We Start With Your Problem, Not a Solution
Research consistently shows that 95% of AI pilots fail to deliver measurable value. The reason isn't the technology. It's the approach. Most initiatives start with a solution looking for a problem. We do the opposite.
Our methodology is built on a simple idea: your organisation already knows its problems better than anyone. You can describe the friction, the wasted time, the bottlenecks, and the missed opportunities with real evidence. Our job is to help you structure that knowledge into something testable, so every investment decision is grounded in data rather than assumption.
We call this the First Principles Innovation Framework. It draws on established methodologies from lean startup thinking, product risk management, and innovation science, adapted for organisations exploring how AI can improve their business.
The core belief behind everything we do: the risks associated with any initiative exist whether or not you invest in finding them. Our job is to help you surface those risks early, before they surface themselves at a much higher cost.
Six Steps, One Goal: Clarity Before Commitment
The framework moves through six stages. Each one is designed to build your confidence that the next step is worth taking. At every point, you'll have the evidence to make an informed decision, including the decision to stop and redirect resources to where they'll have more impact.
Problem Definition
We work with you to clearly articulate the problem from the perspective of the people experiencing it. A good problem definition answers six questions: Who has the problem? What is it? What negative impact does it cause? How frequently does it happen? What is the root cause? And what outcome would you see if it were solved?
This step is more important than it sounds. A well-defined problem becomes the foundation everything else is built on. If the problem can only be described as "we need [some tool]," it's not ready yet, and that's okay. We'll help you get there.
Stakeholder Alignment
We identify the key people whose support the initiative needs: the person responsible for ROI (will this justify the investment?), the person responsible for operations (will this actually make things better?), and the person responsible for risk (can this be done safely and sustainably?).
Sometimes one person wears multiple hats. What matters is that we understand what each perspective needs to see in order to support moving forward.
Readiness Assessment
Not everyone in an organisation experiences change the same way. Some stakeholders are eager to try new approaches. Others are comfortable with how things work today. Neither is wrong, but understanding where people stand helps us anticipate what kind of support the initiative will need to succeed internally.
Gap Analysis
We assess two things: how important is the outcome you're trying to achieve, and how satisfied are you with how things work today? A genuine opportunity for improvement exists when something is critically important and the current state is clearly falling short.
If the gap isn't there, if the current approach is actually working well enough, that's a valuable finding. It means your resources are better spent elsewhere, and we'll help you identify where.
Risk Mapping
We look at four types of risk that can derail any initiative: Is the problem real and urgent enough? (Value) Can a solution actually be built or sourced? (Feasibility) Will people actually adopt it? (Usability) Will it deliver enough improvement to sustain? (Viability)
We identify which of these risks, if they turned out to be wrong, would make the initiative not worth pursuing. Those are the ones we test first.
Experimentation
We run focused, time-boxed sprints to test the assumptions that carry the most risk. Each sprint produces real data, not opinions, that inform whether to continue, adjust course, or redirect investment.
This cycle repeats. Each round of experimentation builds on the last, progressively reducing uncertainty until you have the evidence and confidence to move into full implementation.
Four Questions That Guide Every Decision
Every initiative faces risk in four areas. We assess these together to understand where the real uncertainties are and which ones matter most to address first.
Value
Is this problem urgent enough to justify the investment? Is there a real gap between where you are and where you need to be?
Viability
If a solution works, will it deliver enough improvement to sustain long-term adoption and justify continued use?
Feasibility
Can a solution realistically be built, sourced, or configured with the resources and talent available?
Usability
Will the people involved actually stop using the current approach and adopt the new one?
The top half is about your organisation and whether the appetite and alignment for change exist. The bottom half is about the solution itself and whether it can be built and adopted. We always address value first. If the problem isn't urgent enough, exploring solutions is premature.
How We Test Before We Build
Instead of planning everything upfront and hoping it works, we work in short, focused sprints, typically two weeks, where we test specific assumptions and collect real data.
A sprint might include interviews with your team, a technical proof of concept, a prototype of a proposed workflow, or structured research into how a process currently works across departments. The activities depend on what risks need testing.
