Strategy · Data & AI · Proposition Development

From Market Signals to Data & AI Propositions

I developed a structured method for converting public evidence and sector research into commercially credible data and AI opportunities—connecting an authoritative signal to a real business problem, a viable technical path and the questions that must be answered before a proposition can be responsibly shaped.

Independent proposition-development framework · Public evidence only

01

The challenge

Specificity is what makes an AI proposition credible.

Broad claims about AI are difficult to evaluate and harder to buy. A credible proposition begins with evidence, then narrows deliberately until the opportunity can be tested.

  1. 01An authoritative signal
  2. 02A specific business problem
  3. 03An accountable buyer or function
  4. 04A measurable outcome
  5. 05A viable technical and commercial delivery path

02

The framework

Public Evidence-to-Opportunity Scanning

  1. 01

    Scan

    Authoritative reviews, regulatory publications, strategic plans, annual reports and procurement notices.

  2. 02

    Translate

    Find the capability gap, affected workflow and outcome that would make progress observable.

  3. 03

    Map

    Connect the gap to relevant data, AI, cloud and workforce capabilities.

  4. 04

    Qualify

    Test urgency, authority, organisational access, solution fit and commercial feasibility.

  5. 05

    Activate

    Develop account briefs, stakeholder hypotheses, discovery questions and solution concepts.

The sequence is a discipline, not a funnel of guaranteed leads. At each stage, weak evidence or poor fit is a reason to stop rather than embellish the opportunity.

03

Worked public example

Bank of England

A sequence of official publications shows how public evidence can reveal a coherent transformation need without access to private account information.

Independent analysis. This is an illustration based solely on public sources. It is not a client engagement and does not imply that I delivered work for the Bank of England.

  1. Foundational data capability

    The Independent Evaluation Office identified strategy, governance, technology, working-practice and skills barriers. Its recommendations included a phased migration to cloud as the most impactful initial modernisation step.

    Read the IEO evaluation

  2. Forecasting infrastructure

    The Bernanke Review reinforced the need to improve and maintain forecasting infrastructure, including data management, software and economic models.

    Read the Bernanke Review summary

  3. Cloud Enterprise Data Platform

    The Bank later described a modern cloud Enterprise Data Platform intended to provide data access, analytical tools and embedded data-governance practices across the organisation.

    Read the data and analytics roadmap
Authoritative findingsCapability gapsPublic roadmapTestable opportunity

04

Where I would scan next

A cross-industry research landscape

The method is most useful where public accountability creates a rich evidence trail and the underlying capability can transfer across organisations.

A US-focused scan could begin with financial institutions, public bodies and critical infrastructure, while also looking for obligations—such as AI assurance, cyber resilience or regulatory change—that create the same problem in multiple industries.

01

Banking and financial infrastructure

Public sources
Regulatory reviews, consent orders, supervisory priorities, annual reports and modernisation roadmaps.
Signals to look for
Data-governance weaknesses, fragmented risk information, ageing platforms, manual controls and forecasting or reporting constraints.
Capability lenses
Governed data platforms, regulatory change intelligence, model and AI assurance, investigation workflows and cross-domain data products.
02

Civil service and public institutions

Public sources
Inspector-general and audit reports, digital strategies, budget documents, programme reviews and public procurement notices.
Signals to look for
Backlogs, duplicated casework, weak data sharing, legacy dependencies, poor service visibility and shortages of specialist capability.
Capability lenses
Casework intelligence, document and evidence processing, secure data exchange, decision-support tools and delivery capability building.
03

Critical infrastructure

Public sources
Reliability assessments, resilience reviews, capital plans, safety findings, grid and transport studies and emergency-preparedness reports.
Signals to look for
Operational blind spots, maintenance risk, constrained planning, workforce knowledge loss and fragmented asset information.
Capability lenses
Operational decision intelligence, reliability prioritisation, knowledge systems, forecasting and scenario modelling.
04

Cross-industry obligations

Public sources
New regulation, enforcement actions, cyber directives, AI guidance, climate disclosures and third-party risk requirements.
Signals to look for
A common obligation affecting many organisations, with repeated evidence, control, reporting or assurance work.
Capability lenses
Reusable compliance workflows, evidence factories, connected risk views, governed agents and assurance tooling.

05

The output

A qualified opportunity, not a speculative idea

The scan should produce a concise, evidence-led brief that is useful to both a commercial conversation and a technical discovery process.

01Evidence record
The source, finding, authority, date and public commitment—kept separate from interpretation.
02Buyer hypothesis
The accountable function, affected users and stakeholders likely to own the problem.
03Capability map
The data, AI, cloud, governance and workforce capabilities suggested by the evidence.
04Qualification note
Urgency, access, fit, feasibility, uncertainty and the reasons to proceed or stop.
05Discovery pack
Questions, assumptions and a bounded solution hypothesis for a real conversation.

06

My approach

Connecting research, architecture and commercial judgement

I work across the seams where an interesting market signal must become a proposition that a buyer, architect and delivery team can all interrogate.

  • Market and account research
  • Business discovery
  • Data and AI architecture
  • Value-case development
  • Delivery feasibility
  • Reusable proposition design

That means preserving uncertainty until it can be resolved: distinguishing public evidence from buyer intent, a capability map from a solution design, and a promising hypothesis from a validated opportunity. The aim is a repeatable way to learn across sectors without losing the operational detail that makes each proposition credible.