AI Strategy and AutomationProposed offering

Find the AI opportunities worth funding before you spend on the ones that are not.

We map your workflows, test your data and score every idea on value, feasibility and risk, so leadership gets a ranked backlog with a business case, not another list of possibilities.

Explore an AI Opportunity See the assessment flow

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

Assessment pipeline

Workflow discovery
Data audit
Feasibility tests
Scoring and ROI
Ranked roadmap

Reference design. Components are options, not a statement of what is deployed at any customer.

Problems this service is built to solve

These are situations we expect buyers to recognise. Each one states why it happens, what it costs and how we would approach it.

AI spending without a measurable business case

Three departments have each bought an AI licence. Marketing uses it for drafts, support trialled a bot, and finance runs a pilot with an analytics vendor. Nobody can say which of the three changed a number the board cares about.

Why it happens: Tools are bought against vendor demos, not against a baseline of how the work is done today. Without a baseline there is nothing to compare the result against.
What it costs: Licences renew on momentum, pilots stall without a decision, and the next AI request is judged on enthusiasm instead of evidence.
How we approach it: We time the current workflow, count volumes and error rates, then model the improvement for each candidate with explicit assumptions you can challenge. Every opportunity gets a value range, a cost range and a payback estimate.
What to measure: Baseline cycle time and cost per transaction, modelled benefit range, and the percentage of AI spend tied to a named business metric.

Disconnected and inconsistent enterprise data

A claims team wants an assistant that answers policy questions. The policy wording lives in PDFs on a shared drive, the endorsements in a legacy system, and the exceptions in an email thread that only two people can find.

Why it happens: Systems grew separately. Identifiers differ between them, documents have no owner, and nobody tracks which version is current.
What it costs: A model trained or grounded on stale or contradictory sources gives confident wrong answers, and the project is blamed instead of the data.
How we approach it: We sample the real sources a use case would depend on, check coverage, freshness, duplicates, access rules and identifier consistency, and score data readiness per use case rather than for the enterprise as a whole.
What to measure: Share of required sources that are reachable, current and permissioned, duplicate rate, and the time to answer a set of known test questions from existing data.

Competing AI ideas without prioritization

The leadership offsite produced forty ideas. The loudest sponsor gets the first budget, and engineering is asked to start before anyone has checked whether the required data or integrations exist.

Why it happens: Ideas arrive without a common scoring method, so selection becomes a negotiation about who is senior.
What it costs: The team starts a hard, low value project, burns a quarter, and loses leadership trust in the whole programme.
How we approach it: We score each idea on value, data readiness, integration effort, risk and time to first result, then sequence them so early wins fund and de-risk the harder work. The scoring sheet is yours and can be rerun.
What to measure: Number of ideas reduced to a funded shortlist, time from idea to decision, and share of the shortlist reaching a pilot.

Security and governance uncertainty

Legal asks whether customer records can be sent to an external model. IT is unsure which teams already paste data into public tools. The answer today is a policy document nobody has operationalised.

Why it happens: AI adoption moved faster than data classification, vendor review and approval paths.
What it costs: Projects freeze at the review stage, or worse, ship without review and create an incident that stops everything.
How we approach it: We classify the data each use case touches, map it against your regulatory and contractual obligations, and recommend a deployment pattern for each: managed API, private endpoint, or in-boundary hosting. We flag where human review is mandatory.
What to measure: Percentage of use cases with a documented data classification and approved deployment pattern, and time taken to clear a security review.

Solutions we engineer

Concrete capabilities, each with the need it serves, how it integrates, what you receive and the value to expect.

Workflow discovery workshops and process mapping

Structured sessions with the people who do the work, backed by observed samples of real cases, produce a step level map with volumes, delays and exception paths.

Customer need: You cannot automate a process nobody has written down.
Integration: Access to ticketing, workflow and system logs where available to validate what people report.
Deliverable: Process maps and a quantified pain point register.
Business value: A factual baseline for every later business case.

Data readiness assessment

Sampling and profiling of the actual sources: coverage, freshness, quality, duplication, access control and format variety.

Customer need: Know before you commit whether the data can support the use case.
Integration: Read access to representative samples from document stores, databases and SaaS exports.
Deliverable: Per use case readiness scorecard with remediation actions.
Business value: Avoids funding projects that fail on data, not on AI.

Feasibility spikes

Small, time boxed experiments run on your sample data against a labelled test set, so accuracy is measured instead of assumed.

Customer need: Replace opinion with evidence on whether a model can do the task well enough.
Integration: A sandboxed model endpoint and de-identified samples.
Deliverable: Spike report with measured accuracy, failure modes and an estimate of the production gap.
Business value: Cuts the cost of discovering a bad idea.

Opportunity scoring and prioritization

A transparent weighted model across value, readiness, effort, risk and strategic fit, with sensitivity checks on the weights.

Customer need: Turn many ideas into a short, defensible list.
Integration: Stakeholder input captured in a shared workbook.
Deliverable: Ranked opportunity backlog and the scoring workbook.
Business value: Faster, less political funding decisions.

ROI and cost modelling

Models that include build cost, run cost such as inference and monitoring, change effort and benefit ranges, with the assumptions listed beside every figure.

Customer need: Finance needs numbers it can challenge.
Integration: Cost data from finance and current vendor pricing.
Deliverable: Business case model for each shortlisted use case.
Business value: Credible payback and a clear view of ongoing cost.

Governance and risk review

A data classification, deployment pattern and human oversight recommendation for each use case, using NIST AI RMF and OWASP LLM risks as reference checklists.