Every activity in a sprint records four things:
- What problem it supports
- What assumption we're testing
- What we expected to find
- What we actually found
These findings become the inputs for the next sprint. Over time, uncertainty decreases, confidence increases, and the path forward becomes clearer with each round.
A decision to stop or change direction is a successful outcome. If the data shows that an assumption doesn't hold, that finding just saved your organisation from a much larger investment in something that wouldn't have worked. The framework is designed to find that out early.
What Working Together Looks Like
This is a collaborative process. We bring the methodology and facilitation. You bring the deep knowledge of your business, your processes, and your people. Here's what that looks like in practice.
From Volta
Structured problem discovery and validation. Access to vetted builders for technical implementation. Project management and experiment design. Frameworks, documentation, and best practices. Documented learnings and outcomes from your project.
From You
Visibility into relevant business processes. Time from key stakeholders for interviews and reviews. Baseline data so we can measure improvement. Honest answers about what's working and what isn't. A willingness to let the evidence guide decisions.
How We Measure Success
Before any intervention, we establish baseline metrics together to capture the measurable current state. We set objectives using a simple formula: verb X to Y by Z. For example: "Reduce processing time from 4 hours to 30 minutes by Q3." Everything we do is measured against that starting point.
We'll check in at regular intervals, typically at 3, 6, 12, and 18 months, to track how things are progressing.
How Long Does It Take?
Engagements typically move through three phases: Research (defining and validating the problem), Planning (setting objectives, mapping requirements), and Execution (running sprints against the highest-priority risks). The timeline depends on the complexity of the problem and the pace at which your organisation can participate.
Artifacts That Outlast the Engagement
As we work together, we produce documentation that your organisation keeps. These aren't just reports. They're working tools that support ongoing decision-making, change management, and future planning.
Problem Statement
A clear, evidence-based articulation of the problem, its impact, root cause, and desired outcome.
Stakeholder Map
Key people, their roles, motivations, and what they need to see for the initiative to succeed.
Current State Documentation
A detailed view of existing processes, technologies, and workflows that serves as the starting point for improvement.
Objectives & Key Results
Measurable targets tied to the problem, with clear baselines so progress is visible and verifiable.
Prioritised Requirements
What needs to be solved, ranked by importance, ensuring nothing critical is overlooked in the first version.
Solution Hypothesis & Prototypes
A proposed approach that emerged from the evidence, with prototypes or process designs you can evaluate.
Risk Register
A living tracker of identified risks, their severity, who owns them, and what's being done about them.
Experiment Findings
The results of every test we ran: what we assumed, what we found, and what it means for next steps.
Documented Learnings
Interview transcripts, process maps, research findings, and all supporting evidence generated during the work.
All intellectual property developed specifically for you remains yours. You keep everything we build together.
This Approach Works Best When...
This framework isn't the right fit for every situation, and we'd rather be upfront about that. Here's what makes for a strong engagement and what might signal a different approach would serve you better.
Strong Fit
Might Not Be the Right Time
None of the "might not be right" items are permanent. If the timing isn't right today, we're happy to revisit when circumstances change. The framework will be here when you're ready.
Proven Methodologies
Our framework isn't invented from scratch. It draws on decades of established thinking from innovation, product development, and organisational change:
- The Lean Startup (Eric Ries): hypothesis-driven development and learning through experimentation
- Inspired / Empowered (Marty Cagan): the four-risk model for evaluating product and organisational risk
- The Lean Product Playbook (Dan Olsen): quantifying the gap between importance and satisfaction
- Diffusion of Innovations (Everett Rogers): understanding how people adopt new ideas and tools
- Jobs to Be Done (Clayton Christensen): framing needs as jobs to solve, not features to build
- OKRs (John Doerr / Andy Grove): measurable objectives that keep everyone aligned
We've combined these into a practical framework designed specifically for organisations exploring how AI can help them improve. The methodology is structured enough to be rigorous, but flexible enough to adapt to your context.