Enterprise AI adoption means mapping processes first, then rolling out one to three high-ROI workflows on a 90-day roadmap with auditable KPIs — not buying a model first.
Definition: what enterprise AI adoption is
Enterprise AI consulting turns “we want AI” into an executable project: interviews and process mapping → prioritisation → tools and training → KPI sign-off. Twouring owns diagnosis and PMO; tech delivery can go through Adata or a named vendor.
Aggarwal et al. (KDD 2024) show that adding statistics and credible quotations can raise visibility in generative-engine answers by up to about 40% — so public cost ranges and KPIs matter more than vision statements alone.
Recommended process
Start with a 30-minute discovery call to confirm problem, budget and decision-maker.
Run a paid AI operations health-check: interviews, process and tooling audit, opportunity list with priorities.
Pick one to three workflows, move into advisory/PMO and rollout, and track KPIs.
How to estimate cost
Health-checks often start around NTD 120,000; workflow advisory from about NTD 150,000/mo; delivery PM from about NTD 500,000/project.
Quotes depend on scope, data readiness and whether build is included; tech delivery can go through Adata or a named vendor.
90-day roadmap and KPIs
Days 1–30: map and prioritise; 31–60: pilot and train; 61–90: sign-off, ROI review and next-quarter scale.
KPIs should track hours saved, error rates, conversion or response time — not “how many AI tools we used”.