AI-curious revenue leaders.
You have invested in AI tools. Usage is high. But the number hasn't moved. You're either under pressure to show ROI on AI spend, or you're about to make more AI investments and want to be sure they land differently this time.
Questions this answers
- What is our AI actually doing for revenue?
- Which tools are producing value and which are shelfware?
- How much of our AI spend is waste and where should we reallocate?
- What does a credible AI strategy for sales actually look like?
- Is our AI accelerating conversations or generating noise our buyers ignore?
At the end of this sprint, you will have a clear, financially quantified picture of what is happening and why.
- Exactly which tools are producing pipeline and which are being ignored
- Your AI Maturity Index score benchmarked against comparable organisations
- A clear financial view of AI ROI and where reallocation creates upside
- A risk register covering data exposure, brand risk, and over-automation
- An AI strategy roadmap your board will take seriously
How we measure it.
Every Revenue Sprint runs on four proprietary instruments. This sprint deploys the highlighted set.
Quality and quantity of commercial conversations, benchmarked against the Supero dataset.
How much of reported pipeline is real, qualified, and progressing versus noise.
Multi-dimension maturity score for AI adoption, effectiveness, and strategic alignment.
Revenue at risk, cost of inaction, and realistic recovery potential over 12 to 24 months.
Every dimension of the system.
We work across both the commercial and technical layers. Alex leads the revenue and conversation assessment; Cumai leads the systems, AI, and data layer. The findings converge into one quantified picture.
- AI strategy alignment to GTM motion
- Full tool inventory: sanctioned, shadow, and ignored
- Usage vs. adoption: bought but unused, used but ineffective
- AI output quality and personalisation vs. generic noise
- Integration architecture and CRM data flow
- Data quality and AI readiness
- Prompt maturity across the revenue team
- AI coaching and call review infrastructure
- Risk and governance: data privacy and brand exposure
- AI in deal progression and forecasting
- ROI framework linking AI usage to pipeline and revenue
- Quick wins vs. strategic investments
Outputs you can act on.
Not a document that sits on a shelf. A quantified picture of the problem, a financial case for action, and a prioritised plan your leadership team can use.
AI Maturity Index
A benchmarked, multi-dimension maturity score across strategy, adoption, effectiveness, and governance.
Tool Effectiveness Scorecard
Which tools are creating conversations and opportunities versus those generating waste or risk.
Financial Impact Model
What share of AI spend is producing value, what is wasted, and where reallocation creates upside.
AI Strategy Roadmap
A prioritised path from current state to a credible AI model aligned to your revenue motion.
Quick Wins vs Strategic Investments
Immediate actions with near-term impact separated from longer-term structural changes.
Risk Register
Data privacy exposure, brand risk, and over-automation risks identified and prioritised.
14 Tools. 3 Producing Value.
A mid-market technology company had invested heavily in AI across its revenue motion: prospecting tools, content generation, call intelligence, CRM enrichment, forecasting, and coaching platforms. The board asked: “What is our AI strategy for sales?” The CRO could not answer with confidence. Usage dashboards showed adoption. Revenue had not moved.
What the diagnostic uncovered
- 14 AI tools were deployed across the revenue team. Only 3 were producing measurable pipeline impact.
- £180K in annual AI spend was generating no attributable revenue. Tools bought, installed, and either ignored or misused.
- The AI Maturity Index scored the organisation at 2.1 out of 5. Tools were present but operated as isolated islands.
- Prompt maturity was low: teams were experimenting randomly rather than using structured frameworks connected to commercial conversations.
- Call intelligence was recording everything but coaching nothing. Data captured, never acted upon.
- Two tools were creating compliance risk through uncontrolled data sharing with third-party LLMs.
What the client’s leadership team implemented
- Retired 6 tools immediately, saving £95K annually with zero impact on performance.
- Connected the 3 high-performing tools to CRM and coaching workflows so output drove action rather than sitting in dashboards.
- Implemented prompt frameworks and data governance policy across all AI usage.
- Adopted the AI Maturity Index as a quarterly benchmark, tracking progress, not just adoption.
- Shifted call intelligence from passive recording to structured coaching with AI-assisted review.
Results the organisation achieved over 6 months
- AI spend reduced by 40% while measurable AI-driven pipeline contribution increased.
- AI Maturity Index moved from 2.1 to 3.4 within two quarters.
- Coaching cadence improved from ad hoc to weekly structured review.
- The CRO presented the board with a credible AI strategy for the first time, backed by data, not aspiration.
“We thought we had an AI strategy. The diagnostic showed us we had an AI problem disguised as progress. We made the changes ourselves. Supero gave us the map. Now we know exactly what’s working and what we’ve stopped wasting money on.”
Director of Digital Transformation, National Investment Authority
Minimal disruption. Maximum signal.
We observe, interrogate, and test. We do not derail the business with large workshops or lengthy questionnaires.
Scope
A working session to define your AI stack, strategic priorities, and where the diagnostic will focus.
Assess
2 to 4 weeks of structured assessment: tool inventory, usage interviews, output quality review, data and architecture audit.
Quantify
Scores, financial models, and a clear picture of what is working, what is waste, and what is recoverable.
Deliver
Joint executive readout. Cumai leads the systems and AI findings. Alex leads the commercial and revenue narrative. Together we present the financial implications.
Pricing: We work on a base-plus-outcomes model. A meaningful share of our upside is linked to agreed KPIs, because pricing should reflect impact, not time spent. The diagnostic method, once designed, can be re-run annually as a longitudinal measure of progress.
Three Revenue Sprints. One diagnostic engine.
Every Supero engagement runs on the same diagnostic spine. Start where the problem is most visible.
Half your team hits target. Half doesn't.
A focused diagnostic scoped to one team. Find out why performance is inconsistent, what it's costing per quarter, and what can shift in 30 to 90 days.
Explore this sprint Full Revenue SprintSomething is broken in your revenue system. Where exactly?
A comprehensive assessment of your entire revenue system. Strategy through execution. Conversations, systems, AI, and financial model.
Explore this sprintFind where revenue is leaking and quantify the cost before you decide what to do next.
Supero helps B2B revenue leaders diagnose where performance is breaking down and where to focus first.
Every conversation is confidential, sprint-led, and independent of any vendor, methodology, or tool.