Why 'teach everyone to prompt better' isn't a strategy – and why agents change the game

The latest IOIC Impact Brief  flagged something that surprised me – we're way too polite!

Seventy-one per cent of UK AI users are polite to their chatbot – and why shouldn't our human nature be to say please and thank you when asking someone to help us? But the report shows being too polite could be giving us less accurate results, which opens up a bigger question: how do organisations control the quality of AI outputs when every employee is bringing their own communication style to the prompt?

In my role as head of client experience at Silicon Reef, I hear first-hand the conversations happening across our client base. Nearly everyone's story is the same. People are experimenting with AI, and the results are often impressive. But the outputs are wildly inconsistent. Not because the tools are inconsistent. Because the people are.
 

Prompting is personal – and that's the problem

How you prompt an AI tool is shaped by how you communicate naturally. Your personality, your cultural background, your level of digital confidence, whether English is your first language and whether directness comes naturally to you.

The IoIC Impact Brief quotes Penn State professor Dr Akhil Kumar, who says the optimal approach is "very business-like and specific to the point". That style works well for some people. For others it's unfamiliar – and that's not a skills gap you can close with an afternoon training session.

What this means in practice is that a cautious HR manager and a digitally confident marketer can run the same task through the same tool on the same day and get outputs that are miles apart. For personal use, that's fine. For business-critical, repeatable work – drafting comms, maintaining tone of voice and running standard internal communication processes – it's a real organisational risk.

The conversations I'm having with senior internal communication professionals quickly turn to consistency, governance and measurable outcomes. How do we make sure outputs are consistent, regardless of who uses the tool?

Individual prompting strategies, or cumbersome prompt libraries, can't answer those questions.

Agents can.

What an agent actually changes

In the simplest terms, an agent has a pre-defined job. The thinking - the framing, the context, the tone, the data sources - goes in once and is applied consistently every time after that.

In other words, you don’t need to rely on prompts for a consistent output.

A well-defined, well-governed agent runs the same job, the same way, every time. Often autonomously, on a trigger or a schedule. You don’t need to prompt it at all.

For those who joined our workshop at the IoIC Festival this year, you will have seen we talked about exactly this. The critical distinction between standard Copilot Chat and an agent is that an agent already knows the job and your context. With Copilot Chat, you start every conversation from scratch, and the output quality depends on whatever prompt you happen to write that day. With an agent, you get the same quality output every single time - regardless of who triggers it, what mood they're in, or how naturally the direct, business-like register comes to them.

Some of the agents we’re speaking about with IC teams show exactly what this looks like in practice. For example, an exec voice agent that drafts a weekly CEO digest in the leader's actual voice, grounded in real business data, every Monday morning - not dependent on whoever happens to have the skills and headspace that week. A sentiment agent scanning Viva Engage daily, flagging what needs attention, with no manual prompting required.

We all know by now that AI can pick up the heavy, manual work that eats away at your week – the reformatting and re-posting of content across channels, the hours of data analysis. Agents handle all of it, identically every time. That frees up the team to do the work that actually requires human judgement.

The equity argument

This is the part I find most compelling, and I don't think it gets enough airtime.

The "business-like, direct" prompting style that gets the best AI outputs isn't a neutral standard. It maps naturally onto certain communication styles and backgrounds - and less naturally onto others. Non-native English speakers. Neurodiverse colleagues. People from cultures where that level of directness isn't the norm. Less digitally confident employees. These groups aren't bad at AI. They're being disadvantaged by a system that rewards a particular way of communicating.

From the relationship I have with many of our clients, I know that IC professionals care deeply about equitable access to information and tools. Agents extend that principle into AI. The expertise goes into the design once. Everyone who uses it gets the benefit of that expertise, regardless of how they'd naturally phrase a request.

For a function whose job is to make sure everyone has access to what they need, that matters.

Where this points

If your organisation is planning to get maximum value from AI in the next few years, you don’t necessarily need to train an army of AI-confident individuals. But, you do need to progress from “everyone experiments with prompting” to “we’ve defined specific AI roles with consistent, auditable output.”

Crucially, that's not a story about replacing human judgement. The IC work that requires strategic thinking, empathy, and genuine insight - the work IC professionals are increasingly being asked to do at leadership level - stays with people. But the repeatable, process-heavy groundwork? Agents handle that more consistently, more equitably, and with better governance than individual prompting ever will.

The question I'd put to any senior IC professional right now is a simple one: what are the repeatable tasks in your team's week that depend entirely on whoever happens to be doing them? Because that's exactly where an agent belongs.

Amanda Harvey is Head of Client Experience at Silicon Reef, a Microsoft solutions partner specialising in Microsoft 365 and AI-powered tools for internal communications teams.