Module 8 · 95 min
AI Strategy, Portfolio & Economics
Where to Place the Bets, and Why. Opportunity and use-case prioritisation, portfolio choices, buy/build/partner decisions, vendor strategy and due diligence, total cost of ownership, ROI and value realisation, funding and operating-model choices. The central question: what should we pursue, and why?
- Frame AI strategy as a set of choices under scarcity — capital, leadership attention, integration capacity, clinical change capacity and data access — rather than as a list of technologies to adopt.
- Turn an AI idea into a strategic opportunity by attaching it to a service problem, a value hypothesis and a named route to realising the value.
- Compare candidate use cases on value potential, strategic fit and reuse, feasibility, evidence confidence and delivery burden, without collapsing them into a single misleading score.
- Use 'high value but not ready' as a deliberate portfolio position with a stated readiness gap, instead of a rejection or an indefinite pilot.
- Build a portfolio that balances near-term productivity, clinical value bets, enabling capabilities and longer-horizon options, and identify the shared capabilities that unlock several use cases at once.
- Name the full cost stack of an AI deployment — acquisition, integration, data preparation, evidence, redesign, training, oversight, infrastructure, compliance, monitoring and exit — and separate fixed from variable and one-off from recurring.
- Distinguish cashable from non-cashable benefit, and trace a claimed benefit through operational mechanism, measurable KPI, financial or clinical consequence and named owner.
- Recognise misalignment between who pays and who benefits, and say what that implies for funding and sequencing.
- Express an economic case as scenarios with stated assumptions and uncertainty rather than as a single-point return figure.
- Choose between buy, build and partner on capability, differentiation and maintenance appetite, and run a vendor diligence conversation covering lock-in, data rights, version control and exit.
- Make and defend fund, fund-conditionally, hold and stop decisions across a constrained portfolio.
Continuing from Module 7
Modules 6 and 7 answered two questions about a single system: does the evidence support the claim, and can it be operated safely? This module asks the question a board actually has to settle. Given scarce capital, scarce leadership attention and a finite amount of change a service can absorb in a year, which of the things we could do are worth doing — and in what order?
Building on Essentials
Essentials made the case for starting from the bottleneck rather than the technology, and for proving value in a small pilot. That argument is not made again. Here the subject is what a leader does once several defensible candidates compete for the same scarce attention, integration slots and clinical change capacity: prioritisation without false precision, portfolio shape and sequencing, total cost of ownership, cashable versus non-cashable benefit, buy/build/partner and vendor diligence.
Strategy starts with value, not with AI
A list headed 'AI opportunities' is a technology inventory. It answers 'where could we use this?' — a question with more answers than any organisation has capacity to pursue, and one that quietly assumes the technology is the scarce thing.
The scarce things are elsewhere. Capital is scarce, but it is rarely the binding constraint. Leadership attention is scarcer: a chief medical officer can sponsor two or three genuine changes of practice in a year, not nine. Integration capacity is scarcer still, because the same small team connects every system to the record. Clinical change capacity — the willingness and time of the people whose work actually changes — is usually the tightest constraint of all, and the one least often written into a plan. Data access sits underneath all of it.
Strategy is the discipline of allocating those scarce things deliberately rather than by whoever asked most persistently. That means starting from the service, not from the technology: which care pathways are underperforming, where does demand exceed capacity, where is quality variable, where does the workforce burn out, where does the organisation lose money doing something well?
From there, each candidate becomes a value hypothesis rather than a product. And the test that separates a serious candidate from an interesting one is a single compound question: is the value mechanism plausible, and can this organisation actually realise it? Both halves have to hold. A plausible mechanism the organisation cannot act on produces nothing; an organisation eager to act on an implausible mechanism produces a well-run disappointment.
The prioritisation dimensions, portfolio categories and value-realisation bridge in this module are teaching structures for organising a leadership conversation. They are informed by health technology assessment practice — WHO's HTA guidance, the NICE HealthTech programme manual and CHEERS-AI reporting guidance — but none of those prescribes this format, and none of the numbers used in the examples is a published finding. Completing the structures is not evidence that an investment is sound.
What is actually scarce
Capital
Licence and implementation funding, and the capital plan it competes with. Usually the most visible constraint and rarely the tightest.
Leadership attention
A named executive and clinical sponsor who will chair the reviews, arbitrate the conflicts and defend the change when it is unpopular. Cannot be spread across a dozen initiatives.
Integration & technical capacity
The same small team maintains the interfaces, identity, infrastructure and record integration for everything. Two projects wanting the same three engineers in the same quarter is a portfolio decision, not a scheduling problem.
Clinical change capacity
How much altered practice a service can absorb while still delivering care. Frequently the binding constraint, and the one most often assumed to be free.
Data access & readiness
Legal basis, extraction, mapping and quality. Module 2's readiness work is what tells you whether this constraint is weeks or quarters.
Previously, in Module 1
Module 1 introduced the value hypothesis: for whom, what changes, what value is expected, how it is measured, and what would falsify it. Strategy is what happens when you have twenty of those and can fund three. The hypothesis is the unit of comparison here, not the model or the vendor.
The four value surfaces, as investment targets
The same four surfaces introduced in Module 1 are how a portfolio conversation stays honest about what kind of value each candidate is expected to produce. A portfolio that turns out to sit entirely in one surface is a choice — it should be a deliberate one.
Earlier detection, more consistent decisions, fewer missed findings, better-targeted treatment.
