Why AI Adoption is Your Next Growth Opportunity

For Managed Service Providers, AI represents more than just another technology trend to
support. It’s a transformational shift in how your clients operate, and they’re looking for
partners who can guide them through this complexity with confidence and expertise.

The opportunity is substantial. Organisations are eager to adopt AI but struggle with
implementation. They face unclear strategies, difficulty assessing readiness, high
consultancy fees, and the very real risk of wasted investment. This is where MSPs who
position themselves as trusted AI adoption partners can create exceptional value and build
long-term, strategic relationships.

The challenge? Most MSPs know their clients need AI, but lack a structured methodology to
deliver consistent, measurable success. This guide shows you how to bridge that gap using
proven frameworks and best practices, with WaiFinder Adoption as your delivery platform.

The Trust Equation: From Service Provider to Strategic Partner

Customer success in AI adoption isn’t about deploying technology—it’s about transforming
your relationship from tactical service provider to strategic business partner.

What Clients Really Need from You

Your clients aren’t looking for another vendor. They need a trusted partner who:

• Understands their business outcomes, not just their IT infrastructure
• Reduces their risk by identifying and avoiding common pitfalls before they happen
• Delivers measurable value quickly to maintain executive support
• Provides transparency throughout the journey with clear milestones and expectations
• Builds their capability to sustain AI initiatives long after the initial engagement

The Trust Framework

Building trust requires demonstrating four key capabilities:

1. Strategic Insight
   Show you understand AI as a business transformation, not a technology project. Frame
   every conversation around outcomes, ROI, and competitive advantage.
2. Structured Methodology
   Bring a repeatable, proven process that clients can see and understand. Ad-hoc approaches
   create anxiety; structured frameworks build confidence.
3. Risk Management
   Proactively identify potential failures and create mitigation plans. Clients trust partners who
   surface problems early, not those who hide them.
4. Accountability
   Tie your success to their success with clear KPIs, regular reporting, and transparent
   governance. Own the outcomes, not just the activities.

The Four-Stage Framework for AI Adoption Success

WaiFinder Adoption provides a proven, structured approach built on Microsoft’s BXT
(Business-Experience-Technology) framework. Understanding and delivering each stage
effectively is crucial for customer success.

Stage 1: Assess – Establish the Baseline

What It Is
A comprehensive evaluation of your client’s current AI maturity, culture readiness, data
landscape, and strategic objectives.

Why It Matters
You can’t improve what you don’t measure. The assessment creates a shared understanding
between you and your client about where they are and what needs to change.

Best Practices

• Involve stakeholders across business and IT, not just the technology team
• Use objective, standardised assessment tools to ensure consistency
• Document both strengths and gaps transparently
• Link findings directly to business impact (e.g., “data quality issues will delay customer
• segmentation by 6 months”)
• Create a heat map showing readiness across different dimensions

Common Pitfalls to Avoid

• Rushing the assessment to get to “the build” faster
• Only assessing technical capability while ignoring culture and process
• Presenting findings without clear implications for the business
• Using the assessment as a sales tool rather than a diagnostic

Your Deliverable
A comprehensive AI Readiness Assessment report showing maturity metrics across data,
process, people, and technology, with specific examples and business context.

Stage 2: Improve – Build Strong Foundations

What It Is
Targeted improvements to strengthen the capabilities required for successful AI adoption—
data quality, governance frameworks, skills development, and cultural readiness.

Why It Matters
AI projects fail most often due to weak foundations, not poor algorithms. Investing time here
dramatically increases the success rate of later initiatives.

Best Practices

• Prioritise improvements based on which AI use cases they unlock
• Run improvement initiatives in parallel with early, low-risk AI pilots to maintain
• momentum
• Create cross-functional working groups to break down silos
• Establish governance frameworks before they’re needed, not as an afterthought
• Use quick wins to demonstrate progress and build organisational confidence

Common Pitfalls to Avoid

• Treating improvement as a separate, endless project that delays AI value
• Focusing only on technology improvements while neglecting process and people
• Creating governance that’s so heavy it slows everything down
• Failing to communicate why improvement work matters to business stakeholders

Your Deliverable
A prioritised Improvement Plan with specific action items, ownership, timelines, and clear
links to the AI use cases they enable.

Stage 3: Identify – Prioritise with Precision

What It Is
Systematic evaluation and prioritisation of AI use cases using the BXT framework, scoring
each against business impact, organisational fit, and technical feasibility.

Why It Matters
This is where strategic alignment happens. Poor prioritisation leads to pilot fatigue, wasted
resources, and failed initiatives that damage confidence in AI.

