Methodology

How Workfutured works

Our scoring model and CV rewriting methodology is built on peer-reviewed research, leading economic models, and frontier AI capabilities.

The AI disruption scoring model

Our job disruption score draws on three foundational research frameworks that have become the industry standard for evaluating automation risk.

Frey & Osborne Task Analysis Framework (2013, Updated 2023)

Oxford researchers Carl Benedikt Frey and Michael Osborne identified nine key task types that determine automation susceptibility. Their model evaluates roles across perception, manipulation, creative intelligence, social intelligence, and cognitive non-routine dimensions. We apply this framework as our primary scoring layer.

Reference: Frey, C.B. & Osborne, M.A. (2013). The Future of Employment. Oxford Martin School.

McKinsey Global Institute Work Activity Model

McKinsey's research analyses 2,000+ work activities across 800 occupations, identifying which activities within a role are automatable versus which require uniquely human capabilities. This allows us to score roles at a task level, not just a job title level — significantly improving accuracy.

Reference: McKinsey Global Institute (2023). The Future of Work After COVID-19.

World Economic Forum Future of Jobs Index

The WEF surveys 803 companies across 27 industries in 45 economies annually. Their findings on job displacement timelines, emerging role categories, and skill demand shifts form our third scoring layer, providing real-world employer perspective alongside academic analysis.

Reference: World Economic Forum (2025). Future of Jobs Report 2025.

The five disruption dimensions

Every job is scored across five dimensions drawn from the research above. Each dimension is weighted based on current AI capability benchmarks.

Routine task density

25%

What proportion of the role involves repetitive, rule-based tasks? Higher routine density correlates strongly with automation risk. Benchmarked against O*NET occupational database task classifications.

Social & emotional complexity

25%

Does the role require empathy, negotiation, leadership, or care? These capabilities remain beyond current AI systems. Weighted heavily based on Accenture research showing emotional intelligence as the primary differentiator in human roles.

Creative & novel problem solving

20%

Is the role primarily about applying known solutions, or generating genuinely new ones? Original creative work remains a strong human advantage according to Stanford HAI benchmarks.

Physical dexterity requirements

15%

Roles requiring fine motor skills, physical presence, or real-world manipulation remain highly resistant to automation. Robotics costs and limitations make physical roles safer than equivalent cognitive ones.

AI tool leverage potential

15%

Can the role be dramatically augmented by AI rather than replaced? Workers who can direct AI tools to multiply their output occupy an increasingly premium position in the labour market.

The CV rewriting methodology

Our CV rewriting process applies a structured transformation model grounded in how hiring managers and ATS systems evaluate candidates in an AI-augmented hiring environment.

Step 1

Role decomposition

We analyse the target job title against our disruption database to identify which specific tasks within the role are most future-proof. This determines which aspects of your experience to amplify.

Step 2

Experience reframing

Your existing experience is rewritten using future-of-work language — emphasising human judgment, cross-functional collaboration, adaptive problem solving and AI tool proficiency where relevant.

Step 3

Skill signal optimisation

We surface skills in your background that signal AI resilience: mentoring, stakeholder management, creative direction, systems thinking. These are repositioned prominently to match what forward-looking employers are screening for.

Step 4

ATS alignment

Your CV is structured to pass automated hiring system filters while remaining readable to human reviewers. Section ordering, keyword density, and formatting follow current ATS best practices validated against major platforms including Workday, Greenhouse, and Lever.

Step 5

Forward-looking positioning

A custom professional summary frames you as an adaptive, future-ready professional — not just a holder of past credentials. This framing is calibrated to what the research shows high-growth employers are currently seeking.

Powered by Claude

Workfutured is powered by Claude, Anthropic's frontier AI model — consistently ranked among the top-performing large language models for reasoning, writing quality, and nuanced analysis.

We chose Claude specifically for this application because of its superior performance on long-form professional writing tasks, its ability to reason about complex career contexts, and Anthropic's focus on AI safety and accuracy — critical when people are making important career decisions based on the output.

Every CV rewrite is generated fresh from your specific experience and target role. We do not use templates or generic rewrites — each output is individually reasoned by the model against your actual career history.

Honest limitations

We believe in being transparent about what our model can and cannot do.

Disruption scores are probabilistic estimates, not certainties. Individual outcomes depend heavily on geography, industry sector, company size, and specific role responsibilities that may differ significantly from the job title alone.

AI capabilities are advancing rapidly. A role scored as lower risk today may face greater disruption within 24 months as model capabilities improve. We update our scoring model quarterly to reflect this.

Our CV rewriting improves how your experience is communicated — it cannot create experience you do not have. It is most effective when you have genuine relevant experience that is currently undersold in your existing CV.

Research references

[1]Frey, C.B. & Osborne, M.A. (2013). The Future of Employment: How Susceptible are Jobs to Computerisation? Oxford Martin School Working Paper.

[2]McKinsey Global Institute (2023). The Future of Work After COVID-19. McKinsey & Company.

[3]World Economic Forum (2025). Future of Jobs Report 2025. WEF Geneva.

[4]Goldman Sachs (2024). The Potentially Large Effects of Artificial Intelligence on Economic Growth. Goldman Sachs Global Investment Research.

[5]Accenture (2024). Work, Workforce, Workers: Reinvented. Accenture Research.

[6]Stanford Institute for Human-Centered AI (2025). AI Index Report 2025. Stanford University.

[7]Autor, D., Levy, F. & Murnane, R. (2003). The Skill Content of Recent Technological Change. Quarterly Journal of Economics.

[8]OECD (2023). Employment Outlook 2023: Artificial Intelligence and the Labour Market. OECD Publishing.

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