Roadmap
Roadmap
Horizon 1 · Pilot validation window
Pilot only where workflow fit and economics can be tested.
The pilot horizon is for narrowing the problem, KPI, workflow owner, and loss/gain hypothesis before broader rollout. MIT NANDA reports enterprise GenAI investment of $30–40 billion while 95% of organizations are getting zero return, so the objective is not activity but measurable value proof.
Benchmarks
- MIT NANDA describes the enterprise-grade AI funnel as Sixty percent of organizations evaluating tools, 20 percent reaching pilot stage, and 5 percent reaching production.
- Gartner predicts at least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025 because of data quality, risk controls, escalating costs, or unclear business value.
- Gartner says business-model transformation GenAI deployments can cost $5 million to $20 million, depending on the deployment approach.
Common pitfalls
- Treating vendor demos as workflow validation.
- Starting with broad adoption announcements before defining the measurable operating KPI.
- Building internally when the organization cannot yet supply contextual learning and day-to-day workflow alignment.
Horizon 2 · Scale-up window
Scaling requires governance, workflow redesign, and data discipline.
The scale-up horizon addresses the gap between adoption and enterprise change. McKinsey reports 88 percent regular AI use in at least one business function, up from 78 percent, but says the majority remain at experimenting or piloting stages at the enterprise level.
Benchmarks
- McKinsey says nearly two-thirds of organizations have not yet begun scaling AI across the enterprise.
- Deloitte reports that more than two-thirds of leaders say 30% or fewer of their GenAI experiments will be fully scaled in its near-term scaling window.
- Deloitte reports that Sixty-nine percent of leaders say fully implementing an AI governance strategy will take over a year to resolve.
Common pitfalls
- Confusing regular use in one function with enterprise scaling.
- Scaling before governance owners, risk controls, and data stewardship are explicit.
- Assuming model improvement alone will overcome change-management bottlenecks.
Horizon 3 · Enterprise transformation window
Transformation reallocates resources toward people and process.
The transformation horizon begins only after the enterprise can repeatedly convert use cases into financial and operating impact. BCG says only 26% of companies have developed the capabilities to move beyond proofs of concept and generate tangible AI value, while the rest have yet to show tangible value.
Benchmarks
- BCG's maturity split shows 4% with cutting-edge AI capabilities across functions and 22% beginning to realize substantial gains.
- BCG says AI leaders follow the resource rule of 10% into algorithms, 20% into technology and data, and 70% into people and processes.
- BCG reports that AI leaders achieved 1.5 times higher revenue growth, 1.6 times greater shareholder returns, and 1.4 times higher returns on invested capital than other companies over the past three years.
- McKinsey reports that 64 percent of respondents say AI enables innovation, while 39 percent report enterprise-level EBIT impact from AI.
Common pitfalls
- Calling a portfolio transformed because it has many active pilots.
- Underfunding people and process redesign relative to models and tooling.
- Reporting innovation sentiment without testing enterprise-level EBIT impact.
Intro
An AI roadmap should be managed as evidence gates, not as a technology rollout. The pattern in the benchmark sources is consistent: pilots are plentiful, scaling is the bottleneck, and enterprise transformation depends on workflow, governance, and people-process redesign.
Preconditions
- For Korean export manufacturers, KITA survey evidence suggests an execution gap: only 17.9% actually use AI on the shop floor even as consensus on AI transformation spreads.
- Companies cited difficulty identifying AI adoption areas and processes (41.6%) and lack of basic data infrastructure for AI adoption (39.1%) as reasons AI adoption was delayed.
- A separate KITA seminar report says only about 17% of responding export companies actively use AI in their work, despite recognizing the need to adopt it.
- The KITA-related surveys differ in population, question wording, and timing, so they should not be read as a direct trend line.
Government Support
| Program | Verification scope | Support scale / note | URL |
|---|---|---|---|
| AI Voucher Program | NIPA program page | Provides demand-side companies vouchers of up to ₩200M to purchase AI solutions and pays suppliers for the AI solutions sold. | https://www.nipa.kr/home/bsnsAll/1/detail?bsnsDtlsIemNo=580 |
| Export Voucher Program | https://www.exportvoucher.com/ | ||
| KOTRA Trade-Investment24 support programs | https://www.kotra.or.kr/subList/20000020753 | ||
| KOSME Export Marketing / Export Voucher | https://kosmes.or.kr/nsh/SH/SBI/SHSBI016M0.do |