Many organisations are investing in AI tools simply because they feel they must, but too few tie their investments back to tangible business outcomes. In the UK, only a quarter of small businesses are confident about AI’s return on investment, even as two-thirds of B2B revenue leaders report first-year ROI from AI deployments 1 2. This disconnect risks turning AI from a growth enabler into an unchecked cost centre.
The ROI Gap in AI Adoption
Despite growing adoption, UK businesses still struggle to translate AI into value:
- 19% of UK and EU B2B teams see ROI within the first three months of AI deployment; another 19% within 3–6 months; and 27% between 6–12 months 2.
- 25% of UK firms cite uncertain ROI as a top barrier to scaled AI adoption, alongside high costs (30%) and lack of expertise (35%) 1.
- Over half (56%) prioritise operational efficiency with AI, yet only 40% target AI for innovation gains 1.
| Time to ROI | Percentage of Early Adopters |
|---|---|
| 0–3 months | 19% |
| 3–6 months | 19% |
| 6–12 months | 27% |
| Beyond 12 months | 35% (uncertain or longer) |
The MSP’s Dilemma: Licences vs Outcomes
Vendors push licence volumes. MSPs often follow suit, reselling seats instead of architecting solutions around the customer’s strategic goals. Key barriers undermining value delivery include:
- Data quality issues (93%) that skew AI insights and derail performance metrics 3.
- Legacy system integration hurdles (53%) that stall end-to-end automation 3.
- Skills shortages (52%) preventing MSP teams from tailoring AI to unique business contexts 3.
Without clear alignment to customer objectives, MSPs risk delivering technology for technology’s sake, rather than driving measurable ROI.
Delivering Value: A Strategic MSP Playbook
Top UK MSPs are closing the gap by embedding business goals into every stage of their AI programmes:
- Strategic Alignment: Map AI initiatives to specific KPIs—revenue growth, cost reduction, customer satisfaction, before selecting tools 4.
- Data Governance: Ensure high-quality, structured data pipelines that feed reliable AI models and support GDPR compliance 4.
- Infrastructure Readiness: Validate compute and storage capacity to handle AI workloads without service disruptions 4.
- Skill Development: Invest in upskilling or partner with specialist consultancies to supplement in-house capabilities 4.
- Performance Monitoring: Continuously measure AI outcomes against business goals and iterate on the model and process.
Top AI use cases among MSPs in the UK:
| Use Case | Adoption Rate | Business Benefit |
|---|---|---|
| IT monitoring | 66.7% | 20% reduction in downtime costs |
| Ticketing & incident mgmt | 54.4% | 30% faster resolution times |
| Automated reporting | 49.8% | 25% productivity gain in operations |
(Data for adoption rates from Lansweeper survey; benefit estimates per industry benchmarks) 3 5.
A Scalable Framework: WaiFinder Adoption
WaiFinder Adoption offers MSPs and consultants a repeatable framework that embeds strategic alignment at every step, from maturity assessment through to full AI implementation 6. Key features include:
- A structured AI maturity assessment tied to business objectives.
- A prioritisation matrix that ranks AI projects by projected ROI and strategic impact.
- A comprehensive list of KPIs for tracking AI performance metrics against customer outcomes.
By adopting WaiFinder, MSPs can:
- Deliver a consistent, repeatable approach across multiple clients.
- Demonstrate clear, quantifiable ROI from every AI engagement.
- Scale their AI practice without reinventing the wheel for each customer.
Ready to turn AI investments into real business outcomes?
Discover WaiFinder Adoption and schedule a demo to see how you can align every AI project to your client’s strategic goals and deliver measurable ROI, every time.
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