- Triage of imaging worklists so time-critical studies are read sooner
- Risk stratification that directs a limited follow-up clinic to the patients most likely to benefit
Example metrics to test: Time from study to report for time-critical findings; Proportion of flagged patients receiving the intended intervention.
The same staff and estate delivering more care, or the same care with less rework and waiting.
- Drafting discharge summaries and referral letters for clinician review
- Automated coding suggestions and scheduling optimisation
Example metrics to test: Documentation minutes per encounter; Turnaround time from request to completed referral.
Care that is easier to reach and understand, and work that is less draining to do.
- Plain-language and multilingual explanations of results and instructions
- Ambient documentation that returns attention to the consultation
Example metrics to test: After-hours documentation time per clinician; Patient-reported comprehension of discharge instructions.
Faster learning from the organisation's own data and from the wider evidence base.
- Screening and extraction support for evidence reviews
- Cohort discovery for trials and service evaluation
Example metrics to test: Time to assemble an eligible cohort; Screening throughput per reviewer at a maintained recall level.
Strategic fit: five questions
Mission and stated priorities
Does this advance something the organisation has already committed to publicly — waiting times, equity of access, workforce retention, a specific pathway? If it has to be justified from scratch, it will compete badly for attention every quarter.
Scale of the problem
How many patients, encounters or staff hours does the problem touch? A perfect solution to a narrow problem can be worth less than a partial improvement to a large one.
Ability to act on the output
When the system produces its output, is there a decision, a person and the capacity to act differently? Module 3's actionability test, asked as an investment question.
Repeatability across the organisation
Does solving it once create a capability or pattern reusable in other services, or is it bespoke to one department's workflow?
Cost of not acting
What continues if nothing is done — locum spend, backlog, avoidable admissions, staff attrition? A credible counterfactual is part of the case.
An AI idea versus a strategic opportunity
The difference is not enthusiasm or technical sophistication. It is whether the proposal names the problem, the mechanism, the constraint and the measure. Both columns below describe the same underlying technology.
How it is described
- AI idea
- "We should use AI to improve outpatient letters."
- Strategic opportunity
- "Clinic letters in the two respiratory clinics take a median of four working days to reach the GP, and the backlog grows every winter."
Who it is for
- AI idea
- Clinicians, generally. Patients, indirectly.
- Strategic opportunity
- The eleven consultants and four registrars in those clinics, and the GPs waiting on the letter to change a prescription.
Mechanism of value
- AI idea
- The tool saves time.
- Strategic opportunity
- Drafting from the consultation removes the dictation-and-typing loop, so the letter is signed the same day rather than queued for secretarial capacity.
Constraint that could stop it
- AI idea
- Not stated.
- Strategic opportunity
- Secretarial capacity is already the bottleneck, so the benefit only appears if the redeployed secretarial time goes to the backlog and not to absorbing further growth silently.
How it would be judged
- AI idea
- Clinician satisfaction with the tool.
- Strategic opportunity
- Median and 90th-percentile days from clinic to letter received, against the current baseline, with letter quality sampled as a balancing measure.
Notice what the right-hand column makes possible. It can be compared with an unrelated proposal from radiology, because both are now expressed as a problem, a mechanism, a constraint and a measure. A list of technologies cannot be prioritised; a list of value hypotheses can.
It also makes the honest negative answer available. If nobody can name the constraint or the measure, the proposal is not yet ready for investment — which is different from being a bad idea, and should be recorded differently.
Sources & evidence · 4 sources
This module cites public or consensus guidance, scholarly literature, technical documentation.
Content reviewed: September 2026. Publication dates of the individual sources are shown in each citation.
World Health Organization. Health technology assessment of medical devices, 2nd ed. Geneva: WHO; 30 May 2025. ISBN 9789240110878.
WHO's practical guidance on health technology assessment as a multidisciplinary evaluation of clinical, economic, organisational, ethical and social implications, intended to support efficient allocation of resources. It frames the discipline of assessing value before adoption; it is guidance on process, not evidence about any particular technology, and it does not prescribe the prioritisation categories used in this module.
Open sourceNational Institute for Health and Care Excellence. NICE HealthTech programme manual (PMG48): early-use HealthTech guidance assessments. Published 14 July 2025; updated 17 December 2025.
Describes how NICE assesses promising health technologies where evidence is still developing — weighing unmet need, potential value for money, the nature of remaining uncertainty and what further evidence generation is required, including workforce and system efficiency impacts. Directly relevant to staged commitment and to treating a weak evidence base as a reason to size a first investment as learning. It is a UK process manual, not a general economic method.
Open sourceNational Institute for Health and Care Excellence. Introducing CHEERS-AI: improving health economic evaluation reporting for AI technologies. NICE blog, 31 October 2024.
Introduces an AI-specific extension to health economic evaluation reporting, covering transparency about input data, model functionality, conflicts of interest and the assumptions behind claimed economic effects. A reporting standard: it improves how an economic case is described and audited, and following it does not make the underlying case correct.
Open sourceBinkley CE, Bouslov D, Zaidi A, et al. An early pipeline framework for assessing vendor AI solutions to support return on investment. npj Digit Med. 2025;8:368. doi:10.1038/s41746-025-01767-z
Proposes an early-stage framework for triaging vendor AI proposals around strategic alignment, shared accountability, measurable value and risk, with an impact/value case covering the problem, the solution landscape, quantifiable objectives and costs. Supports the case for structured early triage before procurement; it is a proposed framework rather than validated evidence that using it improves investment outcomes.
Open source
Capstone Board Pack
Add what you just learned to your own strategy document while it is fresh.