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To find where AI can create value in your business, start with a measurable business problem—not a tool or technology demo. Identify the workflow and its owner, check whether the data, skills and operating conditions are suitable, compare AI with simpler alternatives, and test a bounded use case against a baseline. Scale only when the measured benefit justifies the full cost and the process can sustain the change.
Where can AI actually create value in my business?
AI is worth considering when it can improve an outcome your business already cares about: cost per transaction, time to complete a process, error or rework rates, customer wait or satisfaction, conversion, retention, or the ability to deliver a new product or service. Choose one outcome and name the person accountable for it, along with the people who perform the work.
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Then map the current process before proposing a system. Look for repetitive or information-heavy tasks, bottlenecks, and work involving finding, drafting, sorting, predicting, classifying or interpreting information. These are discovery prompts, not proof that AI will help. Describe what would change in the workflow and compare it with process redesign, conventional automation or leaving the process alone. AI is not necessarily the best answer to every business problem.
How do I assess whether my business is ready for AI?
Readiness is more than giving staff access to an AI tool. A useful check is whether the business can identify a suitable use case, assess available solutions, access data appropriate to the task, evaluate outputs, and make the workflow and organizational changes needed to use the system. The OECD, BCG and INSEAD describe firms as moving through activities such as identifying use cases, evaluating pre-trained solutions and planning implementation. This is a lens for choosing an appropriate next step, not a mandatory maturity ladder or a requirement to build a custom model. OECD/BCG/INSEAD, The Adoption of Artificial Intelligence in Firms (2025)
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- Process ownership: Is someone responsible for the outcome and able to change the process?
- Data fit: Is the relevant data accessible, sufficiently reliable and suitable for the task? The OECD/BCG/INSEAD report states: “High-quality and sufficiently voluminous data are essential to create, test, evaluate and validate AI models.” The amount and quality required depend on what the system is expected to do. OECD report chapter on data and AI policymaking
- People and skills: Can staff use, check and appropriately challenge the system’s output?
- Workflow and integration: Can the system fit into the tools and steps people already use, or is a broader change needed?
- Evaluation and risk: Can you detect errors that matter and decide what happens when the system fails?
- Economics: Can you estimate implementation, integration, data preparation, oversight and recurring costs—not just the purchase price?
The OECD identifies uncertain returns, limited skills, data maturity and underestimated changes to workplace practices as barriers firms report. Gathering reliable data also has a cost, which belongs in the business case. OECD, The Adoption of Artificial Intelligence in Firms (2025), chapter on support institutions OECD report chapter on data and AI policymaking
Small and medium-sized businesses can use the OECD SME AI Readiness Tool as a prompt for discussion. The OECD describes it as a pilot for G7 SMEs; its results are indicative, not an official OECD assessment or endorsement.
How do I prioritize AI use cases?
Compare candidates using the same questions rather than choosing the one with the most impressive demo. The dimensions below are a practical synthesis, not a validated scoring system: do not assume universal weights or cutoffs. A small, measurable use case with an owner and a credible path into daily work can be a better starting point than a sweeping transformation with uncertain benefits.
| Dimension | Question to ask |
|---|---|
| Expected impact | Which business outcome could improve, and by how much might that matter? |
| Evidence confidence | What supports the estimate: a measured baseline, a small test, or an untested assumption? |
| Feasibility and data | Are the process, data and evaluation methods available for this task? |
| Risk | What are the consequences of a wrong, delayed or inconsistent output, and how will people catch it? |
| Implementation and change | What integration, training, workflow redesign and oversight would adoption require? |
| Ongoing cost | What will deployment, data maintenance, review and operation cost over time? |
| Time to learn and measurability | How soon can the team observe meaningful results, and can it distinguish them from other changes? |
Also decide what kind of solution fits the use case: an existing ready-made product, a solution that integrates a pre-trained model, or a custom capability. The right choice depends on business needs and capabilities; building a custom model is not the default destination.
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How can I measure AI ROI?
Set the baseline and define what would count as success before deployment. For each measure, record the current value, target, data source, measurement window and accountable owner. A useful chain connects the system’s performance to adoption, operational results, strategic outcomes and financial impact. McKinsey presents this as a five-layer measurement framework; it is a practitioner framework, not a regulatory standard. Choose measures that fit the use case rather than tracking every possible metric. McKinsey, “The five-layer AI measurement framework”
- Technical performance: Does the system perform the intended task with acceptable quality, reliability, latency and cost? Track relevant failure modes.
- Adoption: Are the intended people using it in the workflow? Depending on the case, track frequency, acceptance, overrides or edits.
- Operational outcomes: Has the process changed in measures such as cycle time, defects, rework, cost per case, abandonment or first-contact resolution?
- Strategic outcomes: Is there an effect on a customer outcome, delivery performance, retention, compliance or another business-unit goal?
- Financial impact: What is the effect on revenue, margin or cost to serve after accounting for total cost of ownership?
Usage or system health alone does not establish business value. The measures need to connect: for example, increased use matters financially only if it improves a meaningful outcome enough to justify the costs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should I test a use case and decide whether to scale?
Treat a pilot as a test of a business hypothesis, not a showcase. Write down the expected change, the measures that will test it, the costs to include and how the team will handle quality or safety failures. Where appropriate, use a comparison group, an A/B test or a staggered rollout to help separate the system’s effect from background changes. The design should suit the workflow and the risks; not every business can or should use the same experiment.
- Set the decision rule: Agree in advance what evidence would support continuing, revising or stopping.
- Run the bounded test: Track technical performance, adoption, business measures and full costs over the agreed window.
- Review attribution and failures: Check whether the observed change plausibly followed from the intervention, and examine errors, overrides and unintended effects.
- Make the next decision: Continue or revise if the evidence is incomplete but the question remains answerable; stop if the case fails its agreed criteria. Scale only when results support the business case and the workflow can sustain adoption.
McKinsey recommends defining value and metrics up front, building attribution into rollout where possible, and reviewing benefits alongside total cost of ownership before scaling. McKinsey measurement framework article
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How do I evaluate AI risks and performance for the intended use?
Test the system in the context where it will actually be used: with the relevant users, task, data and consequences of failure. Define what counts as an unacceptable output, how it will be detected, who can intervene and what happens when the system cannot be trusted. NIST describes testing, evaluation, verification and validation (TEVV) as ways to produce evidence that AI can meet organizational goals while minimizing negative impacts. Its TEVV-Athlon framework is intended to support customized assessments; it is not a one-size-fits-all method for calculating business ROI. NIST, “The TEVV-Athlon Framework for Evaluating AI Systems”
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