Your competitor just announced they're processing loan applications in 4 hours instead of 4 days. A startup in your industry is using AI to deliver personalized customer experiences at scale you can't match with 10x your staff. Your board is asking why your AI pilots haven't moved to production after 18 months.
The answer isn't that you lack AI technology. It's that you're trying to run AI initiatives inside an operating model designed for traditional technology projects. And that mismatch is costing you speed, scale, and competitive advantage.
According to McKinsey research, organizations that build AI-first operating models achieve 3.2x faster innovation cycles and 2.6x higher AI success rates compared to those running AI as bolt-on projects. But most executives don't understand what "AI-first operating model" actually means beyond the buzzword.
Here's what it doesn't mean: firing everyone and replacing them with algorithms. Here's what it does mean: building six core organizational capabilities that let AI become embedded in how you operate, not just what you experiment with.
I've seen dozens of organizations struggle with this disconnect. They approach AI the same way they approached previous technology waves—ERP implementations, cloud migrations, mobile apps. They create a project, assign a team, set a deadline, deploy a solution, declare success.
That approach worked for traditional technology because those technologies replaced manual processes with digital equivalents. AI is fundamentally different: it creates adaptive systems that improve over time through continuous learning.
Here's where traditional operating models break:
Traditional: Fixed requirements defined upfront
AI Reality: Optimal approach discovered through experimentation
Traditional: Success = deployment on time and budget
AI Reality: Success = model performance improves and business adoption scales
Traditional: Maintenance = bug fixes and patches
AI Reality: Maintenance = continuous model monitoring, retraining, and governance
Traditional: Skills = project managers, business analysts, developers
AI Reality: Skills = data scientists, ML engineers, AI product managers, ethics specialists
Traditional: Governance = change control and release approvals
AI Reality: Governance = model performance monitoring, bias detection, compliance management
Traditional: Timeline = months to deployment, then stabilize
AI Reality: Timeline = weeks to first model, continuous improvement forever
The result: Organizations apply waterfall thinking to iterative technology, project thinking to continuous operations, and deployment-focused governance to learning-focused systems. It doesn't work. AI pilots succeed in labs but fail in operations because the operating model isn't built for what AI requires.
The AI-First Operating Model: Six Core Capabilities
An AI-first operating model doesn't mean everything runs on AI. It means your organization has the capabilities to discover AI opportunities, develop AI solutions, deploy them safely, operate them reliably, measure their impact, and improve them continuously—all at scale, not as one-off projects.
Based on my experience helping organizations transition from AI experimentation to AI operations, six capabilities distinguish AI-first organizations from AI-curious ones:
Capability 1: Distributed AI Opportunity Discovery
What it means: Business units identify AI opportunities within their operations, not waiting for central IT or data science teams to tell them where AI could help.
Why it matters: The people closest to business problems spot the best AI opportunities. But they need to understand AI capabilities, limitations, and economics well enough to separate viable opportunities from hype.
What it looks like:
- Business leaders trained in "AI possibility thinking"—what AI can and can't do
- Simple process for business units to propose AI use cases
- Lightweight scoring framework to evaluate opportunities (value, feasibility, risk)
- Examples and templates showing successful AI use cases in similar contexts
- Regular "AI opportunity workshops" where business teams brainstorm applications
How to build it:
Month 1-2: Foundation
- Develop "AI 101 for Business Leaders" training (4 hours, use case focused)
- Create AI opportunity canvas template: problem statement, data availability, success metrics, risk factors
- Establish quarterly AI opportunity review forum with business and technical leaders
Month 3-4: Operationalization
- Train 20-30 business leaders across departments in AI opportunity identification
- Run first AI opportunity workshop with each business unit
- Create library of example use cases from your industry
- Set up simple intake process (Jira, SharePoint form, whatever works)
Month 5-6: Scaling
- Embed AI opportunity thinking in strategic planning processes
- Celebrate and communicate successful use case identifications (even if not yet built)
- Track opportunity pipeline as key metric (not just delivered projects)
- Assign "AI champions" in each business unit to drive opportunity discovery
Success metrics:
- Number of AI opportunities identified per quarter (target: 5-10 in year one)
- Percentage of opportunities coming from business units vs. IT (target: 70% from business)
- Conversion rate from opportunity to pilot project (target: 20-30%)
- Business leader AI literacy score (pre/post training assessment)
Common mistake: Creating an AI innovation competition with prizes. This generates lots of low-quality ideas from people trying to win, not solve real problems. Instead, make AI opportunity discovery part of regular business planning, not a special event.
