Almost every company has a board or executive AI mandate. Vendors are rolling out agentic AI platforms. The pressure to move is intense.
But the reality on the ground looks different. Eighty-three percent of organizations say data quality is their top AI challenge, and 74% struggle to demonstrate ROI, according to Lopez Research. And only 21% report having a mature governance model for AI agents, per Deloitte’s 2026 State of Enterprise AI report.
“Agentic AI is real, and vendors’ offerings are very real, too,” says 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, frames it as a race with three arrows moving forward — one representing AI and tech evolution, one representing organizations and people, and one representing data. “The AI arrow is far out ahead,” he says. “That delta is the readiness gap.”
The gap isn’t the technology. It’s the foundational work most organizations haven’t done: data readiness, operating models, governance, skills, and culture. The companies making progress aren’t waiting for vendors to solve these problems. They’re tackling the unglamorous work themselves.
AI readiness can be framed across six levels — from data foundation at the base to reinvented business experiences at the top, says Afshean Talasaz, former CIO at Colonial Pipeline and now an executive advisor. One of the key areas that doesn’t always get the attention it needs is the operating model.
“The technology playbooks of the past don’t work in the AI world,” Talasaz says. “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.”
That demands a different kind of partnership between IT and the business. AI systems learn from data — records and measurements of what’s actually happening in the business — and then operate within business processes. Unlike traditional software, which is built based on user requirements, AI is sandwiched between the business that produces the data and the business that consumes the outputs.
“AI is requiring IT and business teams to work more closely together, to be clearer about what AI will and will not do — that really close partnership is crucial,” Talasaz says. “It’s not something that will always naturally evolve. It requires a lot of intentionality about how teams need to work together to deliver outcomes.”
The AI questions CIOs must answer aren’t just technical. Do we have the right operating model? Have we balanced governance and standard operating procedures within the model? Have we organized teams appropriately? All this must be designed within the context of what the business actually needs.
Too many organizations are bolting AI onto existing processes without redefining roles or workflows, Forrester’s Evelson. “Organizations can either incrementally enhance existing workflows by augmenting capabilities with AI or pursue a more transformative approach by redesigning the process end-to-end.”
The companies getting value are doing the latter.
Data readiness remains the most common barrier to scaling AI. “We’ve never fixed this data quality problem in most organizations,” says 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, the foundational work came first. “If you think about the Levi’s business, it’s quite complex — 100 countries, over 3,000 stores, multiple business models,” says Jason Gowans, the company’s chief digital and technology officer. “You can imagine the complexity of gathering all that data to understand how the business is performing. The idea of this single source of truth — that’s been the biggest thing.”
Levi’s now has more than 1,100 standard operating procedures that govern how work gets done on top of SAP. “That’s fertile material to feed to LLMs on how work gets done,” Gowans says.The results are tangible: partner onboarding that once took three to six months to set up EDI exchanges now takes days.
At contract manufacturing company Jabil, Chase Christensen, segment CIO, 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 notes. Most organizations’ data isn’t AI-ready; it hasn’t been prepared for how AI systems consume and learn from information. “Data is siloed, poorly governed, and hard to discover, integrate, and trust,” he says.
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 evolve. The firm recommends a balanced approach: roughly 48% of AI spending on foundations such as data management and engineering, and 52% on consumption, including analytics, governance, and applications.
But even as organizations work to prepare existing data, AI is creating new challenges. Users leveraging AI tools are generating new forms of data and information that never make it into corporate databases, West Monroe’s Hilborn notes.
“There are explosions of new data, content, and insights being created on the periphery of these data lakes,” he says. “The challenge is how do you capture that and leverage it.”
Even when data is in order, many AI initiatives stall due to how they’re 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,” Forrester’s Evelson says. “IT-led initiatives often become siloed or tool-centric, whereas business sponsorship ensures alignment to enterprise strategy, prioritization of end-to-end use cases, and a focus on decisions and actions rather than insights alone.”
Too often, AI is still treated as a series of disconnected use cases rather than a sustained, multi-year investment. Evelson calls this 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.
Leadership readiness is a distinct layer of AI preparedness, Talasaz says. “Are leaders prepared to provide a vision of reinvented business experiences that become the north star?” he asks. “Leadership teams, at various levels of the organization, need to articulate what a reinvented business looks like so teams have the direction and support to build differentiating capabilities.”
