Your Roadmap: Implementation by Role

Before You Start: Map Your Current State

Weeks 1-2: Identify Your Sources of Truth

For each role in your product development cycle, ask:

Where does authoritative information currently live? (Often: someone's head, scattered documents, tribal knowledge)
What decisions does this role own?
What information do they need from other roles to do their work?
What information do other roles need from them?

Tools: Use a simple spreadsheet or diagram. Don't overcomplicate this step.

Output: A visual map of information flows and dependencies that everyone can understand.

Weeks 3-4: Find Your Current Bottleneck

AI changes production economics. Where is your constraint NOW‚ not where it was two years ago?

Is it still development time? (Unlikely if you're using any AI coding tools)
Is it code review and integration?
Is it requirements clarity and product decisions?
Is it validation and testing?
Is it deployment and infrastructure?
Is it business decision-making and stakeholder alignment?

Critical insight: If you optimize for the wrong bottleneck, AI won't help and might actively hurt productivity.

Method: Track where work waits in queue. Time waiting reveals bottlenecks more accurately than subjective assessments.

For Individual Contributors

Month 1: Add Intent Documentation

What to change: When you complete a task, spend 2-5 minutes capturing WHY you made key decisions.

Developer examples:

"Chose library X over Y because it handles edge case Z that we've seen in production"
"Refactored this section to reduce coupling with authentication system for future flexibility"

Designer examples:

"Simplified this flow because user testing showed confusion at step 3"
"Color choice balances accessibility requirements with brand consistency standards"

Product Manager examples:

"Prioritized Feature A over B due to customer requests from top 3 enterprise clients"
"This requirement intentionally leaves flexibility for future third-party integrations"

Why this matters:

AI can learn from your reasoning, not just your output
Future you (or your replacement) understands the context when changes are needed
You're building the dataset for AI to provide actually useful suggestions rather than generic patterns

What to expect:

First 2 weeks: Feels like overhead. You'll question whether it's worth the time.
Weeks 3-4: Becomes habitual. You find yourself thinking more clearly about decisions.
Months 2-3: You start benefiting when AI can reference this context in its suggestions.

Month 2-3: Use AI to Amplify Your Work

Now that you're documenting intent, you can use AI more effectively:

Use AI to draft documentation based on your decision logs
Ask AI to identify patterns in your past decisions
Have AI suggest edge cases based on your historical context
Generate test cases from your implementation notes

The critical difference: AI suggestions are now grounded in YOUR domain knowledge and decision-making patterns, not just generic best practices.

Month 4-6: Validate and Refine

Close the loop on what's working:

When AI suggestions work well: Note why they were helpful
When AI suggestions fail: Note what context was missing
Update your intent documentation with these learnings
Share successful patterns with your team

Success indicator: You're spending less time on routine cognitive tasks and more time on complex judgment calls that actually require your expertise.

For Managers (Team Leads, Engineering Managers, Design Leads)

Month 1: Establish Role-Based SLAs

Create simple agreements between roles that make expectations explicit.

Example SLA: Design‚ Development

Design provides:

Mockups, user flows, edge case specifications
Within 2 business days of development request
Format: Figma files + written specs in [agreed tool]

Development provides:

Technical feasibility feedback
Within 1 business day of design proposal
Format: Written assessment with alternatives if original design isn't feasible

Example SLA: Product‚ Validation

Product provides:

Acceptance criteria and success metrics
Before development work begins on a feature
Format: Structured template in [agreed tool]

Validation provides:

Test coverage report and identified gaps
Within 1 sprint of feature completion
Format: Dashboard link + written summary of risks

Why SLAs matter:

Makes implicit expectations explicit, reducing friction
Creates accountability without creating blame
Provides clear targets for what AI could automate
Reveals where processes actually break down in practice

Month 2-3: Identify High-Value Data Pipelines

Pick ONE pipeline to optimize first. Don't try to fix everything at once.

