Consulting & strategy
AI Strategy for Businesses: Prioritising Use Cases and Avoiding Pilot Purgatory
An AI strategy prioritises use cases against data readiness and risk, so pilots actually reach production.
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Short answer
An AI strategy is a short list of prioritised use cases, backed by an honest assessment of data readiness and risk, with a realistic roadmap for moving from pilot to production. Without it, businesses run multiple disconnected AI pilots that never scale — often called pilot purgatory. B2Social helps prioritise use cases, assess what data and governance are actually in place, and set a roadmap that avoids that trap.
What you need to know
Most businesses do not lack AI ideas; they lack a way to choose between them. A strategy session should rank potential use cases by expected value and feasibility, then check each against the data and systems actually available. A use case that sounds valuable but depends on data that is scattered across unconnected systems will stall regardless of how good the underlying model is.
Risk and governance need attention before a pilot starts, not after. This includes who is accountable for AI-assisted decisions, what happens when the system is wrong, and where human review is required. Where the EU AI Act applies to a use case — for example high-risk applications — that needs to be factored into the roadmap early, not treated as a compliance afterthought discovered during rollout.
Pilot purgatory happens when a proof of concept proves a model can technically work but no one planned for production: ownership, monitoring, integration with existing systems, and a path to measurable value. A workable strategy treats the pilot as one step in a sequence toward production, with clear criteria for what justifies moving to the next stage.
Practical examples
Prioritising competing use cases
A business with five potential AI projects ranks them by data availability and expected impact, and commits to the two that are both feasible and valuable rather than spreading effort thin.
Data readiness check
Before building a customer-service AI assistant, checking whether support tickets are structured and accessible enough to ground answers, instead of assuming they are.
Governance before rollout
Defining who reviews AI-generated recommendations before they reach a customer, and under what conditions a human must intervene.
Moving past a stalled pilot
A proof of concept that worked technically but was never adopted gets re-scoped with clear ownership and integration requirements before being tried again.
How to make the right choice
Value and feasibility ranking
Are use cases ranked by both expected value and realistic feasibility, rather than by novelty or internal enthusiasm?
Data readiness
Has someone actually checked whether the data needed for a use case exists, is accessible, and is clean enough to use?
Governance and accountability
Is it clear who is accountable for AI-assisted decisions and where human review is required, including any EU AI Act considerations that apply?
Path to production
Does the roadmap define what a pilot needs to demonstrate to justify moving to production, rather than ending in an open-ended trial?
Independence of advice
Is the strategy being shaped by someone with an interest in a specific tool or vendor, or by an independent assessment of what fits your situation?
When this is not the right choice
- If your business has no reliable data foundation at all, strategy work should focus on that first, not on selecting AI use cases.
- A strategy document does not implement anything by itself; it needs follow-through and ownership to produce value.
- For a single, simple, well-understood use case, a full strategy exercise may be unnecessary overhead compared to just building it.
How B2Social approaches it
- 01
Map use cases
We gather potential AI use cases across the business and rank them by expected value and feasibility.
- 02
Assess data and risk
We check data readiness for the top candidates and identify governance or compliance considerations that apply.
- 03
Set the roadmap
We sequence use cases into a realistic plan, with clear criteria for moving each from pilot to production.
- 04
Support execution
We support implementation of priority use cases, adjusting the roadmap as results and constraints become clearer.
Frequently asked questions
What is pilot purgatory?
It is when AI pilots repeatedly prove technical feasibility but never reach production, because no one planned for ownership, integration or a path to measurable value from the start.
How do you prioritise AI use cases?
By ranking them on expected value and realistic feasibility, including whether the data needed actually exists and is usable, rather than picking the most exciting idea first.
Does the EU AI Act affect our AI strategy?
It can, depending on the use case — particularly for higher-risk applications. Where it applies, governance and compliance considerations should be built into the roadmap early rather than addressed after a pilot is built.
How long should an AI strategy take to produce?
It depends on the number of use cases and how much data assessment is needed, but the output should be a concrete, prioritised roadmap rather than a lengthy report with no sequencing.
Does B2Social also implement the use cases it prioritises?
Yes, B2Social can support implementation of the priority use cases identified in the strategy, rather than only producing recommendations.
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