Greg Coleshill, Chief Commercial and Operating Officer
If you want people to make sense of artificial intelligence, start with the words you use.
Language is not window dressing. It frames how people think and, importantly, it influences behaviour.
It decides whether colleagues picture a helpful teammate beside them or a black-box agent wandering off on its own.
Get the language right and you unlock adoption, value and safer outcomes. Get it wrong and you slow everything down.
Most firms have rolled out horizontal gen-AI tools like chat and document copilots. Useful, yes, but value often feels unclear because leaders talk about a platform rather than a teammate with a clear job.
The shift under way is toward agentic systems that behave like proactive collaborators. The message that lands is simple: describe the colleague that helps a claims handler close a case or a merchandiser tune a range. Skip the abstract platform talk.
Independent studies say the same thing. Adoption is high while genuine transformation is patchy. Pilots stall when tools do not learn from feedback or fit everyday workflows. The fix is to put AI inside specific jobs to be done and measure success on business outcomes, not demo sparkle.
Teams follow words they trust.
What works in practice: democratise access, co-design with the people who live the work, and strip out the process friction that blocks vertical use cases from scaling. The best performers keep humans at the centre, use AI to accelerate insight and lean into customer understanding.
Productivity gains land when you talk like a human.
Jargon does not calm risk. Specifics do. Be clear about what can go wrong and what is watching.
The answer is real-time guard rails and auditable decisions, not policy binders no one reads.
We are focusing on teammates that sit in real workflows and move numbers leaders already track.
Complaints Support Teammate
Drafts clear, plain-English responses that follow DISP rules and our updated procedures. Handlers remain in control and finalise replies. Early targets include higher accuracy, quicker case resolution and fewer reworks. The teammate learns from templates, standards and policy content, and is set up to reduce review time and weekly effort for the team.
Policy Guidance Teammate
Turns our policies and external sources into step-by-step guidance, checklists and examples. Outputs are drafts. Accountability stays with policy owners. The aim is faster, more consistent implementation and less reliance on one team for every question, while raising quality and speeding awareness across the business.
These are not side projects. They sit where work happens, show their sources, and leave an audit trail so risk and audit can see what changed and why.
We deliver AI teammates for regulated businesses. We enable productivity, safely, with real-time guardrails.
We believe the future of work is AI teammates collaborating with humans to lift outcomes. Others share that belief. Where we differ is how it comes to life:
We encourage leaders to see AI differently. Stop treating it like software. Treat it like a teammate. Like any new hire, it needs onboarding and coaching, and people need time and evidence to trust it before it reaches peak productivity.
October 14, 2025
Greg Coleshill, Chief Commercial and Operating Officer
October 14, 2025
If you want people to make sense of artificial intelligence, start with the words you use.
Language is not window dressing. It frames how people think and, importantly, it influences behaviour.
It decides whether colleagues picture a helpful teammate beside them or a black-box agent wandering off on its own.
Get the language right and you unlock adoption, value and safer outcomes. Get it wrong and you slow everything down.
Most firms have rolled out horizontal gen-AI tools like chat and document copilots. Useful, yes, but value often feels unclear because leaders talk about a platform rather than a teammate with a clear job.
The shift under way is toward agentic systems that behave like proactive collaborators. The message that lands is simple: describe the colleague that helps a claims handler close a case or a merchandiser tune a range. Skip the abstract platform talk.
Independent studies say the same thing. Adoption is high while genuine transformation is patchy. Pilots stall when tools do not learn from feedback or fit everyday workflows. The fix is to put AI inside specific jobs to be done and measure success on business outcomes, not demo sparkle.
Teams follow words they trust.
What works in practice: democratise access, co-design with the people who live the work, and strip out the process friction that blocks vertical use cases from scaling. The best performers keep humans at the centre, use AI to accelerate insight and lean into customer understanding.
Productivity gains land when you talk like a human.
Jargon does not calm risk. Specifics do. Be clear about what can go wrong and what is watching.
The answer is real-time guard rails and auditable decisions, not policy binders no one reads.
We are focusing on teammates that sit in real workflows and move numbers leaders already track.
Complaints Support Teammate
Drafts clear, plain-English responses that follow DISP rules and our updated procedures. Handlers remain in control and finalise replies. Early targets include higher accuracy, quicker case resolution and fewer reworks. The teammate learns from templates, standards and policy content, and is set up to reduce review time and weekly effort for the team.
Policy Guidance Teammate
Turns our policies and external sources into step-by-step guidance, checklists and examples. Outputs are drafts. Accountability stays with policy owners. The aim is faster, more consistent implementation and less reliance on one team for every question, while raising quality and speeding awareness across the business.
These are not side projects. They sit where work happens, show their sources, and leave an audit trail so risk and audit can see what changed and why.
We deliver AI teammates for regulated businesses. We enable productivity, safely, with real-time guardrails.
We believe the future of work is AI teammates collaborating with humans to lift outcomes. Others share that belief. Where we differ is how it comes to life:
We encourage leaders to see AI differently. Stop treating it like software. Treat it like a teammate. Like any new hire, it needs onboarding and coaching, and people need time and evidence to trust it before it reaches peak productivity.
