Lack of foundational infrastructure limits AI agent success in enterprises
A lot of companies are betting big on AI. 68% of enterprises are putting more than half a million dollars into AI programs every year. That shows ambition, maybe even urgency. But here’s the problem: 86% of these same organizations don’t have the basics in place to execute. Their infrastructure isn’t ready.
AI agents depend on a wide stack, clean, accessible data, integrated tools, and secure, reliable environments. Think about how decisions are made within your company, data flows through systems, gets interpreted, and results in action. AI agents are trying to do the same, but without the human judgment and flexibility. So, if your systems are disjointed, your AI can’t perform. You end up with tools that look sophisticated but don’t deliver results.
And there’s a growing gap. According to Gartner, by 2028, 15% of all workplace decisions will be handled by AI agents. At the same time, they predict 25% of enterprise security breaches will involve AI misuse. The lesson here isn’t “go slow.” It’s “build smarter.” The future will run on autonomous workflows and machine-driven decisions, but only for companies that prioritize infrastructure first.
The reality is, you won’t scale AI with isolated systems, duct-taped platforms, or unstructured data. Closing that infrastructure gap must be objective number one. That’s how you turn AI from a prototype into a profit engine.
Fragmented data ecosystems hinder effective AI agent deployment
Data is either your greatest asset or your biggest bottleneck. For most companies trying to deploy AI agents, the problem isn’t the AI, it’s the data ecosystem. In a recent survey, 79% of organizations said they expect their data challenges to slow down or derail AI projects. That’s a majority acknowledging a fundamental problem they still haven’t solved.
The issue is fragmentation. Many enterprises have data locked in separate platforms with limited or siloed access. A sales platform here, a customer service tool there, legacy databases all over the place. These don’t talk smoothly to each other, and worse, they lack context when stitched together. So even when AI agents can technically access multiple sources, they struggle to make sense of what they’re seeing. Contextual understanding, knowing which data matters, when, and why, is essential. Without it, AI agents deliver shallow output.
Executives need to get this: fragmented systems aren’t just a technical nuisance. They’re a strategic blocker. If you want AI to drive decision-making and automate workflows, then system coherence and unified data orchestration need to be built in, across departments, platforms, and third-party applications.
Enterprises spend years perfecting customer journeys and internal workflows. Trying to plug AI into broken or disconnected systems won’t give you better outcomes, it just accelerates the chaos. Fix the data layer, make your systems interoperate cleanly, and then bring in AI agents. That’s where the compounding value begins.
Underprioritization of the tool foundation restricts agent capabilities
Most AI agent initiatives fail to go beyond analysis. The reason is simple: companies focus either on building custom infrastructure or activating off-the-shelf AI features. Neither path alone gets you to value at scale. You end up with agents that can give you information but can’t do anything with it.
In too many cases, teams burn months building pipelines, authentication layers, and access controls, only to discover they’re still far from actual AI-driven execution. On the other hand, layering AI onto existing SaaS tools delivers cosmetic improvement but lacks scope. When agents are confined to a single tool, they can’t automate entire workflows. That means more manual intervention, more integration issues, and delayed results.
Even worse is the middle path, custom code on top of vendor solutions. This creates fragile systems that don’t perform under scale. They collapse when exposed to real user traffic or business complexity.
The tool foundation, giving agents the ability to take real, meaningful action, is critical. Without it, you get agents that observe, interpret, and then… stop. And that’s a missed opportunity. AI should reduce operational friction. To do that, agents need secure permissions to execute transactions, trigger workflows, and modify systems in controlled, auditable ways.
If you want enterprise AI to compound in value, the ability to act must be part of the original design.
Security and governance inadequacies pose risks
Security is where most AI deployments hit a wall. AI agents act on sensitive content. That raises new types of risks, ones that aren’t covered by traditional enterprise security frameworks. Hard-coded authorizations or basic access monitoring won’t hold up here. These agents must process data, make choices, and execute functions in real time. That’s a different risk surface.
According to a recent survey, 57% of enterprises name security as their number one challenge in deploying AI agents. That number goes even higher when you break it down, 62% of technical operators and 53% of leadership say they’re concerned. That alignment across roles tells you the problem is real and close to breaking point.
Many organizations require eight or more system integrations just to get an agent operational. Every one of those connections is a potential failure point, if not secured correctly. Point-to-point integrations don’t scale, and they don’t secure well under pressure. That creates exposure, especially as agents scale up and touch more critical systems.
