AI process improvement case study
Building Leanable: an AI-led process improvement platform.
How Changeable designed and built Leanable (formerly Midshift), a governed AI platform that runs stakeholder interviews, analyses current-state processes and delivers a professional improvement report and SOP, without the cost or timeline of a traditional consulting engagement.
Project overview
The same quality of analysis a consultant delivers, without the six-week wait.
Process improvement work has traditionally meant booking a consultant, scheduling weeks of stakeholder interviews, and waiting on a report that arrives long after the urgency that triggered the engagement has passed. Changeable built Leanable to compress that timeline without cutting the rigour: structured stakeholder interviews, evidence-based opportunity analysis and a professional deliverable, completed in days.
The platform runs a defined process improvement methodology end to end, using conversational AI to conduct interviews and analyse transcripts, with a senior practitioner reviewing and approving the output at every critical stage rather than a fully automated black box.
Human-gated automation
AI drives the analysis; a senior practitioner reviews and approves before anything reaches a client.
Full methodology, not a chatbot
A structured nine-stage engagement, not a single conversational feature bolted onto a form.
Evidence-based recommendations
Opportunity analysis draws on real market benchmarks, not generic templates.
Rebuilt when the name got in the way
Rebranded mid-build after discovering the original name was already dominated in search by an unrelated competitor.
The problem
Traditional process improvement is slow, expensive and inconsistent.
Most small and mid-sized organisations know a process is broken, but fixing it properly means booking a business analyst or consultant, coordinating interviews across a busy team, and waiting weeks for a report. The cost puts the exercise out of reach for many businesses that would benefit most.
Even where budget exists, quality varies. Findings depend heavily on which consultant ran the engagement, how many stakeholders they managed to interview, and how much of the analysis was templated rather than specific to the business.
What most businesses experience
- A process everyone agrees is broken, but nobody owns fixing it
- Weeks of scheduling friction just to get stakeholders interviewed
- A final report that arrives after the urgency has passed
- Recommendations that feel generic rather than specific to the business
- No clear next step from insight to actual automation or change
The methodology
A nine-stage engagement, run by AI and reviewed by a senior practitioner
Leanable’s engagement model mirrors how a skilled business analyst would actually run a process improvement project, structured into defined stages with clear human review gates.
Organisational onboarding
The client uploads existing documentation and answers deep-context questions so the AI understands the business before any process work begins.
Project & stakeholder setup
The client defines the process in scope, names a process owner and registers the stakeholders who need to be interviewed.
Stakeholder interviews
Each stakeholder is invited to a private, conversational AI interview that adapts its questions based on what has already been said.
Current state analysis
Transcripts are analysed into a structured process step register and pain point list, reviewed and approved by a senior practitioner before proceeding.
Opportunity identification
Improvement opportunities are identified using real market research, not generic templates, then reviewed for feasibility.
Validation session
Findings are put back to stakeholders in plain language for confirmation or correction before the future state is designed.
Future state design
A revised process is designed, with every change documented and justified against the original pain points.
Automation assessment
Each future-state step is assessed for automation potential, with confidence levels and realistic effort estimates.
Report and handover
A professional report and standard operating procedure are generated, reviewed by a senior practitioner, then released to the client.
How it was built
A phased build, one working capability at a time
Rather than design the full platform upfront, Leanable was built and tested capability by capability, with each stage running end to end before the next was layered on.
Start with the hardest part: a real AI-led interview
The riskiest capability was also the most important: an AI-led interview that felt like a conversation with a skilled analyst, not a form with extra steps. Authentication, database structure and document-upload context were built first, so the AI could read a client’s existing documentation before the conversation began and focus its questions on the gaps. Early testing with a real business document confirmed the approach worked: the AI extracted the organisational profile automatically and only asked about what was genuinely missing.
Connect interviews to evidence-based recommendations
With interviews working, the build moved through current-state analysis, opportunity identification using live market research, a stakeholder validation step, future-state design and an automation feasibility assessment. Each stage was tested end to end with real transcripts before being connected to the next, so the full chain, from stakeholder interview through to automation recommendation, could be validated in a single pass before any client used it.
Keep a senior practitioner in the loop, not just the AI
A fully automated report was never the goal. Explicit approval gates were built at the stages that matter most: after current-state analysis, after opportunity identification, and before the final report and SOP are released. A practitioner reviews the AI’s output, edits inline where needed, and only then approves the engagement to move forward. This is the same discipline Changeable brings to client AI governance work: automation accelerates the analysis, but a person remains accountable for what reaches the client.
