AI Adoption Phase

COPILOT INSIGHT SERIES #5

Take Copilot from 'let's try it' to 'we rely on it'

In the last post, I covered considerations for partners and their customers during early-stage Generative AI pilot projects.

So, where to once those clients have taken their first steps, experimented, learned, and seen early wins in targeted areas?

The next challenge for partners is how to turn isolated successes into a fully integrated AI capability that drives measurable outcomes across the business.

Navigating from pilots through to the adoption phase in the AI journey is not without some tricky twists and turns. However, when done well, it’s the turning point for turning sentiment around AI from “something we’re willing to try” to “something we absolutely rely on.”

As we go through this phase, you’ll note there are repeating themes around data readiness, information management, governance and so on. In the video and supporting key points below, I’ll aim to explain how these areas are stepped up when clients move into Copilot adoption.

Key Points

The matrix below was presented at a Crayon Connect partner summit event last year.

As with the table included for the pilot phase in the previous post, this also remains a sound guide on what comes into play during in the Copilot adoption stage. The extent to which this is true depends on your client’s business; size, maturity, industry and need. However, what is included here is based on observable activity in the IBRS client base and from our own research findings.

What evolves in the adoption phase?

The adoption phase moves proven pilot projects into wider deployment within the business. The focus is on expanding scope, refining integration, and ensuring users actually adopt the tools.

For partners, the emphasis shifts from pilot project delivery to creating measurable new capabilities, efficiencies, or innovations. During this phase, benefits should start to emerge, although they may still be uneven or limited to certain areas.

For customers, value in this phase is proven by bridging delivery to resulting benefits. If pilots are about proving the technology works, adoption is about confirming tangible operational improvements.

• AI is starting to be embedded into key workflows, but still in a targeted or department-level way.
• Partners are helping customers scale from isolated successes to organisation-wide uptake.
• The emphasis is on training, governance frameworks, and building trust so AI becomes part of the day-to-day toolkit.
• AI performance optimisation is happening, but often ad hoc, triggered by specific needs or projects.
• Many workflows still require human initiation (e.g., prompting Copilot, triggering a process).

In short, adoption is about expanding usage and embedding AI into business process and culture, but it hasn’t yet become a business utility.

Moving from Prompts to Process.

After a pilot, it’s not unusual to find businesses will still treat AI like an assistant they “ask” for help, rather than a system that can operate in the background to streamline work end-to-end.

The adoption phase is about embedding AI into the fabric of business operations so that it delivers results without users having to think about it. That means moving from occasional prompts to AI-driven processes that are consistent, repeatable, and measurable.

Partner Plays:

Embed AI directly into core systems like Dynamics 365, Business Central, or other business applications so that insights and recommendations appear where people already work.
Why: In-context AI saves time, reduces switching between apps, and increases adoption by making AI support feel seamless.

Develop reusable AI agents that automate routine decisions or surface relevant data at key points in a process.
Why: Reusable agents reduce project costs for customers and create repeatable service revenue for partners.

Use the Power Platform and Azure automation to link AI outputs to downstream actions — reducing the need for manual intervention.
Why: Connecting AI directly to action shortens process cycles and turns insights into measurable results.

Architect Multimodal AI Ecosystems

In the next phase of adoption, the most effective AI deployments will combine multiple models, each performing specialised tasks in a coordinated way.

This “multimodal” approach can deliver more accurate insights, richer outputs, and greater business value than relying on one model alone. Partners have an opportunity to design these interconnected systems for SMB customers, ensuring each AI tool plays to its strengths.

Partner plays:

Design multimodal AI architectures that make the most of Azure AI services, Copilot, and other specialist models.
Why: Multi-model designs combine strengths and minimise weaknesses, leading to more accurate and versatile AI-driven outcomes.

Map the right tools to each function from recommendation engines to generative summarisation, ensuring they operate as a coordinated system.
Why: Tool-to-task alignment avoids over-engineering and ensures each AI investment delivers targeted business value.

Provide reference architectures and best-practise deployment patterns for SMB-scale environments.
Why: Proven patterns reduce risk, speed up deployment, and give SMBs confidence in their AI roadmap.

Operationalise AI with MLOps and LLMOps

Getting AI up and running is one thing. Keeping it tuned, relevant, and aligned to business objectives is another.

