When capability and prices change quickly, a multi-year blueprint centred on one model or platform can lose relevance. We believe transformation should develop three assets together: a revisable task portfolio, shared capability with genuine reuse and organisational learning that survives supplier changes.
reduction in inference cost at a specified capability level1
2025 · Nov 2022–Oct 2024 · GPT-3.5-level performanceFalling inference prices repeatedly redraw the economic boundary of tasks. They do not remove the costs of process integration, human review or error handling at the same pace. We believe AI investment should move from a fixed tool list to a revisable task portfolio, reassessing automation and human control as capability, prices and consequences change.
EXAMINING AI THROUGH THE TASK
Selection before problem definition pushes future tasks into a predetermined platform. A resilient roadmap separates enduring capabilities from those that can be purchased or replaced as the market changes.
Separate approaches to knowledge, permissions and evaluation can look fast initially but create repeated maintenance at scale. Shared capability should emerge alongside demonstrated reuse.
Failure patterns, operational feedback and acceptance rules improve the next experiment. Keeping only demonstrations and closure documents forces teams to relearn the same problems.
Identify what tasks share, then order pilots, common capability and organisational adjustments. Avoid placing every foundation investment ahead of value testing.
Preserve the ability to expand, revise or stop, with evidence requirements for the next commitment. Early enthusiasm should not automatically become enduring cost.
Maintain business evaluations, knowledge standards, interface contracts and operating experience as assets that continue to matter when models change.
For stable tasks and well-validated solutions, chasing every new model may add risk. Replaceability preserves choice; it does not justify constant switching.
If the core model becomes stronger, cheaper or unavailable tomorrow, which capabilities still belong to the enterprise?
Independent pilots often duplicate procurement and knowledge preparation while making investment and results hard to compare. Organisational adoption requires shared capabilities, role changes, budget ownership and exit mechanisms alongside successful demonstrations.
Compare needs for knowledge, identity, tool use and evaluation; separate shared services from domain-specific elements and assign maintenance ownership.
Establish use-case, platform and risk accountability. Plan training, task changes and staged funding, with value owners participating in scaling decisions.
Review evaluation, adoption, service costs and incidents at stage gates. Reassess differences in each new setting and retain pause, retirement and replacement options.
AI capability map and shared-service design
Use-case governance, roles and funding model
Phased roadmap and value-review dashboard
Expansion depends on task characteristics and workforce readiness; no universal rollout speed or workforce-reduction percentage is assumed.
Comparison of inference cost at GPT-3.5-level MMLU performance; not a reduction in total enterprise AI or project costs.
Stanford 2025 · Nov 2022–Oct 2024 · GPT-3.5-level performanceOSWorld is a structured computer-use benchmark; the result is not an energy-production success rate or a deployment threshold.
Stanford AI Index 2026 · OSWorld benchmarkSelf-reported status in a cross-industry, cross-geography survey, not a measured maturity score for energy enterprises.
IBM 2025 · 1,700 senior data and analytics leaders globally