Turn structured data into a professional, branded deliverable
A structured process register is not a deliverable a business owner can act on. A dedicated report-generation pipeline was built to turn the approved analysis into a branded PDF report and a matching standard operating procedure, alongside an SLA-tracked admin dashboard so every engagement’s review status is visible at a glance. Client feedback during testing surfaced a real UX gap: process owners had nothing tangible to see between the interviews and the final report. An interim findings summary, sent automatically once interviews were complete, was added to keep stakeholders engaged through the analysis phase.
Discover a naming conflict, and fix it before launch cost more
The product launched and was tested under its original name before a UK-based competitor was found to already dominate search results for that exact term. Rather than compete for visibility against an established brand with a similar offering, the product was renamed to Leanable, a name with clear search headroom that also fits naturally alongside the Changeable brand family. The website, application, email infrastructure and cross-brand links were rebuilt under the new name and domain, with the existing colour palette carried across so the visual identity stayed consistent through the change.
The AI agents
Conversational interviews, not a survey with an AI label on it.
The core of Leanable is a conversational AI that conducts each stakeholder interview individually, adapting its follow-up questions based on what the stakeholder has already said rather than working through a fixed script.
During testing, the interview AI referenced a detail a stakeholder had mentioned earlier in the same conversation and followed up on it unprompted, the kind of adaptive probing a skilled interviewer does naturally and a static form cannot replicate. The same underlying approach powers the opportunity analysis stage, where the AI draws on current market research rather than a fixed library of generic recommendations.
Website and branding
A SaaS front end built to rank, an app built to work
The marketing site and the working application were deliberately split, each optimised for what it actually needs to do.
Marketing site for discoverability
The public-facing website was built separately from the application, giving full control over page speed, meta data and content for organic search performance.
Application built to work, not rank
The client-facing app sits on its own subdomain, focused entirely on a fast, reliable engagement experience rather than search visibility.
A distinct visual identity
A dedicated colour palette and typography system were developed to give the product its own professional, SaaS-appropriate identity within the wider Changeable family.
Consistent through the rebrand
When the product was renamed, the existing colour palette and design language carried across so the visual identity remained continuous for anyone who had already seen it.
Documented brand guidelines
A formal brand guidelines document covering logo use, colour, typography, imagery and tone of voice was produced to keep every future touchpoint consistent.
Cross-linked into the ecosystem
The site is cross-linked with Changeable and the wider brand family, positioning Leanable as part of a connected set of practical AI products.
Results
What the finished platform delivers
Once the full methodology chain was validated end to end, testing against a real engagement produced measurable results.
Engagement turnaround
A full stakeholder-interview-to-report cycle runs in days rather than the weeks a traditional consulting engagement typically takes.
Annual admin time identified
A test engagement identified several hundred hours of annual administrative time available for reduction through the recommended automation.
Admin burden reduction potential
The automation assessment stage identified a substantial potential reduction in administrative burden for the process under review.
Enforced at every tier limit
Stakeholder and access limits are enforced at both the interface and the database level, so a restriction cannot be bypassed by editing the page.
Generated per completed engagement
Each completed engagement produces a full report, SOP and supporting documents, gated behind practitioner approval before release.
Continuity through the rebrand
The underlying platform, methodology and client experience carried through the name change without disruption to how the product works.
Questions
Questions about how Leanable was built?
Common questions about the approach behind Leanable’s AI-led process improvement methodology.
Why was the interview stage built first?
It was the riskiest and most important capability. If a conversational AI interview could not genuinely replicate the adaptive quality of a skilled analyst’s questions, the rest of the platform would not be worth building. Proving that first shaped every decision that followed.
Does the platform replace a human consultant entirely?
No. AI drives the interviews and analysis, but a senior practitioner reviews and approves the output at named gates before it reaches a client. The platform accelerates the work; it does not remove human accountability for the recommendations.
Why was the product renamed partway through the build?
The original name was already dominated in search results by an unrelated UK-based competitor with a similar product. Rather than compete for visibility under a name that would always be second in search, the product was renamed to something with clear headroom and a natural fit with the wider brand family.
How does the platform keep recommendations from feeling generic?
The opportunity analysis stage uses live market research rather than a fixed library of templated suggestions, and a stakeholder validation step puts findings back to real people for confirmation before the future state is finalised.
Why split the marketing website from the application?
The marketing site needs full control over meta data, content and page speed to perform in organic search. The application needs to work reliably for a paying client. Splitting them let each be optimised for what it actually needs to do.
What happens if the AI gets part of the analysis wrong?
The practitioner review stage exists for exactly this. Findings are editable inline before approval, and if an error is systemic rather than a small correction, the relevant analysis stage can be re-run before proceeding.
Can Changeable build something similar for our organisation?
Yes. Leanable reflects the same use case-led, governed approach Changeable applies in client engagements, including use case development, AI app development and AI governance.
Want to see what Leanable finds in your process?
Explore Leanable directly, or talk to Changeable about applying the same phased, governed approach to your own AI product or workflow.