Example: Requirements‚ Development‚ Validation

Current state (typical):

Requirements exist in meeting notes, Slack messages, PM's head
Development interprets requirements during sprint planning
Validation writes tests based on implemented code
Misalignment discovered late in the process or after deployment

Optimized with world model approach:

Requirements captured in structured template with intent documented
AI suggests technical implications and potential edge cases
Development references structured requirements + documented intent
AI generates initial test cases directly from requirements
Validation refines AI-generated tests with domain knowledge
Changes to requirements automatically flag affected tests and implementations

Measure before and after:

Number of rework cycles (requirements changes discovered mid-sprint)
Validation cycles (bugs found in testing vs. production)
Time from clear requirement to validated, deployable feature

Month 4-6: Pilot AI Tools Within Established Pipelines

Only after you have clear role responsibilities and data flowing between them should you introduce AI tools strategically.

For requirements pipeline:

AI summarizes customer feedback into structured format
AI identifies conflicting requirements across feature requests
AI suggests acceptance criteria based on similar past features

For code review pipeline:

AI flags potential security vulnerabilities
AI checks consistency with established coding standards
AI suggests test coverage gaps based on code changes

Critical rule: AI suggestions must always be validated by the accountable human role. No auto-merge. No blind acceptance. Humans remain responsible for outcomes.

Month 7-12: Expand Based on Success

What worked in your pilot:

Identify specific AI capabilities that consistently saved time without introducing errors
Document the pattern: task type, AI tool used, validation method, success rate
Replicate to similar workflows across other teams

What didn't work:

Did AI lack necessary context? Fix: Improve data pipeline to provide context
Did AI produce too much slop? Fix: Try different tool or keep human-only for now
Did validation take longer than doing it manually? Fix: This workflow isn't ready for AI yet

Success indicators:

Team reports higher quality output in less time
Rework cycles decreased measurably
AI suggestions accepted more than 50% of the time
Team actively requests more AI integration (rather than tolerating mandated tools)

For Executives (Directors, VPs, C-Suite)

Months 1-3: The Honest ROI Conversation

Stop AI-washing. Start measuring what actually matters.

Instead of: "We're AI-first now" (this means nothing to customers or employees)

Measure these specific outcomes:

Time saved on specific, repeatable tasks (document generation, code reviews, data analysis)
Quality improvement: Reduced rework, faster validation, fewer production issues
Bottleneck shifts: Where is work waiting now versus six months ago?
Team sentiment: Are people using AI tools voluntarily or under duress? Are they requesting more capabilities?

Red flags you're in a J-curve decline:

Productivity metrics actually dropping after AI introduction
Team spending more time correcting AI output than previous manual processes took
AI tool adoption declining after initial enthusiasm
Increased errors reaching customers despite AI "quality checks"

Green flags you're on the right track:

Specific workflows showing measurable, sustained improvement
Teams adapting tools to their needs (not just using out-of-the-box configurations)
Data quality improving because people see value in documentation
Cross-functional coordination getting easier and requiring fewer meetings

Months 4-6: Investment Priorities

Reframe capital allocation around what actually drives AI success.

High ROI (do first):

Data infrastructure: Making internal data clean, accessible, and reliable
Role clarity workshops: Helping teams define sources of truth and SLAs
Training on world model approach: Not generic "AI literacy" but specific workflows
Pilot programs in high-value workflows with clear success metrics

Medium ROI (do after pilots succeed):

Expanding AI tools to more workflows based on pilot learnings
Custom AI integrations with your specific internal systems
Advanced capabilities like fine-tuning or specialized models

Low ROI (avoid for now):

Wholesale platform replacements before proving value with existing tools
AI tools without clear, specific use cases tied to actual workflows
"AI transformation consultants" selling generic frameworks
Layoffs based on projected AI efficiency gains (you'll end up rehiring)

The critical lesson from companies like Salesforce: You need experienced people whose judgment can compensate for current AI limitations. Over-optimize for headcount reduction and you'll crater productivity while destroying institutional knowledge.

Months 7-12: Cultural Change Timeline

Set realistic expectations with your organization.