Capability 2: Rapid Experimentation Infrastructure
What it means: Technical infrastructure and processes that let teams go from AI idea to working prototype in weeks, not months.
Why it matters: AI solutions require experimentation—you discover what works by trying multiple approaches quickly. Organizations without rapid experimentation infrastructure spend 6-12 months on their first prototype. AI-first organizations spend 4-8 weeks.
What it looks like:
- Self-service access to ML development platforms (AWS SageMaker, Azure ML, Databricks, etc.)
- Curated datasets available for experimentation (production data copied to safe sandbox environments)
- Pre-configured model templates for common use cases (classification, prediction, recommendation, etc.)
- Feature engineering pipeline that accelerates data prep
- Automated model training, testing, and comparison tools
How to build it:
Month 1-2: Platform Setup
- Select ML platform based on your cloud strategy and team skills
- Configure development, staging, and production environments
- Set up data access with proper security and privacy controls
- Create "starter kits" for 3-5 common ML use case types
- Document platform capabilities and access process
Month 3-4: Team Enablement
- Train 5-10 technical staff on platform capabilities
- Run first "sprint week" challenge: take a use case from idea to working prototype in 5 days
- Document learnings and create templates from first experiments
- Establish experimentation governance: what's allowed in sandbox, what requires approval
Month 5-6: Scaling Experimentation
- Expand access to broader technical team (20-30 people)
- Create self-service data catalog showing available datasets for experiments
- Implement automated model evaluation (accuracy, performance, bias checks)
- Build model experiment tracking (what was tried, what worked, what didn't)
Success metrics:
- Time from idea approval to working prototype (target: 4-8 weeks)
- Number of experiments run per quarter (target: 10-15)
- Platform utilization: active users, experiments launched (track growth)
- Experiment-to-production ratio (how many experiments turn into deployed models)
Common mistake: Building a perfect, enterprise-grade ML platform before anyone uses it. Start with minimal viable infrastructure that removes the biggest bottleneck (usually data access or compute), then evolve based on real team needs.
Capability 3: Cross-Functional AI Delivery Teams
What it means: Teams that blend data science, engineering, business expertise, and design—working together from discovery through deployment and operation.
Why it matters: AI initiatives fail when data scientists build models in isolation, then "throw them over the wall" to engineering for deployment and to the business for adoption. AI-first organizations embed diverse skills in teams that own outcomes, not just outputs.
What it looks like:
- Teams include: AI/ML engineer (builds models), software engineer (deploys and integrates), product manager (defines requirements and measures value), business analyst (understands domain), UX designer (designs AI-augmented experiences)
- Teams own AI products end-to-end: from identifying opportunity to operating in production
- Teams co-locate (physically or virtually) and work in sprints
- Teams have autonomy to make technical and design decisions within guardrails
- Teams are measured on business outcomes (revenue, efficiency, customer satisfaction), not just technical metrics (model accuracy)
How to build it:
Month 1-2: Team Design
- Define AI delivery team structure for your organization (team size: 5-7 people typically)
- Identify skills needed: data science, ML engineering, software engineering, product management, domain expertise
- Map current staff to roles; identify hiring needs
- Create team charter template defining ownership, decision rights, success metrics
Month 3-4: First Teams
- Form 1-2 pilot AI delivery teams around high-priority use cases
- Assign dedicated team members (not "10% time" allocations—that never works)
- Establish team rituals: daily standups, sprint planning, retrospectives, stakeholder demos
- Create team working agreements: how decisions get made, how conflicts resolve, how success is measured
Month 5-6: Team Scaling
- Document team formation playbook based on pilot learnings
- Form 2-3 additional teams for new AI initiatives
- Establish "chapter" model for role-specific skill development (all data scientists meet monthly, all product managers meet monthly, etc.)
- Create career paths for AI product roles (not just technical roles)
Success metrics:
- Team velocity: time from project start to production deployment (track per team)
- Team stability: retention of team members through project lifecycle
- Business outcome achievement: did the team hit their business impact targets?
