Why agentic AI pilots stall and how to fix them

Date:
Mon, 19 Jan 2026 15:23:11 +0000

Description:
Unlike generative AI tools, agentic AI act as autonomous agents that can reason, make decisions, and act across workflows to achieve goals.

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Agentic AI is the latest buzz in boardrooms. Unlike generative AI tools, agentic AI act as autonomous agents that can reason, make decisions, and act across workflows to achieve goals. Done right, they promise to reduce manual work and unlock new levels of productivity .

But many early adopters of AI tools are struggling. Pilot projects stumble, costs escalate, and results fail to match expectations. The problem isnt that agentic AI is overhyped, it is that businesses are moving too fast without
the strategy, infrastructure, and data foundations required to make them work as intended.

And this isnt surprising when you consider that 80% to 90% of all enterprise data is unstructured -- based on multiple analyst reports in recent years.

As someone who has built platforms through multiple waves of intelligent automation , Ive seen firsthand the same repeated patterns: technology alone doesnt transform organizations -- alignment, governance, and cultural readiness do. The real breakthrough comes when innovation is grounded in
trust and connected to business outcomes.

Where conventional AI might sort invoices, an agentic AI could approve payments, flag anomalies, and update compliance systems. That leap demands a contextual understanding of how data, processes, and rules fit together.

Too many organizations are treating agentic AI as bolt-on upgrade, as if theyre simply more advanced chatbots. The reality is more complex: agentic AI needs to be woven into the enterprise fabric, connected to the right data and workflows, and supported by governance. Without that foundation, autonomy quickly becomes chaos. Infrastructure first

One of the biggest stumbling blocks is infrastructure. Many enterprises still run on siloed content repositories, legacy systems, and fragmented integrations. In these environments, agentic AI cant access the full unstructured data they need to perform at their best.

In government, for example, content and processes are spread across different agencies, often using decades-old applications . Asking an AI agent to make decisions without integrating those systems is like asking it to assemble a puzzle with half the pieces missing.

Preparing for agentic AI requires investing in cloud-native foundations and interoperable content platforms that unify information and enable seamless connections across applications. Without this groundwork, agentic AI risk acting on partial or outdated information, and making flawed decisions as a result. Bad data kills autonomy

Even with the right systems in place, poor data quality is a critical flaw. Agentic AI thrives on complete, accurate, and governed information. If datasets are inconsistent or scattered, agentic AI cant make sound decisions.

Healthcare illustrates this challenge clearly. An agent supporting clinicians must pull from medical histories, lab results, and imaging data in real time. If one piece is missing or misaligned, the recommendations these agentic technologies produce could be flawed.

The lesson for early adopters is clear: start with a data audit and gain a firm understanding of where your unstructured data is. Know what you have, where it lives, and how its governed before handing decision-making power to AI. Getting governance right

Another misconception is that agentic AI removes people from the loop. In reality, the most effective early use cases blend autonomy with oversight.

Take financial services. Agentic AI may verify documents and draft compliance reports, but humans still make the final call on high-risk cases, or how to proceed when a document is flagged by an agent. This balance accelerates workflows without eroding trust and accountability.

Strong governance must be embedded from the outset, covering regulation, ethics, and operational control. Without it, these agents risk amplifying bias, undermining trust, and exposing organizations to compliance failures. Lessons from early adopters

The experiences of early adopters reveal three clear lessons.

The first, projects work best when they begin with a clear business outcome, not a fascination with the technology or jumping on a trend. Organizations that take time to define the processes they want to improve and the results they need to achieve are the ones seeing value.

Second, they invest early in the groundwork. Modern infrastructure and clean data may not grab headlines, but they are essential to making the headline-grabbing innovations possible.

And finally, they treat autonomy as something to scale gradually. The most effective implementations begin with human-in-the-loop models and only expand to greater autonomy once confidence and maturity grow. This approach builds trust in the technology while maintaining accountability.

These early lessons are already shaping a picture of maturity. The shape of maturity

As agentic AI matures, it will move beyond isolated experiments and towards interconnected systems. The real breakthrough will come from agentic AI networks coordinating across workflows.

In a hospital, for example, one agent might surface patient histories,
another manages scheduling, and a third flag billing issues; all contributing to a shared context that supports clinicians.

Proof-points will become a non-negotiable. Businesses will expect agents to show their work, like the data they used, the reasoning they followed, and
the compliance checks they applied. Without this transparency, agentic AI
wont be trusted to handle sensitive or high-value work.

And the technology landscape itself will have to open up. Organizations will want the flexibility to integrate agentic AI powered by different models, switch providers as needs evolve, and scale across hybrid or multi-cloud environments. Flexibility and interoperability will be essential to protect long-term investments. Beyond the hype

Far from failing, agentic AI is in its adolescence. Just as cloud computing went through a difficult transition phase before proving indispensable,
agents too will require a period of adjustment.

The organizations that succeed will be those that prepare best, not adopt the fastest. By aligning strategy, modernizing infrastructure, cleaning data, and embedding governance, enterprises can move from experimentation to transformation.

With the right foundations, agentic AI can do far more than just automate tasks. It will enable genuinely intelligent systems that reshape how work
gets done and that could be the most significant shift in enterprise technology for a generation.

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