Highlights
- Agentic AI marks a step change for mid-market companies, moving beyond simple prompts to autonomous agents that can break down tasks, call relevant tools and deliver structured outputs with minimal human input.
- The speed vs governance trade-off is a leadership decision, not an IT one. Businesses must weigh competitive advantage against risk, and that call needs to come from the top.
- Data foundations determine who benefits from agentic AI. Companies with fragmented or poorly structured data will struggle, while those with strong infrastructure are best placed to unlock real value.
The shift to agentic AI offers a change in how work is executed. For mid-market companies, that shift may prove less about catching up with larger peers and more about leapfrogging them.
For much of the past two years, enterprise AI has been about productivity, using LLMs to respond to prompts to analyse documents and draft content more quickly. These incremental gains are giving way to more meaningful benefits, effectively reshaping how work itself gets done. Inflexion’s recent AI webinar discussed the emergence of agentic AI.
For mid-market companies, agentic AI can be game-changing as it vastly simplifies complex automation.
This is because you can describe the outcome, provide access to the right data and tools, and let the system find a way – potentially substantially more convenient than providing layers of process, systems integration and bespoke tooling.
Understanding how it works is key to uptake, so the Inflexion webinar included live demonstrations to showcase how tools such as Claude can ingest multiple raw inputs – Excel files, board commentary, pipeline data – and synthesise them into structured outputs such as dashboards, board packs or even fully formatted presentations. As Rory Cooke, Senior Data Analyst at Inflexion, put it: ''Claude is not just a chat bot anymore. It's able to work with all these different raw inputs and generate structured outputs like dashboards, PowerPoint and Excel files.”
From prompts to objectives
The changes see one-step interactions give way to multi-step reasoning, with agents able to break down a task, call the relevant tools (whether that is querying a CRM, pulling files from SharePoint, or writing code), iterate when errors occur, and validate outputs before completing the task.
This is where tools such as Claude Cowork become powerful. Agents can operate autonomously across the same systems as employees - not just doing existing work faster, but handling volumes and complexity that would previously have required coordination across multiple teams.
At the more advanced end, tools such as Claude Code push this further still. The webinar demonstrated how an agent can build and deploy an application – in this case a configure-price-quote system – by writing, testing and iterating on code autonomously. This is not automation in the traditional sense; it is goal-directed execution.
A strategic, not technical, question
The enhanced capability begets a more strategic set of questions. If, as suggested in the session, “whatever can be automated will be automated”, then businesses need to reassess their differentiation, validating the “right to win” in a world where core processes may no longer be a source of advantage.
The speed vs governance trade-off
An important point from the webinar was the trade-off between speed and control. While moving quickly may bring productivity gains and competitive advantage, moving cautiously may reduce exposure but risk falling behind. There is no right answer – each business must decide its own balance based on its risk tolerance, regulatory environment and strategic priorities. It is about leadership. Decisions on where to deploy agents, how far to automate, and what risks to accept cannot be delegated to IT alone; AI change must come from the top.
It is important to remember that many of the risks are not entirely new. Agents inherit the permissions and vulnerabilities of existing systems, however a key difference is scale and speed: what a human could do slowly, an agent can do rapidly and repeatedly.
Data foundations are key
If agents are the engine, data is the fuel, with agents only as good as the context they can reach. For mid-market companies, this is both a challenge and an opportunity. Those with fragmented, inaccessible or poorly structured data will struggle to realise value and agentic AI can highlight weaknesses. Conversely, those which already invested in robust data infrastructure and foundations can reap real rewards.
External support can accelerate progress, and experienced private equity partners can play an important role. “Management teams hear about AI constantly. We see it as our job to help our portfolio companies understand AI: what does it actually mean for mid-market companies, concrete examples of what the tools can do, and what steps leadership can take forward to progress,” enthuses Jan Beitner, Inflexion Director, Data & AI.
From experimentation to adoption
Perhaps the most practical takeaway is that understanding comes from use. These tools are evolving quickly, and their capabilities are not always intuitive. Structured training and peer learning can shorten the curve, but ultimately leadership teams need hands-on exposure to appreciate both the opportunity and the limitations.
All Inflexion portfolio companies, regardless of size or ownership stake, have full access to our dedicated value acceleration resources covering digital enhancement (including data, AI, technology, cybersecurity and digital marketing), international expansion, M&A, sustainability, commercial strategy and talent management.
