01 / About
A structured diagnostic for real automation decisions.
A technical diagnostic, before the sales narrative
Can I Hire an AI? helps teams evaluate whether a role is meaningfully automatable before vendor promises, implementation plans, or transformation language take over the conversation.
Image 01
Context first, narrative second.
The analysis separates context modelling, task review, economic projection, and regulatory screening into distinct stages so the output reflects the declared operating environment — not a generic AI-readiness score. It frames feasibility, likely economic impact, and where human judgment still matters.
02 / Method
A multi-stage system, not one giant prompt
The role brief is normalised into an enriched profile, specialist nodes run in parallel, and only then is the result assembled. Financials stay deterministic and visible instead of being hidden inside generated prose.
Context-aware profile
Role, country, sector, salary, and workload are treated as one working brief that informs every downstream step.
Parallel specialist nodes
Task decomposition, regulatory screening, solution design, and rollout planning run as separate reasoning steps before synthesis.
Deterministic financials
Savings, ROI, and payback use a fixed model. Inputs and assumptions remain inspectable in the result view.
Pipeline
01
Role brief
Role, task mix, country, sector, and operating notes.
02
Input validation
Low-signal submissions filtered before the graph runs.
03
Context enrichment
Normalised with sector benchmarks and jurisdiction context.
04
Specialist review
Tasks, regulation, solution design, and rollout run concurrently.
05
Economic model
Savings, ROI, and payback computed with a fixed formula.
06
Technical synthesis
Scores, risks, outputs, and implementation shape assembled.
Where we try to be different
A headline percentage with little supporting structure
Task-level scoring with explicit reasoning and responsibility split
One generic narrative covers everything
Context, regulation, economics, and rollout are analysed separately
Sector and country applied as labels, not as interpretive constraints
The role is interpreted inside its actual operating environment
Financial projections embedded in the sales narrative
The economic model is kept separate from the narrative layer
03 / Outputs
Three outputs, one coherent readout
The result helps an operations or leadership team decide whether a workflow deserves deeper investigation — not to close an implementation deal on the spot.
Score & breakdown
Task-level automation potential with explicit reasoning and a human or AI responsibility split.
Limit — Not a pass/fail verdict or a single generic percentage with no context.
Economic projection
A deterministic savings, ROI, and payback view based on the inputs you declare.
Limit — Not a guaranteed financial outcome or a vendor quote dressed as analysis.
Implementation outline
A high-level rollout shape with effort signals, governance flags, and practical constraints.
Limit — Not a statement of work, procurement document, or full deployment plan.
04 / Trust & limits
Clear about the method, clear about the boundaries
What is structured method, what is data handling policy, and what remains outside scope — stated plainly.
Method
Structured outputs before synthesis
LLM steps produce schema-shaped outputs for task analysis, regulation, solution design, and rollout planning before they are merged.
Economics
Fixed financial model
Savings, ROI, and payback are computed month by month over 36 months using a non-linear adoption ramp. Only ~20% of the theoretical productivity value materialises in months 1–6, rising to ~50% at month 12 and ~80% at month 24, stabilising at ~90% in steady state. The ramp is deliberately conservative — real-world adoption often runs faster for well-scoped implementations. Year-1 ROI and steady-state ROI are reported separately so the estimate reflects both cautious early adoption and mature operation. The formula is deterministic and inspectable — not invented inside generated prose.
Data
No model training on submissions
Inputs, lead details, and generated results are not used to train models. Data is processed via Azure OpenAI under a signed Data Processing Agreement.
Context
Regulatory screening is contextual
The analysis considers the declared role, country, and sector instead of applying compliance language as a generic label.
Scope & limits
What this diagnostic does
Estimate task-level automation potential, surface implementation complexity, and present a deterministic economic model for the declared context.
What this diagnostic does not do
Evaluate individual employees, recommend dismissals, replace legal or HR advice, or guarantee financial outcomes.
What remains a limitation
Benchmarks and regulatory references reflect model knowledge rather than live market data — they should inform decisions, not replace review.
This diagnostic informs decisions; it does not replace legal, HR, or compliance advice. The regulatory screening surfaces known frameworks rather than issuing a legal opinion.
Run a structured diagnostic on a role you are considering.
Get a task-by-task automation view, a deterministic financial model, and an implementation outline in about a minute.