Eighty-three percent of organizations say data quality is their top AI challenge, and 74% struggle to demonstrate ROI. Only 21% report having a mature governance model for AI agents. The pressure from boards and vendors to deploy agentic AI is intense, but most enterprises lack the operational foundations to succeed at scale.
"Agentic AI is real, and vendors' offerings are very real, too," said Boris Evelson, vice president and principal analyst at Forrester. "However, most enterprises are still not ready to adopt at scale."
Dave Hilborn, who leads West Monroe's Organization, People & Change practice, describes the situation as a race with three arrows - one for AI and tech evolution, one for organizations and people, and one for data. "The AI arrow is far out ahead," he said. "That delta is the readiness gap." The gap is not the technology itself. It is the foundational work most organizations have not done: data readiness, operating models, governance, skills, and culture.
AI needs clarity, not ambiguity
Afshean Talasaz, former CIO at Colonial Pipeline and now an executive advisor, frames AI readiness across six levels, from data foundation to reinvented business experiences. One area that consistently gets overlooked is the operating model. "The technology playbooks of the past don't work in the AI world," Talasaz said. "Those areas were able to tolerate more ambiguity between business and tech teams. AI doesn't tolerate the same level of ambiguity. It needs clarity."
Unlike traditional software built from user requirements, AI systems learn from data - records of what is actually happening in the business - and then operate within business processes. That puts AI between the business that produces the data and the business that consumes the outputs. This demands a closer partnership between IT and business teams, one that requires intentional design rather than organic evolution.
Too many organizations bolt AI onto existing processes without redefining roles or workflows. Evelson said companies can either incrementally enhance workflows or redesign processes end-to-end. The ones getting value are doing the latter.
Data debt comes due
"We've never fixed this data quality problem in most organizations," said Maribel Lopez, founder and principal analyst at Lopez Research, "and it comes back to haunt a company in spades as they move to AI."
At Levi Strauss, chief digital and technology officer Jason Gowans tackled the data foundation first. The company operates across 100 countries with over 3,000 stores and multiple business models. Building a single source of truth was the priority. Levi's now has more than 1,100 standard operating procedures governing how work gets done on top of SAP. "That's fertile material to feed to LLMs on how work gets done," Gowans said. Partner onboarding that once took three to six months now takes days.
At contract manufacturer Jabil, segment CIO Chase Christensen took a similar path. "We had to get everyone to understand where the source data resides, put tech in place so consumption is easier, and drive ownership around data and decision rights - so 140,000 employees don't feel empowered to create their own data sources that fall out of line."
The data challenge goes beyond quality. Evelson noted that most organizations' data is not prepared for how AI systems consume and learn from information. "Data is siloed, poorly governed, and hard to discover, integrate, and trust," he said. Forrester research shows that 45% of data and analytics decision-makers were adopting vector databases in 2025, and 53% were adopting graph databases - investments that signal recognition of how much data architecture needs to change. The firm recommends roughly 48% of AI spending go toward foundations such as data management and engineering.
Even as organizations work to prepare existing data, AI creates new challenges. Hilborn pointed out that users with AI tools are generating new forms of data and insights that never make it into corporate databases. "There are explosions of new data, content, and insights being created on the periphery of these data lakes," he said. "The challenge is how do you capture that and use it."
Who sponsors the work matters
Even when data is in order, many AI initiatives stall because of how they are sponsored and funded. "Enterprise data, analytics, and AI programs succeed when business CxOs sponsor them because they are accountable for business outcomes, not just technology delivery," Evelson said. IT-led initiatives often become siloed or tool-centric, while business sponsorship keeps the focus on end-to-end use cases and decisions rather than insights alone.
Evelson also warned against the "use case trap" - organizations overindex on individual projects and miss the enterprise-wide compounding impact. That leads to fragmented priorities, inconsistent adoption, and difficulty demonstrating ROI. AI Learning Path for CIOs addresses these structural challenges, covering governance models, operating frameworks, and the strategic decisions that separate successful AI programs from stalled experiments.
Leadership readiness is its own layer of preparation, Talasaz said. Leaders need to articulate a vision of reinvented business experiences that becomes the north star. At Levi's, AI is a CEO priority. During the last quarterly offsite, executives were building agents. "It starts at the top," Gowans said. "It has to be an exec priority."
Skills, fear, and two types of AI
Technical talent is only part of the equation. "We saw it with the AI boom - fear about jobs, not knowing what AI did," Christensen said. "The key is demystifying AI. We doubled down and focused on AI literacy. We want everyone to understand how it was put together, and that removed a lot of that fear."
Talasaz draws a distinction between two categories of AI that require different skills and governance. General-use AI focuses on desktop productivity - training and guardrails, what he calls "bumpers" like in bowling, help employees succeed. Integrated AI, embedded within core business processes, carries higher stakes. "Business leaders responsible for business outcomes based on AI-driven processes need to be fully aware of both the benefits and risks that come along with using these tools," he said.
That distinction matters for governance. Lower-, medium-, and high-risk AI use cases may require different ways of working and different risk management approaches. Deploying AI in high-risk or high-cost areas requires a higher level of rigor than building something that helps write emails.
From proof of concept to production
Perhaps the biggest readiness gap is the transition from proof of concept to production. "A successful proof of concept can create a lot of excitement, but when teams are unprepared to build and scale, it can create the potential to over-promise and under-deliver," Talasaz said. The operating model that works for experimentation does not work for production at scale. Proofs of concept demonstrate efficacy. Building, scaling, and sustaining technology requires operating models, standards, roles, and skills that many organizations have not developed.
There is no one-size-fits-all answer. A business that needs to build capabilities in a fast-moving market requires one kind of operating model. A business that can take longer to adapt can choose a different one. Talasaz said intentionally designed operating models reduce the cost of learning, improve execution, and increase delivery velocity.
Governance, too, should be embedded in the operating model rather than treated as a separate policy document or committee. Peer review built into the development process, bias checks before deployment, clear escalation paths for high-risk use cases - when governance is integrated, it becomes how work naturally gets done rather than a bottleneck. At Levi's, Gowans said the company established a registry to track what agents have been deployed, who authored them, and who is responsible.
"Push agentic AI capabilities too far, and you risk creating a governance and compliance nightmare," Evelson said. "Tighten controls too aggressively, and you stifle innovation. Best practices for striking the right balance are still being discovered."
Why this matters for executives and strategy leaders
The AI readiness gap is not about technology. It is about the work organizations have been deferring for years - data quality, operating models, executive sponsorship, skills, culture, and governance embedded in process. The companies making progress are not waiting for vendors to solve these problems. They are doing the unglamorous foundational work themselves. For executives, the mandate is clear: define the north star, sponsor the work from the business side, and build the operating model that turns proofs of concept into production at scale. AI for Executives & Strategy covers the governance frameworks, organizational design principles, and leadership approaches that close the readiness gap before it derails AI investments.
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