Agentic AI for mid-market companies: a structural advantage

According to Everest Group research commissioned by R Systems, more than 40% of mid-market enterprises are bypassing the traditional stages of artificial intelligence adoption and moving directly toward agentic AI for mid-market companies as a strategic priority rather than an experiment. The companies that scale successfully over the next 12 months will hold a compounding advantage: 93% of business leaders surveyed by Capgemini agree on this point. And yet Everest found that only 15% of mid-market enterprises have operationalised agentic AI across functions.

Our experience in the field shows that this gap is not a technology problem, but an implementation one. AI power on its own does not close it: the difficulty lies in wiring agents into the business processes that actually run the company. Mid-market companies are structurally better positioned to do this than either large enterprises or small businesses operating without the same operational complexity. We first set out this argument in Antoni Kozelski’s Forbes Technology Council column; this piece develops it with the practical steps we apply in the field.

The structural advantage of mid-market companies

Large enterprises are navigating what Deloitte’s 2026 State of AI in the Enterprise survey identifies as the primary barrier to integration: the AI skills gap, which most organisations are addressing through education programmes rather than workflow redesign. The result is slow, internally contested transformation cycles that stretch across entire quarters and require sign-off from centres of excellence, procurement committees, and security teams.

Small businesses face the opposite problem. They often lack the cross-departmental operational complexity where the hidden value of agentic AI lies, and their single-department workflows of limited volume are better served by off-the-shelf automation tools.

Mid-market companies sit in the space that agentic AI workflow automation is best suited to address. Their processes are complex workflows that reach across departments and draw on multiple data sources, and they are domain-specific enough that no platform tool handles them out of the box. A goal-driven agent can reason across billing, analytics, and support records at once, provided it is integrated with the enterprise systems that hold them. This is the ground an agentic AI system is built to cover, and it is exactly the ground off-the-shelf tools cannot reach. At the same time, mid-market decision-making structures are flat enough that a C-level sponsor can move a project from roadmap to production without navigating committee review cycles. That combination, unique operational complexity paired with implementation agility, is where the greatest potential is hidden.

Where most mid-market companies stall

Everest Group data shows 57% of mid-market companies remain in controlled pilot phases. We have encountered more than a few stuck in this gap, and the causes are consistent.

They start with technology rather than the problem. A use case is chosen because it looks promising, not because it removes a critical bottleneck. The real return lives in back-office processes with high manual load, where gains in operational efficiency are largest but least visible in a technology-led scoping session.

They treat the proof of concept as proof rather than discovery. Early deployments answer questions no strategy document can. Using them only to validate a predetermined plan skips the learning phase that makes production deployable.

They allow governance to arrive late. Only 7% of mid-market enterprises have agentic-specific governance policies in place. Governance and security must be designed into the architecture from the first deployment, with human oversight and audit trails in place before the system goes live, not retrofitted afterwards.

They choose the wrong implementation partner. A partner who can confirm they can build something is not the same as one who can explain exactly how, based on prior implementations in comparable environments. Fluency in the latest AI technologies matters far less than a well understood roadmap, forged in experiments and detailed scoping, which is what produces measurable performance in a critical workflow.

How these processes are handled today

At present, most mid-market companies manage their highest-complexity processes through manual coordination, shared spreadsheets, and siloed software tools. Much of the work consists of repetitive tasks handled by people, and much of the operational knowledge sits in individual heads rather than in shared knowledge bases. An order query that touches three departments requires three handoffs. A compliance check drawing on four systems requires four people. The process works, but at the cost of time and headcount, and it does not scale without adding more of both.

This is the baseline every agentic AI business case should start from. Before automating anything, we map who performs the process today, how long it takes, and where it breaks down. That current-state map is what grounds a project in the real world rather than in a demo, and it is what makes the value of automation measurable rather than assumed.

Where the scale opportunity lives

The use cases that deliver the fastest returns are not always the most obvious. Everest Group’s mid-market research identifies IT operations as the most deployment-ready function, with software engineering delivering close to 30% efficiency improvement across monitoring, requirements gathering, and testing. Customer service, finance, and accounting follow, with their structured workflows and clearly defined action boundaries.

The practical path is incremental. Begin with a high-volume, well-defined back-office process, typically one built from routine tasks. Demonstrate measurable output. Use that output as the foundation to expand scope. Where a workflow still involves judgement, a human-in-the-loop review keeps a person on any step that requires human approval, so autonomy expands only as fast as trust allows. With the right use case, a workflow that once required navigating several systems by hand can be reduced to a task measured in seconds, and the saving compounds every day the system runs. This is the pattern behind our text-to-workflow platform, which cut engineers’ tedious task time to seconds by letting an agent translate a plain-language request into an executed engineering workflow.

What the data and our experience show

Across more than 30 agentic AI implementation projects we have delivered, the organisations that reached production shared three characteristics. They started with a clearly defined operational problem. They treated the first deployment as a discovery mechanism, not a verdict. And they chose a partner with applied experience in production-grade systems. Our TriStorm methodology is built around exactly this sequence, moving from use-case discovery to a deployed, observable system.

“The gap between mid-market ambition and mid-market results is not a technology gap. It is an implementation gap, and it is the one thing a company cannot outsource to a platform.”

Antoni Kozelski, CEO and founder, Vstorm

Companies that get ahead of the curve, matching their ambition with a partner who can supply the necessary skills, security, and governance, are best placed to turn their biggest resource drags into practical agentic AI workflows. The returns compound over the long term, and with them, a sustained competitive advantage.