Summary

Stratenity advisory perspective.

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Core Challenge

  • Issue: Firms market “AI capabilities” but struggle to convert them into measurable client outcomes.
  • Context: Point solutions proliferate; little linkage to client operating models or P&L.
  • Stratenity POV: Treat AI as an operating system for value creation, not a feature catalog.
  • Executive Direction: Tie every AI initiative to a target KPI and system of record; publish before/after baselines.
  • KPIs: % engagements with productionized AI; time-to-value; outcome uplift vs baseline; attach rate of managed services.
  • Example Project: “AI-to-Outcome” portfolio review and re-design across top 10 offerings.
  • AI Use: Automated KPI tracking and variance explanation for each engagement.

Strategy vs Tools Misalignment

  • Issue: Tool demos replace strategy-to-execution roadmaps.
  • Context: Sales motions favor quick wins; delivery teams inherit mis-scoped AI pilots.
  • Stratenity POV: Anchor AI in client choices (where to play/how to win) and operating model.
  • Executive Direction: Mandate “Outcome Canvas” in proposals with KPI, owner, data, and integration path.
  • KPIs: % pursuits with Outcome Canvas; win rate; production adoption rate at 90 days.
  • Example Project: Rebuild top pursuit templates with strategy-linked AI impact sections.
  • AI Use: Generate strategy-to-systems traceability maps from proposal → delivery.

Data & Proprietary Assets

  • Issue: Over-dependence on client data and vendor models limits defensibility.
  • Context: Fragmented knowledge bases; thin domain libraries; minimal reusable components.
  • Stratenity POV: Build proprietary libraries (frameworks, patterns, evaluators, retrieval corpora) by industry/process.
  • Executive Direction: Create “AI Asset P&L” with investment rules and reuse incentives.
  • KPIs: % revenue from IP-enabled offerings; reuse rate; evaluation benchmark scores by domain.
  • Example Project: Industry Retrieval Library (15 industries × 5 domains × 6 deliverable layers).
  • AI Use: Auto-curate/evaluate firm IP; red-team and version with governance.

Talent & Operating Model

  • Issue: Strategy consultants and ML engineers operate in silos.
  • Context: Unclear roles; “throw over the wall” from advisory to build.
  • Stratenity POV: Form AI full-stack pods (strategy, data, ML, apps, change, risk) with shared OKRs.
  • Executive Direction: Convert practices into product-line pods with outcome-linked incentives.
  • KPIs: Cross-disciplinary utilization; % engagements staffed with full-stack pods; delivery cycle time.
  • Example Project: Pod playbook and staffing marketplace across practices.
  • AI Use: Skills graph for staffing and capability gap detection.

Proof-of-Concept Trap

  • Issue: Dazzling demos never reach durable production.
  • Context: Missing MLOps, security, data contracts, and runbooks.
  • Stratenity POV: “Ready-to-Run” standards: deployment patterns, eval gates, rollback plans.
  • Executive Direction: Institute Stage-Gates (PoC → Pilot → Production) with hard criteria.
  • KPIs: PoC-to-Prod conversion; cost-to-serve; SLO adherence; incident rate.
  • Example Project: Firm-wide AI deployment blueprint over ERP/CRM/HRIS with observability.
  • AI Use: Continuous evaluation and drift detection with alerting.

Governance & Trust

  • Issue: Executive fear of bias, leakage, and regulatory exposure.
  • Context: Inconsistent guardrails across offerings and geographies.
  • Stratenity POV: Central AI governance with policy-as-code and audit trails.
  • Executive Direction: Adopt unified risk taxonomy (privacy, IP, safety, model risk) and attestations.
  • KPIs: % offerings with signed governance controls; audit pass rate; exception time-to-close.
  • Example Project: “Trust Spine” — red-teaming, evals, and privacy-safe retrieval patterns.
  • AI Use: Automated policy checks and evidence packets for compliance.

Adoption & Change

  • Issue: Users revert to old workflows; benefits evaporate.
  • Context: Training is generic; incentives misaligned; no frontline co-design.
  • Stratenity POV: Treat adoption as product: personas, jobs-to-be-done, incentives, and feedback loops.
  • Executive Direction: Tie role scorecards to AI-enabled KPIs; embed in daily systems of work.
  • KPIs: Weekly active usage; task cycle-time delta; error rate delta; NPS by persona.
  • Example Project: Role-based copilot rollout with embedded training and measurement.
  • AI Use: In-app guidance, nudges, and contextual help with guardrails.

Tech & Integration

  • Issue: Beautiful front-ends with no backbone.
  • Context: Weak connectors to ERP/CRM/PLM; brittle security and logging.
  • Stratenity POV: Data foundation → model layer → apps → integration → observability.
  • Executive Direction: Reference patterns per system (SAP, Oracle, Salesforce, Workday, ServiceNow).
  • KPIs: % use of approved patterns; integration MTTR; incident rate; infra cost per use.
  • Example Project: “Golden Path” connectors with signed data contracts and eval packs.
  • AI Use: Auto-mapping schemas, generating connectors and tests, tracing lineage.

Commercial Model

  • Issue: Time-and-materials doesn’t monetize continuous AI value.
  • Context: Decks and PoCs capture fees; operations capture value.
  • Stratenity POV: Hybrid models (subscriptions, managed outcomes, IP licensing).
  • Executive Direction: Create platform SKUs and outcome-priced add-ons; align comp plans.
  • KPIs: % revenue from recurring/IP; gross margin of managed services; client retention.
  • Example Project: Launch “AI Run” managed offering across two industries.
  • AI Use: Automated usage metering, benchmarking, and ROI reporting.

Stratenity Lens: Path Forward

  • From demos to durable systems: track PoC→Prod conversion and SLOs.
  • From vendor wrappers to proprietary assets: measure IP reuse and eval scores.
  • From siloed teams to full-stack pods: reduce delivery cycle time, increase quality.
  • From billable hours to managed outcomes: grow recurring/IP revenue mix.
  • From advice to accountability: publish KPI baselines and post-go-live deltas.

Future Research Needed

  • Outcome-pricing models and contractual risk sharing for AI programs.
  • Standard eval suites for domain copilots and retrieval quality by industry.
  • Ethics and IP frameworks for co-developed assets with clients.
  • Talent economics for full-stack pods and AI-augmented delivery.
  • Benchmarks for governance maturity and incident transparency.

Management Consulting Guidance

  • Lead with the business problem; show KPI math and operating model changes.
  • Package reusable assets (patterns, connectors, evaluators) into offerings.
  • Stand up governance early; automate evidence and audits.
  • Co-design workflows with frontline users; change incentives and training.
  • Ship to production with observability; publish before/after results.
  • Move to hybrid commercial models; maintain a run organization.

Execution Levers for Consulting Firms

Lever What it Means Example Execution Moves
From Advice → Systems Deliver working AI in client systems with observability and support. • Define Golden Path integrations per platform
• Ship reference deployments and runbooks
• Attach managed service to every production go-live
From Pilots → Scale Codify deployment patterns and roll across accounts/industries. • Stage-gate standards (PoC→Pilot→Prod)
• Reusable connectors and eval packs
• Portfolio PMO tracking adoption and ROI
From Vendor Tools → Firm IP Build proprietary libraries, evaluators, and domain agents. • Curate a retrieval library mapped to firm frameworks
• Domain benchmark suite and leaderboards
• IP reuse incentives in comp plans
From Hours → Outcomes Monetize ongoing performance, not just project effort. • Outcome-based SKUs with SLAs
• Usage metering and ROI dashboards
• Co-investment and gain-share structures

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