As adoption deepens, SMBs will need help managing AI like any other critical business system. This means defined processes for training, monitoring, and improving models over time, and is where where MLOps and LLMOps come in.

Partner plays:

MLOps and LLMOps services to manage the lifecycle of AI models.
Why: Proactive management keeps AI relevant, secure, and delivering consistent performance over time.

Build consultative engagements that clarify what business value will be measured, not just model accuracy.
Why: Focusing on measurable value helps customers justify investment and prioritise projects that impact the bottom line.

Create feedback loops between AI outputs and business outcomes, ensuring continuous improvement.
Why: Closing the loop ensures AI learns from real-world performance and stays aligned with changing priorities.

Elevate Data Readiness into Continuous Data Health

In the pilot stage, the goal was to prepare data for AI to work at all. In the adoption stage, the challenge shifts to keeping data healthy — monitoring quality, ensuring relevance, and maintaining compliance. Data is no longer a “one and done” task; it becomes an ongoing discipline.

Partner plays:

Provide data auditing services to monitor the health and reliability of data feeding AI systems.
Why: Poor data quality leads to poor AI outputs, undermining trust and adoption.

Implement Microsoft Purview to enforce data governance policies and control sensitive information.
Why: Governance safeguards compliance and ensures data is used responsibly and securely.

Deploy Retrieval-Augmented Generation (RAG) for secure, context-aware AI interactions with customer-approved datasets.
Why: RAG improves AI relevance and accuracy while keeping responses within trusted data boundaries.

Build AI-Ready Cultures

Even the most advanced AI won’t deliver its full potential if employees don’t know how to use it — or can’t see where it could make a difference.

Cultural change is the unsung hero of AI adoption. Partners who help build AI litreacy and foster innovation will drive deeper and more sustainable adoption.

Partner plays:

Run experiential AI workshops where teams solve real business challenges using Copilot and related tools.
Why: Hands-on experience changes perceptions and builds confidence in using AI day-to-day.

Establish innovation programmes that capture frontline ideas for new AI use cases.
Why: The best AI ideas often come from those closest to the work, not the IT team.

Create role-specific training to ensure adoption is relevant to daily work, not just a generic skillset.
Why: Tailored training increases relevance, speeds up adoption, and embeds AI into routine activities.

Govern AI Use at Scale

When AI is embedded across an organisation, governance is no longer optional. It becomes a critical framework for protecting data, ensuring compliance, and maintaining trust.

Partners can help customers establish these guardrails — and keep them effective over time.

Partner plays:

Develop and implement AI governance frameworks that cover responsible use, ethical considerations, and regulatory compliance.
Why: Clear rules prevent misuse, reduce legal risk, and protect brand reputation.

Offer independent AI audits to verify that outputs remain accurate, secure, and aligned with policy.
Why: Independent checks build trust with customers, boards, and regulators.

Advise on privileged access management for AI tools, especially those handling sensitive or regulated data.
Why: Controlled access limits insider risk and protects critical business information.

Secure Against AI-Driven Threats

As AI capabilities expand, so does the potential for misuse. Cybercriminals are already exploiting AI to automate attacks, create deepfakes, and weaponise stolen data. In the adoption stage, security needs to evolve in step with AI.

Partner plays:

Deliver AI-powered cyber defence services capable of countering AI-enabled attacks.
Why: Defending AI with AI keeps pace with rapidly evolving threats.

Monitor for data leakage and IP misuse, including derivative content generation.
Why: Early detection of leaks prevents reputational damage and financial loss.

Integrate AI output monitoring into existing security operations to detect anomalies and non-compliant behaviour.
Why: Continuous monitoring ensures AI remains a safe, trusted part of business operations.

Trusted Guidance Wins the Long Game

I’ve mentioned in previous posts that AI adoption isn’t a one-off project — it’s a kickstart on a journey of digital uplift for systems and process.

Partners who can bridge the gap between technology, process, and people will:

• Lead AI strategy conversations, not just react to them.
• Offer lifecycle services that keep AI aligned to business value.
• Build lasting trust as both an innovator and a safeguard.

If you helped customers start their AI journey, you can help them turn it into a lasting competitive advantage. Success in the adoption phase can lead to AI becoming industrialised within a business, and I’ll be drilling into that in the next episode!

Up Next!

AI at Scale: Industrialising the Copilot Opportunity.