Change management for enterprise AI adoption
Tie adoption to a specific workflow, accountable owners, measurable behavior, safe escalation, and feedback from the people doing the work.
Read articleBack to all guidance
Guidance library
Product leader
Product leaders shaping customer outcomes, adoption, and reusable platform capabilities.
7 articlesGuidance library
Reviewed enterprise architecture guidance
Narrow the library by controlled topic, audience, pillar, or API product.
Testing product-market fit for an AI product
Use retention, repeated workflows, willingness to pay, expansion, and customer behavior to test demand without relying on enthusiasm or model novelty.
Read article
Measuring AI value without invented ROI
Start with a bounded workflow, baseline its current cost and quality, record adoption and exceptions, then separate measured results from assumptions.
Read article
Scoping an AI product MVP around one customer workflow
Choose a narrow problem, define the human fallback, instrument real use, and postpone autonomy or platform breadth until evidence supports it.
Read article
Build versus buy for enterprise AI infrastructure
Compare control, differentiation, integration depth, security, time, maintenance, switching cost, and the team's ability to operate what it owns.
Read article
The operating cost of manual inventory reconciliation
Map where inventory conflicts are detected, who resolves them, which consumers wait, and where a qualified Business API can remove repeated interpretation.
Read article
Build a usable product definition across PLM, ERP, and channels
Resolve identifiers, attributes, lifecycle ownership, pricing boundaries, and channel needs without declaring one copied record universally correct.
Read article