Best Practices

• Workshop use cases with business stakeholders, not just IT
• Score every use case across Business value, Experience improvement, and Technical
• feasibility
• Consider both strategic importance and execution readiness
• Create a portfolio view showing quick wins, strategic bets, and longer-term
• opportunities
• Gain executive sponsorship for the prioritised roadmap before build begins
• Document why certain use cases weren’t prioritised (for transparency)

Common Pitfalls to Avoid

• Prioritising based on “what’s technically cool” rather than business value
• Choosing only safe, incremental use cases that don’t drive transformation
• Ignoring organisational readiness (building solutions no one is ready to adopt)
• Failing to align the roadmap with budget cycles and strategic planning
• Creating too many simultaneous initiatives and spreading resources too thin

Your Deliverable
A BXT-scored AI Roadmap with priority rankings, impact scores, milestone-based timelines,
and clear business cases for the top 3-5 initiatives.

Stage 4: Adopt – Execute and Scale

What It Is
Moving from strategy to production—launching pilots, measuring performance against KPIs,
scaling successful initiatives, and embedding AI-driven workflows into daily operations.

Why It Matters
This is where theory becomes value. Execution quality determines whether AI becomes a
business driver or another failed technology initiative.

Best Practices

• Start with a pilot that has clear success criteria and a defined timeline (typically 8-12
• weeks)
• Establish KPIs and measurement frameworks before launch, not after
• Create feedback loops with business users to identify issues early
• Build scale plans before the pilot ends (don’t treat scaling as a separate project)
• Document lessons learned and create playbooks for faster future deployments
• Celebrate and communicate wins to build organisational momentum

Common Pitfalls to Avoid

• Running pilots without clear success criteria or end dates
• Building custom solutions when platform capabilities would suffice
• Neglecting change management and user adoption planning
• Declaring success based on technical completion rather than business outcomes
• Failing to plan for ongoing support, monitoring, and optimisation

Your Deliverable
Detailed Implementation Plans with resources, KPIs, tasks, risk mitigation strategies, and
clear handover to BAU operations.

Frameworks and Best Practices for MSP Success

The BXT Framework in Practice

Microsoft’s BXT framework provides a systematic way to evaluate AI opportunities:
Business

• Does this solve a real business problem?
• What’s the measurable financial impact?
• Does it align with strategic objectives?
• Is there executive sponsorship?

Experience

• How does this improve user/customer experience?
• Will people actually use this?
• Does it reduce friction or add complexity?
• Is change management planned?

Technology

• Is the technical approach feasible?
• Do we have the required data?
• Can we build/buy/integrate this effectively?
• Does it fit our architecture and security requirements?

Use this framework consistently in client conversations to demonstrate strategic thinking
and ensure alignment across all three dimensions.

Governance: Your Competitive Advantage

Many MSPs view governance as bureaucracy. Smart MSPs see it as a competitive
advantage that protects client success.

Governance Essentials

1. Decision Rights: Who approves use cases, budgets, and changes?
2. Risk Management: How do we identify, assess, and mitigate AI-specific risks?
3. Data Stewardship: Who owns data quality, access, and compliance?
4. Performance Monitoring: What gets measured, how often, and who acts on it?
5. Ethics and Compliance: How do we ensure responsible AI practices?

Governance Best Practices

• Keep it lightweight and practical, not bureaucratic
• Build governance into delivery workflows, not as a separate process
• Use governance checkpoints to demonstrate diligence to executive stakeholders
• Document decisions and rationale for transparency and learning
• Review and adapt governance as you learn

Change Management: The Overlooked Success Factor

Technology is rarely the reason AI projects fail. People and process issues are.

Change Management Framework

1. Stakeholder Mapping: Identify who’s affected, their concerns, and their influence
2. Communication Plan: Regular, transparent updates on progress, changes, and next
   steps
3. Training and Support: Ensure users have the skills and resources to succeed
4. Feedback Mechanisms: Create safe channels for concerns and suggestions
5. Champions Network: Recruit influential early adopters to drive peer adoption

Critical Change Management Practices

• Start change management at project inception, not at deployment
• Involve end users in design and testing to build ownership
• Address the “what’s in it for me” question for every user group
• Plan for resistance and have strategies ready
• Measure adoption metrics, not just deployment completion

Common Pitfalls and How to Avoid Them

Pitfall 1: Starting with Technology Instead of Business Outcomes

The Mistake
Leading with “let’s implement AI” or “we should use Microsoft Copilot” before
understanding what business problem needs solving.

Why It Happens
MSPs are technology experts. It’s natural to lead with solutions.

The Fix
Train your team to start every engagement with business discovery. Ask: “What business
outcome are you trying to achieve?” Then map AI capabilities to those outcomes.

