A consistent starting point for AI business-model conversations
AI Risks combines public company research with sector-benchmarked analysis to help you decide which accounts deserve further attention. It supports judgement. It does not replace due diligence.
Assess a Company FreeHow an assessment is produced
Research the company and its public context
AI Risks uses the company name, website and available public information to understand its business model, sector and visible operating context.
Compare the company with its sector
The assessment applies a sector baseline before reporting risk, opportunity and readiness signals for the individual company.
Explain the findings
The result includes scores, written reasoning and source links. Use it to prepare better questions and decide where a deeper conversation is warranted.
What AI Risks measures
AI Risks assesses how artificial intelligence may affect a company’s business model, competitive position, operating approach and potential opportunities. It is a structured starting point for business conversations, not a prediction of what will happen.
AI disruption risk is about potential pressure from changing capabilities, customer expectations, competitors and delivery models. It is distinct from cybersecurity, regulatory, governance or model risk that may arise when an organisation uses AI systems.
Understanding the scores
AI opportunity
Opportunity is scored from 0 to 10. A higher score indicates greater visible potential for AI or automation to improve the business. The assessment considers broad evidence such as operational efficiency, customer and revenue opportunities, competitive position, and the availability of relevant solutions. It does not expose proprietary prompts or confidential weighting logic.
AI disruption risk
Disruption risk is reported across 3, 5, 7 and 10-year horizons. A higher risk score indicates greater potential pressure on the company’s business model, margins, competitive position or customer expectations at that horizon. It is a company-level decision-support signal, not a forecast, guarantee or instruction to act.
AI resilience
Resilience describes expected resistance to AI-driven disruption. A higher resilience score means stronger expected resistance at the relevant horizon. Resilience contributes to the company risk assessment, but it is not presented as interchangeable with company risk or assumed to be a simple mathematical inverse of it.
AI readiness
Readiness represents visible indications of how prepared a company may be to adopt and benefit from AI. Public information cannot fully show internal data quality, systems, skills, budgets, leadership intent, governance or delivery capability, so readiness should be treated as an informed starting point for discovery rather than a complete audit.
Sector and macro benchmarks
Industry-sector baselines describe patterns within a more specific industry. Macro-sector baselines provide a broader comparison across related industries. They are separate populations and should not be described as one count.
A company is mapped to the most relevant available sector using its stated business context and public information. The relevant baseline provides context, while company-specific research qualifies how closely that context fits the individual company.
The benchmark library is periodically reviewed as AI capabilities, markets and business models evolve. New baseline versions are maintained separately, and the active version supplies the public benchmark counts and current sector context.
The current benchmark library contains 131 industry-sector baselines and 14 higher-level macro-sector baselines. These are separate benchmark populations, giving 145 entries in total.
Company research and time horizons
Company-specific research
Public information about the company modifies or qualifies the relevant sector baseline. The resulting report combines the baseline with company context rather than assuming that every company in an industry faces the same exposure or opportunity.
3, 5, 7 and 10-year horizons
Multiple horizons separate nearer-term pressures from longer-term possibilities. Longer horizons allow more time for capabilities, markets and company strategies to change, so uncertainty generally increases as the horizon extends.
Sources, evidence and limitations
Assessments use publicly available company information and provide source links alongside written reasoning. Sources are considered in relation to the company and question being assessed, with attention to apparent relevance and recency. Where public evidence conflicts or is incomplete, the result should be treated as qualified rather than certain.
Use an assessment before a sales meeting, client review, proposal, account plan or portfolio discussion. It cannot verify internal data quality, operating processes, governance, budgets, leadership intent or the details of a company’s current technology estate.
A score is not a forecast or an instruction to act. Read the written rationale and sources alongside it, validate important conclusions with stakeholders and current internal evidence, and combine the findings with professional judgement and deeper due diligence.
How the methodology is maintained
AI capabilities, market conditions and business models change. Benchmarks and supporting assumptions are therefore periodically reviewed and updated when a new active baseline is available.
Updates improve the starting context, but they do not remove the need to review company-specific evidence. The methodology and benchmark populations should be read together when interpreting a report.
Use the findings responsibly
Choose the right starting point
Start with risk when you need to understand business-model pressure, opportunity when you need to prioritise potential value, or readiness when you need to guide a discovery conversation.