From Data to Decisions: Why AI Strategy Is Failing Without Alignment
Written By: Greg Boone, CEO at Walk West
#RoadtoTechFest
North Carolina's technology sector has never lacked ambition, and on the subject of artificial intelligence, that ambition is outrunning execution at most organizations.
What I'm Seeing Across the State
Over the past 18 months, I've worked alongside leaders at companies of every scale across North Carolina, from Research Triangle enterprises to mid-market firms from Charlotte to Wilmington. The most consistent pattern I encounter is strategy and alignment problems masquerading as a technology problem.
Leaders have made the investment, cleared the budget, and deployed the tools, only to find themselves 12 months later unable to point to material business outcomes that justify the spend.
Gartner projects worldwide AI spending will total $2.52 trillion in 2026, a 44% increase year-over-year, yet McKinsey's State of AI research finds that only 6% of companies qualify as high performers where AI contributes meaningfully to EBIT, a gap that should stop every technology leader in this state cold before approving the next initiative.
The Data Foundation Is Broken and Everyone Knows It
Here's a hard truth that most technology executives understand but rarely name directly: the data infrastructure supporting most AI initiatives was not built for what those initiatives require.
AI systems demand clean, current, consistently governed data to produce reliable outputs, and the average enterprise is running on a combination of legacy systems, shadow databases, and governance practices that vary dramatically across departments.
Deloitte's 2026 State of AI in the Enterprise report, drawing on a survey of 3,235 senior leaders, found that only 20% of organizations are already achieving revenue growth from AI while 74% describe it as still an aspiration, with data readiness and organizational misalignment cited as the dominant barriers rather than model performance.
Gartner's prediction that organizations will abandon 60% of AI projects lacking AI-ready data is already proving out in 2026, with a separate Gartner survey of infrastructure and operations leaders published in April finding that 38% identified poor data quality or limited data availability as a direct cause of their AI project failures.
The Alignment Problem No One Wants to Own
The data issue is serious and solvable, but the alignment problem is both more damaging and more difficult, because it lives at the top of the organization where it is hardest to name.
A Pearl Meyer survey of more than 100 executives and board members published in April found that while 90% of boards believe AI responsibility clearly sits with the C-suite, executives themselves are deeply fragmented on ownership, with accountability spread across senior leaders, business units, and functional heads.
The result is a predictable pattern: competing technology roadmaps, redundant tooling purchases, pilots that succeed in isolation and fail to scale, and a workforce waiting for someone with actual authority to deliver a clear mandate.
Pearl Meyer Principal Brad Jayne put it plainly in Fortune: "AI just shines a light on something that was already there," a diagnosis that removes the technology from the center of the problem and places leadership squarely in its place.
The Pilot Purgatory Trap
North Carolina technology leaders attending recent NC Tech events will recognize the phrase "pilot purgatory," and Grant Thornton's 2026 AI Impact Survey confirms precisely why it persists: insufficient data readiness is a leading cause of AI underperformance, with 55% of CIOs and CTOs reporting that fewer than half their core applications are AI-ready, meaning the bottlenecks are organizational rather than algorithmic.
After years of being rooms full of serious executives leading serious organizations, the most telling moment is never what gets said from the podium but what gets admitted quietly afterward: the marketing infrastructure does not match the caliber of the work, the talent, or the challenge. Boards have stopped waiting for legacy leadership to close that gap on its own timeline, and across every major industry sector, the executives being handed the next chapter are the ones who arrived with AI fluency already built in, not bolted on.
IBM's Think Insights 2026 report found that only around 25% of AI initiatives deliver expected ROI and just 16% have scaled enterprise-wide, meaning the vast majority of AI investment is producing activity rather than outcomes, a scale of waste that reframes pilot purgatory from a strategic inconvenience into a material financial problem.
Getting out of pilot purgatory requires leadership alignment, a governed data foundation, and the human engine: the adoption infrastructure of training, incentive alignment, and psychological safety that gives AI tools something real to multiply once they reach the people doing the actual work.
What North Carolina Technology Leaders Should Do Right Now
The organizations across this state that will separate themselves over the next 24 months are not necessarily the ones with the most sophisticated AI models or the largest data science teams.
McKinsey's State of Organizations 2026 report, reaffirms a 2024 survey of more than 10,000 senior leaders, concluded that capturing value from AI depends as much on people as on technology investments, with one executive observing that for every $1 spent on technology, $5 should be spent on people. Some speculate that the cost could be higher with the rate in which technology continues to advance.
North Carolina has 700 member companies in NC Tech, more than 250,000 technology workers, and a research and university ecosystem that gives this state genuine competitive advantages in the AI era: the opportunity is not to lead on tool adoption, but to lead on the alignment and data readiness work that determines whether those tools deliver any return at all.
The tech is ready and North Carolina has every ingredient to lead in this era: the returns belong to the organizations doing the harder work of alignment, data governance, and human adoption before reaching for the next tool, and those organizations are in this state right now.
Greg Boone is a tech-savvy CEO and innovation strategist helping organizations turn AI into real business value. As CEO of Walk West, he leads AI adoption and growth management for brands seeking to scale smarter and faster. He previously co-led Blue Acorn iCi through a $125M acquisition by Infosys and served as CEO of Cleartelligence. Greg blends deep technical expertise with strategic clarity, advising Fortune 500s through startups on how to lead through disruption, implement AI effectively, and build future-ready cultures. He’s a sought-after speaker known for translating complexity into momentum for a variety of topics, including new approaches to GenAI, technology, and digital innovation.
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