TL;DR  

Real AI adoption starts after the PoC. To scale successfully, pick a PoC that delivers real business value, built on well-defined processes and measurable outcomes. Treat AI like a product: iterate through MVP cycles with strong governance, clean data, and clear ownership. Maximise impact by building cross-functional capability, aligning IT and business, communicating openly, and starting with use cases that show quick, visible wins.

How to improve AI adoption and avoid money down the drain

When organisations reach a certain stage — the PoC is complete, the checklist is ticked off, SharePoint is clean, governance is in place, access controls are set, and Copilot is already live across the business — the next question becomes very simple:

What should we build next so that AI actually generates value, not just another experiment?  

This is also the stage where most AI initiatives stall. The technology might be ready, but the organisation isn’t designing for value, adoption, and iteration.  

We call this Value Architecture Design: identifying where AI can create value and designing solutions in a way that people will actually use.  

In this post, we outline how to select the right PoCs, how to scale from early wins to managed AI services, and how to prepare your workforce for meaningful, trustworthy adoption.  

What does real AI adoption look like?  

AI is not “successfully adopted” when the PoC works. It’s adopted when:

• teams understand how agents work and feel confident using them

• reusable components (prompts, agents, flows, APIs) emerge and are shared  

• the business iterates AI solutions like apps, continuously improving MVPs  

• decision-makers themselves understand enough to drive momentum  

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How to choose a PoC that delivers value and actually gets used  

A good PoC is not the most exciting part of the project, but it’s essential.  

It needs to:  

• sit on an already successful business process  

• Be well-defined and constrained  

• have clear, measurable outcomes  

• deliver relief from repetitive, manual work  

• create a sense of “finally, I don’t have to do this like a robot anymore”  

This is what we call Proof of Value, not Proof of Concept. Early lighthouse projects should:  

• reduce time spent on manual categorisation or triage  

• replace low-value cognitive tasks (“read, sort, route, summarise”)  

• demonstrate visible time savings or cost avoidance within weeks  

• be easy to explain and easy to show  

• create appetite for “what else can we automate?”  

A simple example:  
A flow categorises incoming emails → when it finds a certain category, it triggers an agent → the agent decides where the request should go and completes the next action.  

It’s clear, repeatable, and the repetitive manual work from the process.  

That’s the pattern you want.  

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Different users need different AI pathways  

Once the fundamentals are in place (SharePoint cleaned up, governance set, access controls defined), adoption becomes a layered journey:  

Layer 0 — Business users with no technical background  

• Use AI for information synthesis  

• Build small, safe mini-apps with Copilot Studio Light  

• No creation of new systems, just better access to existing knowledge  

Layer 1 — Managed Copilot Studio solutions  

• Built and iterated by more technical users  

• Governance, data connections, compliance configuration  

• Where structured APIs and reusable prompt libraries emerge  

Layer 2 — Pro-code engineering for fully custom solutions  

• Complex integrations, advanced orchestration  

• High-value automation tied into business-critical systems  

• Requires agile delivery: MVP → iterated improvements → continuous optimisation  

All three layers require different adoption strategies. All three can deliver value.  
But the PoC you choose determines which layer you are enabling.  

The biggest non-technical blockers are culture, clarity, and trust  

Technology rarely blocks adoption. People do.  

We see four blockers appear again and again:  

Poor stakeholder management  

Executives, end users, and IT all need to be aligned, and they rarely start that way.

Fear of automation  

People need to hear clearly: “This helps you. It does not replace you.”  

Disconnect between IT and the business  

Business knows the process; IT knows the tools. Agents require both sides to collaborate.  

Lack of clarity about decision rights  

• Who approves agents?  

• Who owns risks?  

• Who maintains the agent when the process changes?  

Without clear answers, trust is hard to establish and even harder to sustain.

How to prepare your workforce to collaborate with agents  

Adoption is ultimately about behaviour change. The mindset shift is:  

“AI is an extension of my tools, not a black box that takes over.”  

Organisations should focus on:  

• Training champions who mentor, explain limitations, and build confidence  

• Teaching teams how to design good prompts and document them in a prompt library  

• Regular feedback cycles (“What’s working? What’s frustrating?”)  

• Making the agent’s role transparent: what it does, where the data goes, how decisions are made  

• Ensuring agents always use up-to-date information  
  (The fastest way to break trust? Let an agent read from outdated files.)  

Think of this as AI workplace readiness, not AI training.  

The most successful teams build cross-functional capability, bringing together business process experts,  

• prompt engineers or AI solution designers,  

• data specialists,  

• integration and pro-code developers,  

• governance and security specialists,  

• and product owners who treat agents as evolving applications.    

Their mindset is agile rather than waterfall: start with an MVP, release it, gather feedback, and iterate continuously.  

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Governance is the foundation for sustainable, safe AI  

Good AI governance is not bureaucracy. It is clarity.  

Organisations need defined roles for:  

• Policy ownership and risk management (usually IT + security)  

• Quality assurance for prompts, agents, and data sources  

• Access control and data protection  

• Decision rights about when AI can act autonomously vs. when humans must step in  

Business criticality becomes the deciding factor:  
“What must remain human-in-the-loop?”

“What can be automated end-to-end?”  

Well-designed governance enables scale. Poor governance kills it.  

How to select a lighthouse use case for quick value and easy adoption  

A great lighthouse project has three characteristics:  

1. Clear boundaries: the business process is simple and well understood.  

2. Measurable results: time saved, cost reduced, fewer errors.  

3. Heavy manual effort: repetitive tasks where humans feel like “bio-robots”.  

These are the opportunities where agents shine immediately:  
categorisation, routing, triage, summarisation, document extraction, escalation decisions. This is where momentum comes from.  

How to build trust that drives real adoption  

Trust is not created by accuracy alone. Users trust AI when:  

• they understand its limitations  

• champions are available to advise and mentor  

• they see a clear audit trail of what the agent did and why  

• their data and identity feel protected  

• feature requests and feedback loops visibly shape the next iteration  

Trust grows with use. Use grows with clarity. Clarity grows with good governance and good communication.  

Avoid these mistakes  

• Over-automating without understanding the process

• Building agents without guardrails  

• No single owner for the solution  

• Ignoring user needs, for example by having poor UX, unclear instructions, or wrong expectations  

• Messy data and outdated SharePoint structures  

• Not communicating early and often  

AI adoption succeeds when it is treated like product development  

Real value happens when organisations stop thinking about AI as a one-off pilot and start treating it as:  

• a managed service  

• an evolving product  

• a collaboration between humans and agents  

• an iterative improvement cycle  

The PoC is only the start. The real work and the real payoff begin with intentional adoption, strong governance, cross-functional collaboration, and continuous improvement.  

Want to move beyond experimentation and get ready for AI that drives real value? Get in touch for an AI-readiness workshop.  

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