The missing step between trying AI and getting value from it

The Short Answer Is…

The businesses getting the most value from AI aren’t starting with the technology. They’re identifying recurring frustrations, repetitive questions and knowledge gaps, then using AI agents to solve those specific problems. The most successful use cases are often simple, practical and focused on helping people access information more easily.

Starting With The Technology Instead Of The Problem

When businesses first begin exploring AI Agents, it’s natural to focus on what the technology can do.

• Can it answer questions?
• Can it automate processes?
• Can it replace manual tasks?

They’re useful questions, but they often lead organisations in the wrong direction.

The businesses getting the most value from AI Agents aren’t starting with a list of features and trying to find ways to use them. They’re starting with a frustration and thinking about how AI can help solve it.

Maybe employees keep asking the same HR questions. Maybe onboarding relies heavily on one person sharing information with every new starter. Or perhaps the IT team spends hours each week responding to requests that have already been answered elsewhere.

Those are the conversations worth paying attention to. Once you’ve identified a recurring problem, it’s much easier to decide whether an AI Agent can help solve it.

Thinking The First Use Case Needs To Be Transformational

There’s often a feeling that an AI project needs to be impressive to justify the investment. Businesses start looking for a use case that will dramatically improve productivity, transform a department or completely change the way work is done. Whilst this is understandable, that expectation can make it surprisingly difficult to get started.

The examples we see deliver the most impactful value are often much simpler than people expect. Things like:

• An HR Policy Assistant that helps employees find answers without searching through documents.
• An onboarding assistant that helps new starters find information.
• A compliance assistant that can answer questions based on existing policies and procedures.

Individually, these aren’t particularly glamorous or hugely transformative. But because they solve a genuine problem, your people actually use them, which embeds AI into your organisation.

Overlooking Opportunities Hiding In Plain Sight

One of the most interesting observations from recent conversations around AI Agents is how often businesses are already sitting on the information they need. Businesses don’t need to use AI to create knowledge; but they do benefit from using AI to make existing knowledge easier to access.

For example, think about the questions that get asked repeatedly across your organisation.

• Where can I find this document?
• What’s the process for doing that?
• Do we have a template for this?
• How did we answer this question last time?

Most businesses already have those answers somewhere. They exist in policies, procedures, knowledge bases, training documents, previous proposals and internal documentation. But finding that knowledge can be another story entirely. That’s why some of the strongest AI Agent use cases are simply built around helping people access information that already exists.

Moving From Questions To Jobs

For many organisations, AI becomes much more useful when they stop thinking about it as a tool and start thinking about it as a role. Instead of asking AI different questions every day, they’re giving it a specific job.

That’s essentially what an AI agent is. Rather than acting as a general-purpose assistant, an agent is designed around a specific task, audience or body of knowledge. The goal isn’t to answer everything. It’s to consistently help with one particular area of the business.

That could be:

• Supporting employee onboarding
• Answering HR policy queries
• Helping users find internal documentation
• Assisting with compliance questions
• Surfacing previous client proposals

Each agent has a clear purpose, a defined audience and a trusted source of information.

That clarity makes it easier for people to understand when to use it and easier for businesses to measure the value it’s providing.

The Missing Step

Trying AI shows you what the technology can do. Getting value from it means working out what you actually need it to do.

That usually starts with something familiar: a question that keeps being asked, information people struggle to find or a process that relies too heavily on one person knowing the answer.

Once you’ve identified that problem, you can give an AI Agent a clear job, provide the right information and see whether it makes a genuine difference to the people using it.

So, rather than starting your next AI discussion with “What could we build?”, try asking “What do our people need help with?” The answer may give you the use case you’ve been looking for.