Customer need: Clear a path through security, legal and compliance.
Integration: Your security, privacy and procurement teams.
Deliverable: Risk register and recommended control set.
Business value: Shorter approvals and fewer late surprises.

How we solve it

A delivery sequence that includes model selection, evaluation, data governance and human oversight.

Frame the decision

Agree the business outcomes that matter, who decides, and the constraints on data, budget and risk. We write down what a good result looks like before looking at tools.

Discover workflows

Interview owners and observe real cases. We capture volumes, effort, delays and the exceptions that make a process hard to automate.

Audit data and systems

Profile the sources each candidate needs, test access and permissions, and list the integrations required.

Test feasibility

Run time boxed spikes on the top candidates using a labelled sample, and record accuracy and failure modes. Model selection starts here with measured comparisons.

Score and model

Score every idea, build the ROI model and run sensitivity checks so the ranking holds when assumptions move.

Assess governance

Classify data, choose deployment patterns and define where a human must approve or review.

Present and decide

Walk leadership through a ranked roadmap, the business cases and the risks, then agree the first funded pilot.

Hand over and revisit

You keep the workbooks and maps. We schedule a reassessment so the ranking reflects new data, models and costs.

Solution in action: Choosing between three AI ideas in a mid sized insurer

Solution Concept An illustrative scenario. It describes how we would structure the work, not a delivered customer project.

Starting problem

Claims leadership has three proposals: a document extraction project, a claims chatbot for policyholders, and a fraud signal model. Budget supports one.

Existing workflow

Adjusters read incoming claim documents, retype key fields into the claims system and search shared drives for policy wording. Average handling effort is not measured today.

Improved workflow

The assessment times a sample of claims, finds that retyping and policy lookup consume the most adjuster effort, confirms the documents are readable at acceptable quality, and ranks document extraction first, with policy retrieval second and the chatbot deferred until data is cleaner.

Leadership must choose one AI investment.

Systems involved

Claims system, document store, policy administration system, email intake.

Data movement

Sample of de-identified claims documents and policy records moved to a sandbox for profiling and spikes.

Human decisions

Adjusters validate sample labels. Leadership approves the ranking and the first pilot scope.

Automation opportunities

Scoring workbook reruns when assumptions change. Profiling scripts rerun on new samples.

Exception handling

Documents below a legibility threshold are listed as a separate remediation item, not hidden in an average.

Resulting user experience

Leadership receives one page per opportunity with value range, cost range, risk rating and the evidence behind each.

KPIs to evaluate

Share of ideas with a documented baseline
Spike accuracy on the labelled sample
Time from assessment to funded pilot

What you receive

Concrete deliverables for this service, written so you can check them against the contract.

Workflow map and pain point register
Data readiness scorecard per use case
Feasibility spike report with measured accuracy
Ranked opportunity backlog and scoring workbook
ROI and run cost models with listed assumptions
Governance and risk register with deployment pattern recommendations
Roadmap for the first two pilots, with success criteria
Executive readout and a technical appendix for engineering

Technology and engineering

Options we would evaluate for this service. Unless a group is marked as publicly listed on kindlebit.com, treat each tool as a proposed implementation option. Naming a tool does not imply a vendor partnership.

Analysis and modelling

Spreadsheet and notebook based scoring models
SQL and Python profiling
Process mining exports where available

Model options for spikes (proposed implementation options)

OpenAI compatible APIs
Anthropic
Azure AI
AWS AI services
Google Cloud AI

Governance references

NIST AI Risk Management Framework
OWASP Top 10 for LLM Applications

Relevant Kindlebit work and evidence

We use the strongest evidence available and say which kind it is.

Proposed offering

Evidence status for this service

This is a service Kindlebit proposes to deliver. No named customer project is published for it on this site.

See case study status

Solution ConceptInteractive demo with simulated data

Intelligent insurance operations: choosing the first AI investment

The concept demo shows how an assessment output feeds a claims pipeline. It is a simulated demonstration with synthetic data, not a delivered customer project.

Open the demonstration

Business outcomes and success criteria

These are the measures we would agree before work starts. They are criteria for success, not results from past engagements.

Business case coverage

Share of AI initiatives with a documented baseline and value range.

Decision speed

Weeks from assessment start to a funded pilot.

Feasibility accuracy

Measured accuracy on a labelled sample against the threshold the process needs.

Approval time

Days to clear security and legal review for shortlisted use cases.

Questions buyers ask

How long does an assessment take?

A focused assessment of one department usually fits in four to six weeks. Covering several departments takes longer because workflow discovery and data access scale with the number of teams. We confirm the schedule after a short scoping call.

What access do you need to our data?

Representative, de-identified samples are enough for most assessments. We agree data handling rules first, work in an environment you approve, and delete samples at the end if you ask us to.

Will you recommend your own services?

The ranking is built from the scoring model, and you receive the workbook so you can check it. Some top ranked items will be things you should buy or configure rather than build, and we say so.

What drives the cost of an assessment?

The number of workflows in scope, how many systems hold the required data, the number of feasibility spikes, and how much stakeholder time is available. We quote a fixed scope once those are clear.

Which AI models will you test?

We test the options that fit your data classification and cloud footprint, typically a mix of managed APIs and, where required, in-boundary hosting. Model choice is made from measured results on your sample.

What if the answer is that we are not ready?

Then the report says so and lists the specific data, integration or governance work that would change the answer, with an estimate. That is a legitimate and useful result.

Related services

Industries where this applies

Find your highest value AI opportunities.

Bring us the ideas, the workflows and the doubts. We will show you which ones are worth funding and what it would take.

Start an AI opportunity conversation Explore a demonstration