Levi’s offers a counterexample. AI is a CEO priority there. At the last quarterly offsite, the execs were building agents. “When you’re committed to upskilling the workforce, you’re better served to answer how to rewire processes with AI at the core,” Gowans says. “It starts at the top. It has to be an exec priority.”
Technical talent is only part of the equation. Organizations also need to address change management. “We saw it with the AI boom — fear about jobs, not knowing what AI did,” says Jabil’s Christensen. “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. That’s been the biggest hurdle.”
Different types of AI require different skills and governance, Talasaz says. “General use focuses on productivity on the desktop,” he says. “Integrated AI — industrial-capable AI embedded within core business processes — requires different skills, capabilities, and governance.”
For desktop AI, training and guardrails help employees be successful — what Talasaz calls “bumpers,” like in bowling. Organizations need to help employees through reskilling and guidance. “You have tools in a toolbox,” he says. “It’s important to know when to use a power tool versus when you need a screwdriver.” But for integrated AI embedded in core processes, the stakes are higher. “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,” Talasaz says.
That distinction matters for governance, too. Lower-, medium-, and high-risk AI use cases may require different ways of working and different risk management approaches. “Deploying AI in potentially high-risk or high-cost areas of the business requires a higher level of rigor,” Talasaz says. “That’s different than building something that helps write my emails.”
Perhaps the biggest readiness gap is the transition from proof of concept to production. “It requires such a different approach,” Talasaz says. “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.”
The operating model that works for experimentation doesn’t work for production at scale. Proofs of concept are designed to demonstrate the efficacy of ideas and the underlying technology. But building, scaling, and sustaining technology in the business requires operating models, standards, roles, and skills that many organizations haven’t developed. Intentionally designed operating models reduce the cost of learning, improve execution, and increase delivery velocity, says Talasaz.
But there’s no one-size-fits-all answer. “A business that needs to build capabilities in a marketplace moving very fast requires one kind of operating model,” Talasaz says. “A business that can take longer to develop business capabilities and adapt to market changes can choose a different operating model. It’s important to design ways of working tailored to what the business needs and the speed at which the business needs to leverage technology to be successful.”
Jabil is navigating this journey as part of its move to SAP’s cloud ERP through RISE, scaling from $29 billion to $34 billion in revenue while keeping selling, general, and administrative (SG&A) expenses relatively flat — in part by layering generative AI onto predictive analytics capabilities built over years.
“We started years ago with computer vision to drive product quality,” Christensen says. “As gen AI blew up, we took the predictive analytics we had built over the years and imbued them with gen AI. We’ve implemented the basics, and now we’re looking for complex scenarios.”
Governance is often treated as a policy document or committee. It should be embedded in the operating model itself, Talasaz argues.
“The operating model doesn’t always get the attention it needs,” he says. “Policies and committees are useful, but they should handle larger enterprise risks. Most of the governance should be embedded in the operating model to ensure you’re getting outcomes you want.”
That might mean peer review built into the development process, bias checks before deployment, or clear escalation paths for high-risk use cases. When governance is separate from the operating model, it tends to slow things down. When it’s integrated, it becomes how work naturally gets done, says Talasaz.
Governance at the agent level matters, too, Levi’s Gowans says. “Know what agents have been deployed, who authored them, and who’s responsible,” he says, noting that the company has established a registry to understand what agents it has operating within its networks.
The challenges of AI governance are unique, Lopez of Lopez Research says. “Very few people have the governance stack required to say they did the right things with AI,” she says. “Non-human identity and access control is totally different and, frankly, evolving so quickly that no one knows what to do.”
The challenge is ultimately a trade-off, Forrester’s Evelson says. “Push agentic AI capabilities too far, and you risk creating a governance and compliance nightmare,” he says. “Tighten controls too aggressively, and you stifle innovation. Best practices for striking the right balance are still being discovered.”
The AI readiness gap isn’t about technology — it’s about the work organizations have been deferring for years. Data quality. Operating models. Executive sponsorship. Skills and culture. Governance embedded in process.
“Once you progress from everyone using Copilot to putting agents in production, then you realize the need for business context,” Gowans of Levi Strauss says.
It’s a shared journey requiring all teams to understand what’s required, Talasaz says. “It involves helping people understand what it takes from all sides — the technology itself, the operating model, the skills and talents needed — but also working with business leaders on the art of the possible,” he says. “Helping them understand both the benefits and the responsibility of deploying this tech.”
A colleague of his calls AI “the ultimate executive team sport.”
“It requires people to do it well and manage it,” Talasaz says.