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The real barriers to enterprise AI and how to overcome them
Feb. 26, 2026 · Authored by K.C. Fike
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Article
Feb. 26, 2026 · Authored by K.C. Fike
Every organization wants to harness artificial intelligence (AI). But wanting it and being ready for it, are two vastly different stages, and the gap between them is where most enterprise AI initiatives quietly stall. Organizations that get the basics right will be positioned to move fast and those that skip them will find themselves layering AI on top of a foundation that was never built to support it.
Before any meaningful AI initiatives can begin, organizations need to understand what data they have, where it lives and how clean it is. The challenge runs deeper than simple fragmentation. Over the past decade, individual lines of business have adopted their own tools and platforms, often without involving the central information technology (IT) team. The result is a patchwork of disconnected systems held together by manual Excel uploads and custom code that nobody fully owns. Many companies arrive at the start of an AI engagement without a clear picture of their own data landscape, let alone a strategy of what they want to build on top of it.
The solution starts with mapping the environment: which systems exist, how data flows between them and where the gaps are. Integration platforms play a central role here, providing the reliable, visible connectivity that transforms a fragile web of workarounds into a foundation that AI can actually be built upon. Without that foundation, even the most sophisticated AI initiative will struggle to deliver consistent results.
Scalability is one of those challenges organizations tend to underestimate, right up until the moment it becomes a crisis that compounds quietly. For instance, a customer visits a website during a promotion, finds inaccurate inventory information and then abandons the purchase. The lost sale does not register as a systems failure on any dashboard, but it is one. Multiply this across a high-traffic event like a seasonal surge and the business impact becomes real quickly.
Scalability is not a technical nicety but a revenue question. Organizations that choose automation and integration platforms based on current needs, without accounting for future growth, often get locked into systems that cannot keep pace where it matters most. Choosing the right platform architecture early is far less costly than trying to re-engineer it after the business has already scaled around it.
Historically, integration and automation tools were the exclusive domain of central IT. That model creates chronic backlogs: the people who understood business processes had no direct way to act on that knowledge without going through a technical queue. AI is beginning to change this in a meaningful way. Large language models (LLMs) can now bridge the gap between business intent and technical execution, translating process requirements into workflow logic and making technical documentation accessible to other departments.
Development standards, security policies and architectural guardrails still matter and AI can help enforce them consistently at scale. The real opportunity is expanding who can contribute to automation, reducing bottlenecks and allowing the organization to move with greater agility across every function.
More access to AI tools inevitably means more risk, particularly when that access is not coordinated across the organization. One of the most prevalent governance challenges in 2026 is what practitioners have started calling “shadow AI”: employees using personal accounts on public-facing AI platforms, often with no awareness of the data exposure risks involved or the compliance implications of the business.
A credible AI governance framework starts with organizational alignment on which tools are approved and for which use cases, so that visibility is maintained across the business. It includes meaningful AI fluency programs and education on how to use them effectively, how to set appropriate boundaries for AI agents and what data can and cannot be shared externally. It also means integrating AI policy into existing information security, data privacy and compliance frameworks. Risk tolerance varies by organization and industry and the governance approach should reflect that. A staged rollout, piloting a single use case, learning from and applying those learnings before expanding, is far more sustainable than a broad simultaneous deployment.
One of the most common questions businesses ask is: Where do we start? With AI applicable to a vast range of processes, the breadth of options can become a barrier to action. The most practical approach is to follow friction. Where are people spending time on tasks that are repetitive, error-prone or dependent on reviewing or interpreting large volumes of information? Those are the places where AI tends to deliver the clearest, most measurable value and where the case for change is easiest to make internally.
Not every process is a good candidate for AI and organizations that try to automate everything at once tend to dilute their results. Identifying a narrow, high friction use case, piloting it rigorously and learning from the experience before expanding, is the approach that consistently produces lasting results.
As AI agents are now becoming a crucial part of many businesses, enterprises face a dilemma: build a custom solution from the ground up or adopt something off the shelf? The answer depends on context but getting it wrong carries real consequences either way. Out-of-the-box agents offered by major software vendors can be an excellent starting point. They are faster to deploy, require less internal technical expertise and can deliver immediate value within the ecosystem they are designed for. The limitations tend to surface over time. Vendor-built agents more often do not connect seamlessly to other systems are customization options are frequently constrained.
Custom development offers greater control and flexibility, but it comes with its own set of trade-offs, higher upfront investment, longer time to value and ongoing maintenance responsibility. The organizations that struggle most are those that make a build vs. buy decision in isolation, without considering how their agents will need to connect, scale and evolve as the business changes.
Baker Tilly helps organizations safely and effectively harness AI. Our AI consulting services support you from strategy development through implementation, including model design, data and AI governance, workflow automation and organizational readiness programs. With specialists across different industries and a business‑first approach, we help you identify the right use cases, integrate AI into natural workflows and achieve measurable, secure outcomes.
For more information on enterprise automation, watch this on-demand webinar. Baker Tilly’s KC Fike walks through the most pressing challenges enterprises face as they try to operationalize AI in 2026 and the fundamentals that matter most.
Ready to accelerate your AI journey? Connect with our AI consulting team to get started.
