Transformation Pros Know: AI Remaps the Work
Adapting to AI takes more than training leaders and technologists.
A viewpoint shaped by decades of hands-on work helping organizations adopt and operate through major technology platform shifts.
AI redraws the work
- Tasks and decisions
- Responsibilities and job boundaries
- Role relationships
- Human–technology interaction
- Evidence requirements
Where does
the new work belong?
Who does what now?
What decisions move?
What must people validate?
How do existing roles interact differently
with technology and one another?
Rehiring, replacing roles, or reorganizing the company is not the automatic answer.
Human Work Moves Towards Judgement
AI shifts human work from producing evidence to determining whether evidence is sufficient.
Less emphasis on production
- Gathering
- Searching
- Summarizing
- Routine analysis
- Report preparation
- Chasing information
Greater emphasis on assurance
- Testing responses
- Understanding how conclusions were reached
- Recognizing failure modes
- Finding excluded outcomes
- Judging evidence completeness
- Deciding whether results are sufficient to proceed
The Problem You See May Not Be the Problem You Need to Test
AI oversight requires looking beneath the obvious failure.
Autonomous action
→Decision explainability
False positives / false negatives
→The failure mode behind the result
Wrong information
→Depth of analysis and coverage
of material candidate outcomes
The new skills: scrutiny, testing, and correction.
Everyone Still Has a Role
Existing professional disciplines remain. Their application point moves.
Engineer
Technical assurance
Build / configure
↓Test sources, integrations, behavior, controls, and model-dependent operation.
Verify intended system behavior.
Business analyst
Decision assurance
Gather / synthesize
↓Scrutinize evidence, assumptions, completeness, candidate outcomes, and decision sufficiency.
Establish sufficient evidence to act.
Quality / operational assurance
Corrective assurance
Check conformance
↓Analyze errors, recurring failures, and root conditions. Correct and retest.
Identify root causes. Correct and retest.
The Technology Changes.
The Pattern Repeats.
Every major operating shift has changed work, not just technology.
- Service-oriented
systems - Information sharing
/ fusion - Cloud
computing - Automation
- Digital
services - Artificial
intelligence
New tasks. Different decision points. Changed responsibilities.
New technology relationships. Different evidence requirements.
Across these transitions, leaders faced a recurring choice:
1. Define the work before launch
→ Earlier operational readiness
2. Leave the work to emerge in operation
→ Delay, confusion, and rework
Pressure-Test the Operating Model Before Launch
Most of the answer is probably already inside your plan.
Examine the plan
Strategic intent & proposed architecture
Existing jobs & current processes
Decision authority & evidence flows
Planned operating milestones
Organizations have adapted resources already in place,
rather than relying on a structural overhaul.
Make the assumptions explicit
Task ownership and decision authority
Changed relationships between roles
Required tests and sufficient evidence
Failure response and corrective action
- Intent
- Tasks
- Roles
- Decisions
- Evidence
- Tests
- Operation
Explicit responsibilities. Actionable milestones. Tests.
Decision points and corrective-action paths.
Cybersecurity Gave Me a Front-Row
Seat to Transformation
Many see Dan as “the cybersecurity guy.” That work put him where technology,
people, responsibilities, risk, and management expectations had to change together.
Software / systems
→Automation, source control, builds, testing,
technical responsibilities and operational handoff.
Government / mission
→Architecture, roles, instructions, authorization,
evidence and staffing for mission operations.
Risk & security
→Processes, tools, responsibilities, workflows,
reporting and management decision visibility.
Cloud
→A global consulting capability: workforce preparation,
delivery methods and reusable assets across countries.
AI
→Locate shifts in human tasks, decisions, validation
and oversight before the operating model is under load.
and people already inside the organization who can carry the new model forward.That is the experience behind his approach to AI.