- Cross-functional collaboration score (survey team members on collaboration quality)
Common mistake: Creating a "Center of Excellence" that all AI requests flow through, creating a bottleneck. Instead, distribute AI capability into business-aligned teams that can move independently while sharing practices.
Capability 4: Production AI Operations (MLOps)
What it means: The technical capability and processes to deploy AI models to production, monitor their performance, manage their lifecycle, and update them as needed—reliably and at scale.
Why it matters: This is where most organizations fail. They build great models in notebooks that never make it to production, or they deploy models that slowly degrade with no one noticing. Production AI operations is the difference between interesting experiments and business value.
What it looks like:
- Automated model deployment pipelines (CI/CD for ML)
- Real-time model performance monitoring (accuracy, latency, errors)
- Automated model retraining triggered by performance degradation
- Model versioning and rollback capability (if new model performs worse, revert to previous)
- A/B testing infrastructure to safely test new models against current production
- Model governance tracking: what models are in production, who approved them, what data they use, what decisions they make
How to build it:
Month 1-3: MLOps Foundation
- Implement model registry (tracks all models, versions, metadata)
- Set up model deployment pipeline (dev → staging → production with automated tests)
- Establish model performance monitoring dashboards
- Define model deployment approval process (who reviews, what criteria, what documentation required)
- Create model incident response process (what happens when a model fails or performs poorly)
Month 4-6: Advanced Operations
- Implement automated model retraining pipelines
- Set up A/B testing framework for model experiments in production
- Create model governance dashboard (executive view of all production models)
- Build automated alerts for model performance degradation
- Establish model retirement process (when and how to decommission models)
Month 7-9: Scaling Operations
- Implement feature stores (centralized feature engineering for consistency)
- Build cost optimization: right-size compute for model serving
- Create model explainability tools (understand why model made specific decisions)
- Establish model performance SLAs (expected uptime, latency, accuracy)
- Document runbooks for common model operations tasks
Success metrics:
- Model deployment frequency (target: weekly deployments once mature)
- Model deployment success rate (target: >95% successful deployments)
- Mean time to detect model issues (target: <1 hour for critical models)
- Mean time to recover from model issues (target: <4 hours for critical models)
- Number of models in production vs. in development (goal: healthy ratio, not all stuck in dev)
Common mistake: Trying to build perfect MLOps before deploying first model. Start with manual deployment for first 2-3 models, document pain points, then automate the biggest bottlenecks iteratively.
Capability 5: AI Risk Management and Ethics
What it means: Systematic processes to identify, assess, and mitigate risks from AI systems—including bias, fairness, privacy, security, compliance, and unintended consequences.
Why it matters: AI systems make decisions that affect people: who gets hired, who receives medical treatment, who qualifies for a loan, what content people see. Without proper risk management, AI systems can discriminate, violate privacy, make unsafe decisions, or create legal liability.
What it looks like:
- AI ethics principles documented and communicated
- Risk assessment process integrated into AI project approval
- Automated bias detection tests run on all models before production deployment
- Model explainability and transparency for high-risk decisions
- Regular audits of production AI systems for fairness and compliance
- Clear accountability: specific people responsible for AI ethics and risk
- Incident response for AI failures or ethical concerns
How to build it:
Month 1-2: Principles and Governance
- Establish AI ethics principles (fairness, transparency, accountability, privacy, safety)
- Create AI risk assessment framework: categorize AI use cases by risk level (low, medium, high, critical)
- Define risk-based approval process: low-risk = team approval, high-risk = executive review
- Establish AI ethics committee or working group (cross-functional: legal, compliance, technical, business)
- Document AI governance policies (who decides what, based on risk level)
Month 3-4: Risk Assessment Tools
- Implement bias detection tests (demographic parity, equal opportunity, predictive equality)
- Create model documentation standard (what decisions model makes, what data it uses, what risks exist, how it was tested)
- Establish model explainability requirements based on risk level (high-risk models must be explainable)
- Build privacy impact assessment process for AI using personal data
- Create security review checklist for AI systems
Month 5-6: Ongoing Management
- Implement automated compliance monitoring (ongoing checks for bias, performance, privacy)
- Establish regular AI system audits (quarterly for high-risk, annually for low-risk)
- Create AI incident response playbook (what to do if model exhibits bias, makes unsafe decisions, etc.)