Pitfall 2: Underestimating the Importance of Data

The Mistake
Assuming client data is “good enough” and discovering quality issues deep into the project.

Why It Happens
Data assessment isn’t glamorous and clients often overestimate their data maturity.

The Fix
Make data assessment a non-negotiable first step. Use WaiFinder’s assessment tools to
create objective visibility into data readiness. Surface data issues early with clear business
impact.

Pitfall 3: Lack of Executive Sponsorship

The Mistake
Working only with IT teams or middle management without securing active executive
sponsorship.

Why It Happens
Executives are busy and IT teams are keen to “just get started.”

The Fix
Require executive sponsorship as a project pre-requisite. Frame AI initiatives as business
transformations that need leadership support. Create executive-friendly reporting that
shows strategic value.

Pitfall 4: Running Endless Pilots

The Mistake
Launching pilot after pilot without clear success criteria, timelines, or paths to production.

Why It Happens
Organisations fear commitment and prefer “low-risk experiments.”

The Fix
Define success criteria and timelines before the pilot starts. Build the scale plan during the
pilot, not after. Use pilot results to make firm go/no-go decisions.

Pitfall 5: Ignoring Compliance and Risk

The Mistake
Treating AI as “just another application” without considering regulatory, ethical, and security
implications.

Why It Happens
Compliance seems like a barrier to speed and innovation.

The Fix
Integrate compliance and risk assessment into your standard process. Position it as
protecting client value and reputation. Use frameworks (like those in WaiFinder Compliance
when available) to streamline the process.

Pitfall 6: Poor Communication and Expectation Management

The Mistake
Not communicating progress, challenges, or changes frequently enough, leading to surprise
and loss of trust.

Why It Happens
MSPs focus on delivery and assume good work speaks for itself.

The Fix
Over-communicate. Create regular cadences for status updates, steering committee
meetings, and stakeholder reviews. Use dashboards and visual reporting to make progress
tangible.

Building Your AI Services Practice

Positioning Your AI Offering

Don’t Position As: “We implement AI tools”

Do Position As: “We guide organisations through strategic AI adoption that delivers
measurable business outcomes”

Your Value Proposition

• Structured, repeatable methodology proven across multiple clients
• Objective assessment and transparent road-mapping
• Risk reduction through best practices and governance
• Faster time to value using proven frameworks
• Transfer of knowledge and capability to client teams

Packaging Your Services

Tier 1: AI Readiness Assessment
A fixed-scope engagement delivering comprehensive maturity assessment and
improvement plan. This is your entry point and diagnostic service.

Tier 2: AI Strategy and Roadmap
Building on the assessment, create a prioritised, BXT-scored roadmap with business cases
for the top AI initiatives.

Tier 3: Pilot Implementation
Deliver 1-3 pilots with clear success criteria, taking use cases from concept to measurable
business value.

Tier 4: Scale and Embed
Expand successful pilots across the organisation and embed into BAU operations.

Tier 5: Managed AI Services
Ongoing support, monitoring, optimisation, and expansion of the AI portfolio.

Leveraging WaiFinder as Your Delivery Platform

WaiFinder Adoption provides MSPs with a brandable, enterprise-grade platform that:

• Standardises your approach across all client engagements
• Produces professional, objective reports that build client confidence
• Accelerates delivery with pre-built assessment frameworks and templates
• Creates consistency in quality and outcomes
• Enables scaling of your AI practice without proportional headcount growth

WaiFinder Benefits for MSPs

• Reduce custom scoping and assessment work by 60-70%
• Deliver consistent, high-quality readiness assessments
• Generate professional, branded reports automatically
• Access proven BXT prioritisation methodology
• Create implementation plans with built-in best practices
• Build client confidence with structured, transparent processes

Pricing and Commercial Models

Assessment and Strategy (Fixed Fee)

• Lower risk for clients
• Predictable revenue for you
• Easy to scope and deliver
• Creates pipeline for implementation work

Implementation (Time and Materials or Fixed Fee)

• Align pricing to complexity and scope
• Include risk buffer for unknowns
• Consider success-based components for alignment

Managed Services (Recurring)

• Create annuity revenue streams
• Deepen client relationships
• Enable continuous improvement and expansion

Value-Based Pricing
For mature MSPs, consider pricing based on client outcomes (e.g., percentage of realised
savings, revenue generated). This requires strong measurement frameworks but creates
powerful alignment.

Measuring and Demonstrating Customer Success

Success in AI adoption must be measurable and communicated effectively.