AI governance has to evolve, from monitoring access to monitoring outcomes. What decisions are agents making? How are they interpreting data? Are they complying with organizational policies at every step? AI behavior must be transparent, explainable, and auditable. Anything less creates blind spots, and blind spots lead to breaches.
Security is an always-on requirement that needs to be embedded into architecture, not added after launch. If your AI agents aren’t governed tightly, on decision boundaries, data flows, and execution limits, you’re amplifying risks.
High-value use cases accelerate scalable AI deployment
The best AI deployments don’t start with broad ambition. They start with a well-defined, high-impact problem that can be solved clearly and measured precisely. The organizations that move fastest are the ones that resist the pressure to deploy AI across the entire enterprise upfront. They focus on a single, contained use case, something that already has reliable, accessible data and defined operational value.
The Aprende Institute is a real example. What they estimated as a multi-quarter rollout went live in just days. They didn’t get there by cutting corners. They got there by locking in the right foundation: clean data connections, standardized data orchestration, and a repeatable testing strategy. They didn’t overcomplicate the process, just executed well, starting small and scaling fast.
If you’re trying to pick the right starting point, go where the data is clear and the ROI is trackable. In the survey, 61% of enterprises said IT ticket resolution is one of the highest priority use cases for AI agents. It has structure, high frequency, and measurable outcomes, ideal for proving what’s possible without significant risk.
Executives should focus teams on pilot deployments that are achievable in weeks, not quarters. Those early wins set you up for long-term success. When the first use case performs, it paves the way for scaled deployment across departments, geographies, and new business units. But scaling without early proof is where most companies run into resistance, internally and externally.
Enterprise AI doesn’t succeed through volume. It succeeds through clear value at every stage.
Effective integration is the linchpin for unlocking AI’s transformative potential
AI agents aren’t going to deliver value unless they’re deeply integrated into the systems that matter, the CRMs, ERPs, support platforms, financial systems, and internal tools that run your business. Integration is essential. These agents need to pull data in real-time, act on it without breaking workflows, and push output back into systems of record.
Yet, most companies still treat integration as something to solve later. That doesn’t work. 90% of enterprises surveyed said integration with internal systems is key to AI success. So, if the answer is clear, the focus needs to catch up. It’s about connecting the right things the right way.
When AI agents sit outside of core systems, they create friction. They can’t complete tasks. They depend on human intervention. They miss business context. And when that happens, you end up with chatbots that deliver information, not outcomes. That’s not transformation. That’s a user interface with no engine.
The enterprises that unlock real AI value are the ones that prioritize integration early: APIs, authentication patterns, data pipelines, execution permissions, all structured from the start. This is about readiness. Because once the agent is embedded properly, scaling that logic to more functions becomes predictable.
If you care about ROI, and every executive should, then your integration strategy is the clearest lead indicator of whether your AI investment pays off. Get it right, and the rest gets easier. Get it wrong, and it doesn’t matter what platform you chose or how much you spent.
Key executive takeaways
- Infrastructure gap blocks ROI: Most enterprises (86%) invest heavily in AI but lack the foundational infrastructure, clean data access, integrated systems, and scalable architecture, needed to deliver results. Leaders should build infrastructure first to avoid wasted spend and stalled adoption.
- Fragmented data kills agent performance: 79% of organizations expect data challenges to derail AI rollouts. Executives must prioritize unified, contextual data ecosystems to enable agents to reason effectively and operate across workflows.
- Weak tooling limits business impact: Enterprises stuck between building custom solutions and integrating SaaS features often end up with agents that can’t act. Leaders should ensure that agents are equipped with tools to transact, automate, and execute, not just analyze.
- Security must be embedded at scale: 57% of enterprises cite security as the top barrier to AI agent deployment, especially with 42% requiring eight or more integrations. Governance, decision logic, and end-to-end monitoring need to be in place early to reduce risk as usage scales.
- Small wins lead to large-scale success: Starting with narrow, high-impact use cases, like IT ticket handling, can accelerate value and de-risk investment. Executives should fund targeted deployments that are measurable, repeatable, and ready to scale across the enterprise.
- Integration drives transformation: Nearly 90% of organizations agree that system integration is essential for AI success. Leaders must treat integration as a core priority, not a post-launch fix, to unlock enterprise-grade automation through AI agents.