- Train AI teams on responsible AI practices
- Build stakeholder communication process for high-risk AI deployments
Success metrics:
- Percentage of AI projects completing risk assessment before deployment (target: 100%)
- Number of bias incidents detected in testing vs. production (goal: catch everything in testing)
- Time to resolve AI ethics concerns when raised (target: immediate pause for critical, resolution in 5 days)
- Compliance with AI regulations (target: zero violations)
- Stakeholder trust score in AI systems (survey employees, customers)
Common mistake: Treating AI ethics as a legal/compliance checkbox. The best AI ethics programs embed responsibility into team culture and development practices, not just policy documents.
Capability 6: Business Value Measurement and Optimization
What it means: Clear frameworks and processes to measure whether AI initiatives are delivering business value, and mechanisms to optimize or shut down initiatives that aren't performing.
Why it matters: It's easy to get excited about AI's technical possibilities and forget to measure business impact. AI-first organizations have discipline around value: they know what success looks like before starting, they measure rigorously, and they're willing to kill projects that don't deliver.
What it looks like:
- Every AI initiative has clear business success metrics defined upfront (not just technical metrics)
- Standardized business case template showing expected investment and returns
- Regular value reviews (monthly or quarterly) comparing actual vs. expected outcomes
- Portfolio view of all AI initiatives with their business impact
- Process to stop or redirect initiatives not delivering expected value
- Continuous optimization: experiments to improve model business impact, not just accuracy
How to build it:
Month 1-2: Value Framework
- Define AI value measurement framework: cost savings, revenue growth, customer experience, risk reduction, etc.
- Create business case template for AI initiatives (required before approval)
- Establish value tracking dashboard (tracks all AI initiatives and their business metrics)
- Define "minimum viable impact" thresholds: what business result justifies continued investment?
- Set up quarterly AI portfolio reviews with executives
Month 3-4: Measurement Implementation
- Implement baseline measurement for all AI initiatives (what's current state before AI?)
- Create automated data pipelines for business metrics (don't rely on manual reporting)
- Train AI teams on value measurement and optimization techniques
- Establish A/B testing for business metrics (does AI version outperform baseline?)
- Document value measurement methodology (how we calculate ROI, attribute causation, etc.)
Month 5-6: Optimization and Scaling
- Run first quarterly portfolio review: which initiatives deliver, which don't?
- Kill or redirect lowest-performing initiatives (demonstrate discipline)
- Launch value optimization experiments: can we improve business impact through model changes, UX changes, process changes?
- Create value case studies for successful initiatives (share learnings)
- Refine business case model based on actual results (update assumptions for future projects)
Success metrics:
- Percentage of AI initiatives with defined business metrics (target: 100%)
- Percentage of AI initiatives meeting value targets (target: 60-70% initially, improve over time)
- ROI of AI portfolio overall (total investment vs. total business value)
- Time from deployment to measurable business impact (target: 90 days or less)
- Number of initiatives killed due to low value (yes, this is a positive metric—shows discipline)
Common mistake: Only measuring technical metrics (model accuracy, latency) without connecting to business outcomes. A model with 95% accuracy that nobody uses creates zero business value.
The Maturity Journey: From Traditional to AI-First
Building these six capabilities doesn't happen overnight. Most organizations progress through four maturity stages:
Stage 1: Project-Based AI (0-6 months)
Characteristics:
- Running 1-3 AI pilot projects
- Each project custom staffed and managed
- No shared infrastructure or processes
- Focus: Can we make AI work at all?
Capabilities to build: Start with #2 (Experimentation Infrastructure) and #3 (Cross-Functional Teams)
Stage 2: Repeatable AI (6-18 months)
Characteristics:
- Successfully deployed 3-5 AI solutions
- Starting to develop patterns and templates
- Dedicated AI team or Center of Excellence forming
- Focus: Can we repeat our success?
Capabilities to build: Add #4 (MLOps) and #5 (AI Risk Management)
Stage 3: Scaled AI (18-36 months)
Characteristics:
- 10-20 AI solutions in production
- AI capability distributed across business units
- Clear ROI and business value demonstrated
- Focus: How do we scale efficiently?
Capabilities to build: Mature #1 (Distributed Discovery) and #6 (Value Measurement)
Stage 4: AI-First Operations (36+ months)
Characteristics:
- AI embedded in core operations
- Continuous innovation and improvement
- Competitive advantage from AI capabilities
- Focus: How do we stay ahead?