What to communicate:

3-6 months: Teams will trust the process and see value in new workflows
6-12 months: Data pipelines become habitual, AI tools integrate naturally
12-18 months: Measurable productivity gains and reduced time-to-market become evident
18-24 months: Competitive advantage from institutional knowledge captured in world models

Warning: Demanding faster results will kill trust and create performative adoption where teams claim to use AI but quietly revert to old methods to actually get work done.

Your role as executive:

Communicate clearly: Why this matters, what success looks like, realistic timeline
Buffer pressure: Protect teams from quarterly demands during transition period
Celebrate progress: Small wins, specific improvements, team innovations
Model the behavior: Use world model thinking in your own strategic work

Success story to aim for at Month 12:

"Our product releases have 40% fewer post-launch issues. Customer Support receives release notes and known issues before deployment instead of discovering problems through customer complaints. Marketing launches coordinated campaigns aligned with feature releases. Development team spends less time in alignment meetings and more time building because requirements are clear and documented. And we've captured institutional knowledge that makes onboarding new team members three times faster than before."

The Universal Implementation Pattern

This pattern works across all roles and organization sizes.

Phase 1: Map (Weeks 1-4)

Current sources of truth (where authoritative information actually lives)
Information flows (who needs what from whom, when, and why)
Current bottlenecks (where work consistently waits)
Success metrics (how we'll know if this is working)

Phase 2: Pilot (Months 2-4)

Choose ONE high-value data pipeline to optimize
Define clear role-based SLAs for that pipeline
Add minimal tasks to each role (intent documentation, handoff clarity)
Measure baseline performance‚ implement changes‚ measure improvement

Phase 3: Validate (Months 4-6)

Answer these questions with data:

Did rework decrease? By how much?
Did validation cycles shorten? What's the time savings?
Did stakeholder confidence increase? How do we know?
Are people actually using the new process or working around it?

Phase 4: Introduce AI (Months 5-7)

Critical: Only AFTER the data pipeline is working with humans should you add AI.

Start with low-risk, high-value tasks that have clear validation
Ensure AI suggestions are always validated by accountable humans
Measure time saved versus validation overhead
Be willing to remove AI from tasks where it's not adding value

Phase 5: Expand (Months 7-12)

Replicate successful patterns to other data pipelines
Remove AI from workflows where it's creating more work than it saves
Deepen AI integration in areas where it's proving consistently valuable
Update your world model based on what's actually working

Phase 6: Evolve (Ongoing)

As AI capabilities improve, revisit what's possible in workflows that weren't ready
As bottlenecks shift due to AI adoption, adapt role responsibilities accordingly
As teams learn and share knowledge, capture new patterns in the world model
As business priorities change, update what's considered high-value

Conclusion: What Success Looks Like

Month 6: Early Wins

Your team conversations change in noticeable ways:

"The AI caught three edge cases in the requirements that we would have discovered in production"
"I spent 30 minutes on documentation this sprint and it saved us four hours of rework later"
"Customer Support knew about the changes and potential issues before we deployed"
Fewer hours in meetings clarifying requirements because they're already clearly documented
Designers and developers resolving questions through structured documentation rather than Slack messages

Year 1: Transformation Underway

Measurable improvements accumulate:

Feature delivery time reduced by 20-30% with higher quality
Post-launch defects down 30-40%
Cross-functional coordination smoother and requiring less synchronous time
Team autonomy increasing as roles and expectations become clearer
AI tools naturally integrated into 60-70% of workflows
New team members onboarding faster because knowledge is documented

Year 3: Competitive Advantage

What you've built becomes genuinely differentiating:

A living knowledge base that captures WHY decisions were made, not just what was decided
Institutional knowledge that survives employee turnover and role changes
AI systems that understand your specific domain, context, and decision-making patterns
Roles that co-evolve with AI capabilities rather than being disrupted by them
A culture of continuous adaptation that welcomes capability improvements
Speed and quality advantages that competitors can't easily replicate