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Welcome

AI Now Next

5 min

Recommendation: choose one AI-assisted workflow to improve.

22 June 2026 · 7N AI Path · Arrow keys → / ←

  • You already have basic AI exposure; lens slides add PM, Developer and risk angles.
  • Focus on judgment and workflows, not tool tourism.
AI Now Next — supporting diagram

PM takeaway

PM lens — Welcome

  • Start from one decision: what should change in delivery after AI Path?
  • Name the owner and review point before you scale any AI-assisted step.

Developer extension

Developer lens — Welcome

  • Use lens slides for implementation detail without slowing the main track.
  • Keep tool choices secondary to contracts, tests and review loops.

Client risk check

Risk lens — Welcome

  • The site is guidance, not policy. Client data rules still decide what is allowed.
  • Resources: session resources

AI now and next

What changed in AI by June 2026

10 min

Recommendation: track workflow consequences, not model announcements.

  • Ask what teams can safely delegate now.
  • Watch where human judgment becomes more important.
  • Source: BCG, AI at Work: Why Strategy Matters More Than Tools.

PM takeaway

PM lens — AI shifts

  • Track AI shifts by delivery risk, stakeholder expectations and decision quality.
  • Ask which recurring meeting or brief AI can shorten without hiding judgment.

Developer extension

Developer lens — AI shifts

  • Look for workflow affordances: tool use, context handling, evals and handoff points.
  • Upgrade prompts into skills when the same contract repeats across tasks.

Client risk check

Risk lens — AI shifts

AI now and next

Signal over noise

Recommendation: adopt a trend only when it changes recurring delivery work.

  • A trend matters when it changes a recurring task or shifts client expectations.
  • Ignore announcements that do not change a workflow you own this quarter.

Prompting, verification, responsibility

Using AI well

8 min

Recommendation: treat prompting as work design with a review loop.

  • Define the job, assumptions and stop rule before asking.
  • Verify outputs against factual, client and code constraints.
  • Sources: MIT Sloan on “AI gravity”; HR Dive on AI overdependence.
Using AI well — supporting diagram

PM takeaway

PM lens — Prompting and verification

  • Good use starts before prompting: define decision, risk level and review point.
  • Escalate to a human check when the output affects client commitments.

Developer extension

Developer lens — Prompting and verification

  • Prompt quality improves when context, constraints, tests and output contracts are explicit.
  • Encode the contract in a skill when the same prompt pattern repeats weekly.

Client risk check

Risk lens — Prompting and verification

  • Overdependence shows up as skipped verification. Keep a human review loop for critical outputs.
  • Source: MIT Sloan on AI gravity

Prompting, verification, responsibility

Prompt pattern

Recommendation: write prompts as explicit work contracts.

  • Role, task, context, constraints, output format, verification criteria and stop rule.
  • Short is fine when the contract is clear and the review path is named.

Agentic workflows

AI agents in practice

12 min

Recommendation: use agents for bounded, checkable workflows.

  • Give tools, limits and a visible success test.
  • Start with reversible work before client-critical delivery.
  • Good fits: research, tested code changes, document transformation.
AI agents in practice — supporting diagram

PM takeaway

PM lens — Agent workflows

  • Think of agents as workflow helpers with boundaries, not autonomous colleagues.
  • Require named owner, success test and rollback before any client-facing use.

Developer extension

Developer lens — Agent workflows

  • Implementation quality depends on tools, state, permissions, observability and rollback.
  • Log agent actions and keep human approval on merge or deploy steps.

Client risk check

Risk lens — Agent workflows

  • Agents fail when goals, permissions or verification are vague. Start with reversible work.
  • Broad tool access without logging is a client and security liability.

Agentic workflows

Where agents break

Recommendation: design agents assuming context will go stale.

  • They drift when success criteria are implicit or no one checks the final artifact.
  • Add stop rules and a human checkpoint before irreversible actions.

Agentic workflows

Agent examples

Recommendation: start with workflows you can verify end to end.

  • Good fits: research synthesis, tested code changes and document transformation.
  • Poor fits: open-ended client advice and unreviewed production deploys.

Consulting industry

Real consulting use cases

13 min

Recommendation: choose repeated friction before impressive demos.

  • Require owner, input, output and review path.
  • Start with briefs, stakeholder drafts and testable plans.
  • Source: BCG on strategy and work redesign over tools.
Real consulting use cases — supporting diagram

PM takeaway

PM lens — Consulting use cases

  • Start with recurring delivery friction: meeting prep, stakeholder alignment, risk logs and decision briefs.
  • Measure benefit as time saved on a named recurring task, not generic productivity.

Developer extension

Developer lens — Consulting use cases

  • High-value uses include test generation, migration planning, code review support and docs-to-implementation traceability.
  • Pair each use case with tests or diff review before it touches shared codebases.

Client risk check

Risk lens — Consulting use cases

Consulting industry

Use-case test

Recommendation: reject use cases that lack a review path.

  • A good AI use case has named owner, bounded input, expected output, review path and measurable benefit.
  • If any element is missing, keep the workflow manual until it is defined.

Market awareness

Stay informed without chasing every trend

7 min

Recommendation: build a weekly signal routine instead of chasing every trend.

  • Scan one capability shift and one delivery implication.
  • Test one small workflow before changing team practice.
  • Ask: what should we try, stop or standardize?

PM takeaway

PM lens — Market signals

  • Use a weekly routine: one capability scan, one delivery implication, one small experiment.
  • Share only signals that change a decision your team owns this month.

Developer extension

Developer lens — Market signals

  • Track release notes only when they affect models, context, tool calling, evals, deployment or security posture.
  • Spike one change in a sandbox before proposing team-wide adoption.

Client risk check

Risk lens — Market signals

  • Trend chasing consumes attention. The risk is not missing every update; it is missing updates that change your work.
  • Standardize a filter: client impact, data handling and review burden before adoption.

Next steps

Wrap-up Q&A

30 min

Recommendation: make the next step practical, local and reviewable.

  • Choose one repeated work pattern.
  • Add AI only where the review path is clear.
  • Keep the human judgment loop explicit.

PM takeaway

PM lens — Next steps

  • Leave with one workflow to improve, one risk rule to clarify and one experiment for next week.
  • Name who reviews AI-assisted output before it reaches the client.

Developer extension

Developer lens — Next steps

  • Pick one bounded automation loop, make review observable, then expand.
  • Document the prompt or skill contract so teammates can reproduce the workflow.

Client risk check

Risk lens — Next steps

  • Responsible AI is operational: permissions, data, review, logging and escalation.
  • Escalate when client data, production access or unreviewed outputs enter the loop.