Capabilities to build: Continuous optimization of all six
Real-World Evidence: Before and After
Let me share what this operating model transition delivered for a regional healthcare network I worked with previously.
The Starting Point:
- Organization: 5 hospitals, 2,500 beds, $800M annual revenue
- AI maturity: 3 pilot projects running for 18 months, none in production
- Challenge: Technical teams could build models but couldn't deploy them; business stakeholders didn't trust AI; no clear ROI
The 12-Month Transformation:
We systematically built all six capabilities over 12 months:
Months 1-3: Foundation
- Trained 25 clinical and operational leaders in AI opportunity discovery (#1)
- Set up Azure ML platform with secure data access (#2)
- Formed 2 cross-functional AI delivery teams (#3)
Months 4-6: Operationalization
- Implemented MLOps pipeline and model monitoring (#4)
- Established AI governance committee and risk assessment process (#5)
- Defined business metrics and value tracking for each initiative (#6)
Months 7-12: Scaling
- Deployed first 3 AI solutions to production (patient no-show prediction, staffing optimization, supply chain forecasting)
- Business units identified 12 new AI opportunities through workshops (#1)
- Formed 2 additional delivery teams (#3)
- Achieved measurable business value across initiatives (#6)
The Results:
Business Impact (Year 1):
- $2.1M in operational savings (reduced no-shows, optimized staffing, decreased waste)
- 19% reduction in patient appointment no-shows
- 12% improvement in nurse schedule optimization
- 8% reduction in supply costs through demand forecasting
- ROI: 3.2x in year one
Capability Maturity:
- Time to production: Reduced from 18+ months to 4-6 months
- Business opportunity pipeline: 12 qualified AI opportunities (vs. 0 before)
- Team effectiveness: 2 teams delivering 5 production solutions per year
- Risk management: Zero compliance or ethical incidents
- Portfolio discipline: Killed 2 low-value experiments after 8 weeks (vs. letting them linger)
Cultural Shift:
- Clinical leaders actively proposing AI use cases (not waiting for IT)
- Trust in AI decisions: 78% of clinicians surveyed say they trust AI-assisted predictions
- AI team retention: 92% (industry average: 60-70%)
- Executive confidence: Board approved 3-year, $4M AI investment based on year-one results
The Critical Success Factor: We didn't start by trying to transform everything. We built one capability at a time, demonstrated value, then scaled. By month 12, the operating model was self-sustaining.
Your 90-Day Capability Building Roadmap
You can't build all six capabilities simultaneously. Here's a sequenced approach that works:
Months 1-3: Foundation (Capabilities #2 and #3)
Week 1-2: Team Formation
- Identify your first AI delivery team members
- Select first 1-2 high-value use cases to pilot the new operating model
- Assign executive sponsor and establish success metrics
Week 3-6: Infrastructure Setup
- Select and configure ML experimentation platform
- Set up secure data access for AI team
- Create first experiment from use case to prove the infrastructure
Week 7-12: First Delivery
- Form cross-functional team (data science, engineering, business, product)
- Run 4-week sprint cycles with regular stakeholder demos
- Build first prototype and validate with business users
- Document team practices and pain points
Success criteria for Month 3:
- One cross-functional team operating effectively
- ML platform accessible and being used
- First working prototype delivered
- Documented learnings for next teams
Months 4-6: Operations (Capabilities #4 and #5)
Week 13-16: MLOps Foundation
- Implement model deployment pipeline
- Set up model performance monitoring
- Define deployment approval process
- Deploy first model to production (or pilot production)
Week 17-20: Risk Management
- Establish AI ethics principles and governance
- Create risk assessment framework
- Implement bias testing for models
- Document model decisions and risks
Week 21-24: Scaling Delivery
- Form second AI delivery team
- Deploy 1-2 additional models to production using MLOps pipeline
- Run first AI ethics committee review
- Refine processes based on learnings
Success criteria for Month 6:
- 2-3 models in production with monitoring
- MLOps pipeline operational and documented
- AI governance committee functioning
- Zero critical risk incidents
Months 7-9: Value and Discovery (Capabilities #1 and #6)
Week 25-28: Value Measurement
- Define business metrics for all AI initiatives
- Implement value tracking dashboard
- Calculate ROI of current initiatives
- Present first value review to executives
Week 29-32: Opportunity Discovery
- Train business leaders in AI opportunity identification
- Run first AI opportunity workshop with each business unit
- Create AI opportunity pipeline and review process
- Select 2-3 new opportunities for next wave of development
Week 33-36: Portfolio Management