Define Success Metrics Upfront

Business Metrics

• Revenue impact (increased sales, new revenue streams)
• Cost reduction (efficiency gains, automation savings)
• Customer satisfaction improvement
• Time savings (hours recovered per week/month)
• Risk reduction (compliance, security, quality improvements)

Adoption Metrics

• User adoption rates
• Usage frequency and depth
• Time to competency
• Reduction in support requests

Technical Metrics

• System performance and reliability
• Data quality improvements
• Integration success
• Technical debt reduction

Create Executive Dashboards

• Build quarterly business review materials that show:
• Progress against roadmap milestones
• Business value delivered (in financial terms where possible)
• Risk mitigation achieved
• Capability maturity improvements
• Next phase preview and business case

Case Study Development

• Document and share success stories (with client permission):
• Business challenge and context
• Approach and methodology
• Results achieved (quantified)
• Lessons learned
• Client testimonials

These become powerful sales and marketing tools for growing your practice.

The Path Forward: Building Long-Term Partnerships

AI adoption isn’t a one-time project—it’s a multi-year journey. Position yourself as the
partner who will be there throughout.

From Project to Partnership

Year 1: Assessment, foundation building, and initial pilots
Year 2: Scaling successful initiatives and expanding use cases
Year 3+: Continuous optimisation, new capability development, and strategic expansion

Continuous Value Creation

• Regular maturity re-assessments to show progress
• Quarterly roadmap reviews to adapt to changing business priorities
• Proactive identification of new opportunities
• Knowledge transfer and capability building
• Community building (user groups, lunch-and-learns, executive forums)

Expand Your Relationship

As your clients’ AI programs mature, expand your engagement:

• Compliance and Governance services (coming with WaiFinder Compliance and
• Governance)
• Advanced use cases in new business areas
• Integration with broader digital transformation initiatives
• Strategic advisory at board and executive level

Why WaiFinder is Built for MSPs

WaiFinder Adoption was designed specifically to help MSPs and consultants deliver AI
adoption services at scale.

For MSPs, WaiFinder Provides:

• A proven, repeatable framework that reduces risk
• Professional, branded outputs that build client confidence
• Accelerated delivery through automation and templates
• Consistent quality across all client engagements
• Scalability without proportional resource growth
• Competitive differentiation in a crowded market

Flexible Plans for Your Business:

• Consultant Plan (£199/month): Perfect for solo practitioners or small teams
• Enterprise/MSP Plan (£499/month): Designed for growing AI practices with multiple
• consultants
• Custom Plans (from £999/month): Branded apps, reports, and dedicated infrastructure
• for established practices

All plans include the structured Assess-Improve-Identify-Adopt framework, BXT
prioritisation methodology, and comprehensive report generation.

Getting Started: Your First 90 Days

Week 1-2: Internal Readiness

• Review WaiFinder Adoption platform and methodology
• Train your consulting team on the framework
• Develop your service offering and pricing
• Create sales and marketing materials

Week 3-4: Pilot Client Selection

• Identify 1-2 ideal clients for early engagements
• Offer discounted or pilot pricing to gain experience
• Set clear success criteria for your own learning

Week 5-8: First Assessment Delivery

• Run comprehensive readiness assessment
• Deliver improvement plan and roadmap
• Gather client feedback on process and value

Week 9-12: Refine and Scale

• Incorporate lessons learned
• Develop case study from pilot engagement
• Begin actively marketing AI adoption services
• Target 2-3 new client engagements

Conclusion: The Opportunity is Now

Organisations everywhere are eager to adopt AI but desperately need trusted partners who
can guide them through the complexity. As an MSP, you have existing client relationships,
technology expertise, and the operational capability to deliver AI adoption at scale.

What you need is a structured methodology that builds client confidence, reduces risk, and
delivers measurable outcomes consistently. That’s exactly what WaiFinder Adoption
provides.

The MSPs who move quickly to establish AI adoption capabilities will build deep, strategic
partnerships that generate recurring revenue for years to come. Those who wait will find
themselves commoditised as competitors differentiate through AI expertise.

The question isn’t whether to build AI adoption services—it’s how quickly you can get
started.

Ready to Build Your AI Adoption Practice?

Book a WaiFinder demo and discover how our structured approach, proven frameworks,
and enterprise-grade platform can help you:

• Differentiate your MSP in a competitive market
• Deliver consistent, high-quality AI adoption services
• Build trusted partnerships with strategic clients
• Create recurring revenue through managed AI services
• Scale your practice without proportional headcount growth

WaiFinder is your guide through enabling AI success—for your clients and for your business.

About WaiFinder

WaiFinder provides strategic AI adoption tools that guide businesses and consultants
through every phase of AI implementation. Built on Microsoft’s enterprise-grade platform
and leveraging the proven BXT framework, WaiFinder dramatically increases the likelihood
of successful, measurable AI deployments. Learn more at
waifinder.uk.