Your organisation has an AI strategy. Does it have a workforce plan?

Task-level AI impact analysis assesses how artificial intelligence affects individual work tasks within a role, rather than predicting whether an entire job title will be automated. Every role is broken into its component tasks and each task is individually assessed against current and near-term AI capabilities. This produces a more accurate picture of workforce impact because two employees with identical job titles can have completely different AI exposure depending on how their role is actually structured. GoFIGR extends the International Labour Organisation task framework to classify every task across five future states, from Human Only to Fully Automated, modelled across three adoption scenarios and three time horizons.

McKinsey and WEF reports analyse at occupation level using broad industry averages. GoFIGR analysis is specific to your organisation - your actual roles, your actual tasks, your technology stack, and your AI roadmap. Two organisations with identical job titles can have completely different AI exposure depending on how those roles are structured and what tools they use. Generic reports cannot capture that. GoFIGR extends the International Labour Organisation task framework to your specific context, producing findings that are defensible in a board conversation rather than illustrative of an industry trend.

A non-PII extract from your HRIS or ATS containing role titles, job descriptions, job families, and headcount. No employee names, no personal identifiers, no performance data. You also provide strategic context: your AI roadmap, tech stack, and planned pace of adoption. GoFIGR can work with imperfect or incomplete data. A clean export is helpful but not a prerequisite.

Initial findings are typically delivered within one to three days of receiving your workforce data. From there, we work with you on scenario modelling, skills framework development, and board-ready outputs. No months of preparation, no employee surveys, no lengthy discovery process before work begins.

No. The assessment runs entirely on role-level workforce data. No employee surveys, no focus groups, no individual assessments. Employees are not contacted and do not need to be informed. Many clients deliberately complete the analysis before any internal AI communications, so they can shape the narrative from a position of knowledge rather than react to employee anxiety. Every engagement starts with a mutual NDA. Findings, scenarios, and organisational context stay confidential. Data is deleted within 30 days of completion, or earlier on request.

Automation opportunities are identified through task-level analysis that classifies each task within each role according to its susceptibility to AI automation. Tasks that are routine, rule-based, or involve processing structured information typically have the highest automation potential. The financial value of those opportunities is calculated by mapping task time allocation against role salary data. If a task represents 15 percent of a role's time and that task is fully automatable, the dollar value can be quantified across every employee in that role. Aggregated across an organisation, this produces a systematic view of efficiency gains available through AI adoption, which can be used to prioritise AI investment, build a business case for transformation, and identify where human capacity can be redeployed to higher-value work.

Pricing is scoped to your organisation - number of roles, functions covered, and the level of output and iteration required. Book an executive briefing and we will outline what an engagement for your organisation would look like and what it would cost.

You do. All findings, scenario models, skills frameworks, and raw data produced during the engagement are owned by your organisation. GoFIGR does not retain, repurpose, or use client data or outputs for any purpose beyond the engagement. Every engagement begins with a mutual NDA and data is deleted within 30 days of completion, or earlier on request.