- Conduct first quarterly portfolio review
- Make go/no-go decisions on current initiatives based on value
- Refine business case model based on actuals
- Plan next 6 months of AI initiatives
Success criteria for Month 9:
- Clear business value demonstrated (ROI calculated)
- 5-10 new opportunities identified by business units
- Portfolio governance functioning
- Executive confidence in AI program value
Common Capability Building Mistakes to Avoid
Mistake 1: Building capability before demonstrating value
- Wrong: "Let's spend 6 months building perfect AI infrastructure before starting any projects"
- Right: "Let's solve one real problem with minimal infrastructure, then build based on what we learned"
Mistake 2: Treating capability building as a one-time project
- Wrong: "We'll build our AI operating model in Q1, then start delivering in Q2"
- Right: "We'll build capabilities iteratively while delivering value, evolving based on what we learn"
Mistake 3: Centralizing all AI capability in one team
- Wrong: "All AI requests go through our Center of Excellence team"
- Right: "We distribute AI capability into business-aligned teams that share practices through a community"
Mistake 4: Optimizing for technical excellence over business value
- Wrong: "Our models have 98% accuracy!" (but nobody uses them)
- Right: "Our model has 87% accuracy and delivered $500K in savings because people trust and use it"
Mistake 5: Ignoring organizational change management
- Wrong: "We built it, now the business needs to use it"
- Right: "We built it with the business, addressing their concerns and building their trust throughout"
When You Know Your Operating Model Is AI-First
You'll know you've successfully built an AI-first operating model when:
- Business units propose AI opportunities without prompting from IT or data science teams
- New AI initiatives go from idea to production in 8-12 weeks, not 8-12 months
- You measure AI initiatives by business impact, not just technical metrics
- You kill low-value AI projects quickly, demonstrating portfolio discipline
- AI teams have autonomy to make technical and design decisions within guardrails
- Models are continuously improved in production, not deployed and forgotten
- Risk and ethics are embedded in development practices, not bolt-on compliance
- Executives can articulate the business value of your AI portfolio in board meetings
These aren't aspirations—they're operational realities for AI-first organizations.
Take Action: Start Building Your First Capability This Week
You don't need to transform everything immediately. Pick one capability to build first based on your current biggest constraint:
If your constraint is "We don't know where to use AI" → Start with Capability #1 (Opportunity Discovery)
If your constraint is "We can't move fast enough" → Start with Capability #2 (Experimentation Infrastructure)
If your constraint is "Our data scientists and business don't collaborate" → Start with Capability #3 (Cross-Functional Teams)
If your constraint is "We can't get models to production" → Start with Capability #4 (MLOps)
If your constraint is "We're worried about AI risks" → Start with Capability #5 (Risk Management)
If your constraint is "We can't prove ROI" → Start with Capability #6 (Value Measurement)
This week:
- Identify your biggest constraint from the list above
- Review the "How to build it" section for that capability
- Schedule a 1-hour session with key stakeholders to commit to building that capability
- Block time on your calendar for Month 1-2 actions
- Assign someone to own the capability building effort
Within 30 days:
- Complete Month 1 actions for your chosen capability
- Document what's working and what's not
- Adjust approach based on learnings
- Plan to add second capability in Month 4
Building an AI-first operating model is a journey, not a destination. But organizations that commit to this journey consistently outperform those that treat AI as just another technology project.
Let's Build Your AI-First Operating Model
If you're serious about moving from AI experiments to AI operations, you need more than a framework—you need a customized implementation roadmap for your organization's specific context, constraints, and objectives.
I help organizations design and implement AI-first operating models that accelerate delivery, manage risk, and deliver measurable business value. This includes capability assessment, roadmap development, team formation, and hands-on implementation support.
→ Book a 90-minute AI Operating Model Strategy Session to map your current state, identify your biggest constraints, and create a 90-day capability building plan.
Or download the AI-First Operating Model Assessment (PDF) to score your organization's maturity across all six capabilities and identify your priority gaps.
The organizations that win with AI don't have better technology—they have better operating models. Make sure yours is built for AI, not just adapted from the past.