Agentic AI and your business: The next chapter of the AI opportunity

If 2025 was the year mid-market leaders asked how to make AI pay back, 2026 has been the year they started asking what "agentic" actually means. The term has dominated boardroom conversation this year, but many CFOs, CEOs and COOs are still working through what distinguishes agentic AI from the tools they already use, and what its development could mean over the next twelve to twenty-four months.

HLB's Data & Systems specialists see agentic AI as a significant step forward: technology capable of carrying out a series of connected tasks towards a defined objective, with less need for human intervention at every stage.

For mid-market businesses, that could make sophisticated operational capabilities accessible without the technology budgets and resources traditionally associated with much larger organisations.

What agentic AI actually means

Three things often get conflated in client conversations, and they are worth keeping separate:

1. Traditional automation, or robotic process automation (RPA), is rule-based and deterministic. It does the same thing every time, exactly as instructed, but can struggle when the process, inputs or underlying systems change.

2. Generative AI assistants such as ChatGPT, Copilot and Claude typically keep a human in the loop. The user asks, the assistant drafts, the user reviews. They can accelerate the work, while a person remains involved throughout the process.

3. Agentic AI takes multi-step actions on a user's behalf, often across several systems, towards a defined goal. Depending on the use case and permissions it has been given, an agent might schedule a meeting, reconcile information between systems or triage an inbox without requiring a person to direct every individual step.

As these capabilities move beyond experimentation and into business use, their implications become more significant. The governance implications of agentic AI are more complex than those associated with traditional automation or generative AI assistants, particularly when systems are given permission to act as well as generate an answer.

How the conversation with your data is about to change

One development helping to make agentic AI more practical is the Model Context Protocol, or MCP. In plain terms, it is a standard way to let an AI assistant query and act on a specific business system in natural language. Anthropic published it as an open standard in November 2024. It has since been adopted by OpenAI, Google DeepMind and Microsoft.

In December 2025, it was donated to the Linux Foundation's newly formed Agentic AI Foundation, putting it on neutral, vendor-independent ground. For businesses, this could make it considerably easier for AI to work with the systems and data they already use.

The potential extends across functions and platforms, including finance systems, CRM platforms, project management tools and operational databases where suitable integrations are available.

Instead of going to the application for answers, a manager could ask a question in natural language and receive an answer drawn from authorised business data. Depending on the controls and permissions in place, an agent could then carry out follow-up actions such as updating a record, raising a flag or drafting a response.

This could reduce some of the intermediary work that occupies management time today, including static dashboards, routine status reports and meetings primarily used to exchange information. The opportunity is to redirect that time towards analysis, judgement and higher-value decision-making.

Designing the operating model that comes next

Introducing agentic AI into day-to-day operations raises a bigger question for leaders: how should work be organised around it?

One immediate consideration is human oversight. Businesses need to decide where a person should remain involved in a workflow and what level of authority an agent should have. Regulatory requirements will determine some of those decisions; others will depend on reputational risk, financial materiality and the level of judgement involved.

There is also the question of role design. Withum (HLB USA), whose AI advisory practice has spent recent years helping mid-market clients move pilots into production, sees the relationship between people and technology evolving as AI takes on more of the initial processing and production work. Junior roles in particular could change when an agent produces the first draft or completes routine processing, leaving employees to focus more heavily on judgement, escalation and review.

This raises a further question for leadership teams: how will junior employees develop the experience and judgement required for those responsibilities if some of the traditional entry-level work is automated?

Service delivery models may change too. Where professional or outsourced services have traditionally been priced partly around the people and time required to complete the work, greater automation creates pressure to demonstrate and price value through outcomes. Businesses may encounter this both in the services they sell and those they procure.

Technology procurement will also need to reflect the arrival of AI agents. Every SaaS contract signed in the next twenty-four months should be assessed for how easily the platform can interact with other systems, what permissions an AI agent can be given, how access is controlled and whether its actions can be monitored and audited.

As GHJ's (HLB USA) advisory leadership has put it in network discussions, these choices increasingly form part of a company's wider technology architecture and procurement strategy. Decisions made when renewing or purchasing systems today could determine how easily a business can adopt agentic capabilities in future.

There will not be a single answer to each of these questions. Leadership teams should start addressing them while they still have the opportunity to shape how agentic AI enters the organisation.

Turning agentic AI into a practical business opportunity

Across this series, we have looked at three connected challenges: generating value from AI, establishing trust and governance, and understanding where the technology is heading. Businesses that make progress across all three will be better placed to benefit from the next phase of AI adoption.

For mid-market businesses in particular, agentic AI has the potential to narrow some of the operational advantages historically enjoyed by larger competitors. Processes that once required significant teams, technology investment or manual coordination could become easier to automate and scale.

Realising that potential will require preparation. Reliable data, appropriate governance and a clear understanding of how roles and workflows should change will determine where agentic AI creates useful capacity and where it introduces unnecessary risk.

HLB firms are already helping clients work through these decisions. Drawing on expertise across data, technology, risk and business advisory, our teams can help organisations assess where agentic AI has a credible business case, understand the controls it requires and prepare their operating models for the changes ahead.

What could agentic AI mean for your operating model?

If your team is starting to ask how agentic AI could reshape the way your business runs across its roles, processes and service model, HLB's Advisory and Data Analytics & Business Intelligence teams can help you identify the opportunities, assess the operational and governance requirements, strengthen your data foundations and determine where agentic AI can support wider business objectives.

Speak to an HLB adviser to explore where agentic AI could create value in your organisation and what needs to be in place before you scale it.