Best of LinkedIn: Strategy & Consulting CW 40/ 41

Leadership-led AI governance emerged as the operating anchor

Cross-functional alignment among CTO, CFO, and CSO framed execution decisions

Talent models focused on new skills, role redesign, and workflow integration

Change management emphasized playbooks, adoption rhythms, and measurement cadence

Shift from pilots to scaled value with production architectures

Agent performance management surfaced with reliability, evaluation, and oversight

Techniques like neurosymbolic AI highlighted accuracy, auditability, and speed

RAG and model selection were treated as design choices, not ends in themselves

Guardrails and governance moved into quarterly operating routines

Risk frameworks connected to compliance, bias mitigation, and monitoring

Board and executive oversight clarified accountability and decision rights

Transparency and auditability were framed as enablers of scale and trust

ROI discipline focused on cost, margin, and productivity baselines

KPI stacks and OKRs anchored business outcomes for AI programs

Dashboards framed performance as a single source of decision truth

Hard benefits replaced soft narratives across transformation themes

Data quality and lineage positioned as prerequisites to AI scaling

Decision dashboards elevated from reporting to operating layer

Metric design stressed clarity, comparability, and business ownership

Governance of datasets aligned with model lifecycle requirements

Procurement positioned as owner of AI demand control and vendor risk

Category strategies adapted to model, data, and service spend

Contracts stressed evaluation rights, security, and exit flexibility

Supplier governance tied to ethics, uptime, and measurable outcomes

Platform thinking prioritized APIs, integration, and resilience

Cloud choices aligned to security, sovereignty, and cost envelopes

Reference architectures guided model hosting and retrieval patterns

Observability and FinOps tightened control of AI workloads

Product development integrated AI for faster iteration and testing

Backlogs prioritized value cases over feature experiments

User research and PMF checks preceded automation at scale

Roadmaps linked to capability maturity, not novelty alone

Collaborations highlighted cloud and advisory co-delivery

Alliances aimed at faster build-operate-transfer in AI programs

Ecosystem play reduced time to value and integration risk

Knowledge transfer and playbooks became explicit partnership assets

Value propositions centered on outcome guarantees and risk sharing

Sales motions aligned to executive buyers with operational metrics

Pricing models tied to usage, results, and governance level

Adoption success plans formalized across onboarding and enablement

Security by design embedded into model and data pipelines

Continuous monitoring addressed drift, misuse, and access

Compliance mapping connected model behavior to policy controls

Incident playbooks specified roles, thresholds, and escalation paths

Efficiency gains framed as cost and footprint reductions

Data transparency supported ESG reporting and audit readiness

Operating discipline linked responsible AI to sustainability goals

Vendor selection considered energy, locality, and governance depth