphasize his point. The Global Director of Cloud, AI, and Modern Work at Paragon Micro was cutting through the AI hype with characteristic precision during our recent CIO ThinkTank Series event on AI and Business Transformation.
"AI can tackle everything from anomaly detection to summarizing troves of documentation," he continued, "but if we don't define the business outcome first, we risk losing sight of real value."
His observation resonates with the Boston Consulting Group's findings that over 74% of companies face difficulties transitioning from pilot projects to fully scaled implementations, suggesting that organizations struggle with determining the right AI use cases. In a world where AI promises to transform virtually every aspect of business, this paradox of choice has paralyzed many executive teams.
For those interested in diving into the other articles in this CIO ThinkTank series, they have been listed here for your convenience:
Read on if you would like to learn more about getting the balance right between experimentation and strategic governance.
Lausted has observed numerous companies jump onto the AI bandwagon without a coherent strategy. The result? A proliferation of disconnected pilots that never reach their potential. A sobering insight from Gartner confirms this pattern, noting that at least 30% of AI projects never reach full production due to being abandoned because of poor data quality, inadequate risk controls, escalating costs, and unclear business value.
The alternative, according to Lausted, requires three critical elements:
According to IDC, 30% of enterprises cite lack of governance as the main barrier to scaling AI—a statistic that validates Lausted's insistence on early guardrails.
The democratization of AI tools has dramatically lowered barriers to entry. Today, a single developer can create a content-summarization service in hours, not weeks or months. This accessibility, while revolutionary, intensifies the risk of unregulated "shadow AI" proliferating throughout organizations.
"We see scenarios where a business unit starts using an AI chatbot, feeds it sensitive data, and accidentally breaks compliance," Lausted explained, describing a situation becoming alarmingly common.
"Creating enterprise-wide governance and bringing the AI conversation into leadership meetings are must-dos."
An IT executive in the audience nodded vigorously at this point, later sharing how their organization had discovered multiple departments using generative AI tools, most of which were being used without any security review or data management protocols.
When the conversation turned to measuring return on investment, Lausted's examples became notably concrete. IDC has found that while companies using generative AI are averaging a 3.7 times ROI, top leaders using generative AI are realizing significantly higher returns, with an average ROI of $10.3, a critical insight for IT executives evaluating AI's financial impact.
"We've had clients reduce invoice processing times from four days to a few hours," Lausted revealed. "That's not just faster workflow—it's improved vendor relationships and potential savings in late fees or missed early-payment discounts." What was clear is that while the immediate ROI for many of these investments returns value quickly, the way it unlocks new opportunity matters as much.
In discussions with IT executives, it was clear that these kinds of successes aren't isolated or rare. Another IT executive shared how they slashed customer service response times by 65% using AI to generate initial draft responses that agents could quickly review and personalize. The result? Higher customer satisfaction scores and a 22% reduction in service escalations, all while improving the richness of conversational intelligence, open the door for more improvements down the line.
Achieving effective ROI also requires the right approach to adoption. For many, this might start with the broader value found in Microsoft 365 Copilot deployments, but knowing what to do to drive successful adoption and maximize ROI can be a challenge. To help, we have a checklist for Copilot adoption that covers defining business problems, setting goals, checking feasibility, and picking which departments to start with, among other tasks.
Looking ahead, Lausted envisions AI evolving into what he calls an "AI Utility"—as integral to business operations as electricity is to the power grid. "It will become invisible, ubiquitous, and indispensable," he predicted. Organizations that build mature data infrastructure, governance frameworks, and talent today will be positioned to lead tomorrow.
He emphasized the collaborative power of forums like the CIO ThinkTank: "We're all still learning," he acknowledged. "The faster we share successes and guardrails, the sooner we can scale AI responsibly."
As CIOs, CISOs, CTOs, and IT executives exchanged information after the panel and roundtables, many were already scheduling follow-up discussions to share implementation strategies. The message was clear: turning nebulous AI potential into concrete business outcomes requires both strategic vision and disciplined execution, but it's easier if you don't do it alone.
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