Human + Machine
One book, placed in its field — what this book gets right, where it goes further than the rest, and what the rest of the field adds.
Mike West, Editor · Leading With Data · peopleanalyst.com/guides/profile-human_machine
Arrow keys or swipe to move · N for notes · F for full screen
Human + Machine
One book, placed in its field — what this book gets right, where it goes further than the rest, and what the rest of the field adds.
Mike West, Editor · Leading With Data · peopleanalyst.com/guides/profile-human_machine
The problem
AI is rapidly transforming business, but it is unclear how to apply it for breakthrough results rather than stalling automation.
Leaders feel the topic of AI is intimidating, confusing, and threatening, fearing job displacement and being left behind by competitors.
Human + Machine · Mike West, Editor · Leading With Data
The model
- 01Reimagining Mindset
- 02Experimentation Culture
- 03Responsible AI Leadership
- 04Data Supply Chain
- 05Fusion Skills
- 06Missing-Middle Collaboration Roles
- 07Human Trust in AI
- 08Augmented Human Capability
Human + Machine · Mike West, Editor · Leading With Data
Step 1 of 5 · Reimagining Mindset
Judge an AI initiative by whether it changes how work is organized, not by how many tasks it automates.
Misconception
If we automate our existing processes well and cut the cost of each step, we are already doing what AI transformation requires.
Reality
Automating a process you inherited fixes its assumptions in place. The mindset shows up when leaders ask what the process would look like if people and machines were designed in from the start, and accept that the answer may eliminate the process itself.
Human + Machine · Mike West, Editor · Leading With Data
Step 2 of 5 · Experimentation Culture
Count experiments that reach a scale-or-kill decision, not experiments launched.
Misconception
Experimentation means running a lot of AI pilots, so a long list of proofs of concept shows we have the culture.
Reality
Pilots that never reach production or never get measured are only activity. A real culture ties each test to a hypothesis, a metric, and a decision to scale, change, or kill it, and treats a well-run failure as an asset.
Human + Machine · Mike West, Editor · Leading With Data
Step 3 of 5 · Responsible AI Leadership
Assign a named executive to every AI process, with the authority to stop it.
Misconception
Responsible AI is a risk and legal function that reviews models at the end, so leaders can leave it to compliance.
Reality
Accountability for outcomes sits with executives because process design decides who is exposed when AI errs. Guardrails built into the design from the start shape whether people trust the system, and so how much value the system produces.
Human + Machine · Mike West, Editor · Leading With Data
Step 4 of 5 · Data Supply Chain
Manage data by its delivery to a decision, not by volume stored.
Misconception
A centralized data lake or warehouse is a data strategy, so once data is stored in one place we are ready for AI.
Reality
Storage is only one link. Value depends on whether the right, clean, current data reaches the process at the moment of decision, and that requires continuous capture, integration, and curation owned like a supply chain.
Human + Machine · Mike West, Editor · Leading With Data
Step 5 of 5 · Fusion Skills
Prioritize the skills your next process redesign depends on rather than training all eight at once.
Misconception
Workers need technical AI literacy, such as learning to code or prompt, and then they can collaborate with machines.
Reality
Fusion skills are mostly about judgment and interaction: questioning outputs, deciding when to override, redesigning one's own work, and teaching the machine. Technical fluency helps but does not supply them.
Human + Machine · Mike West, Editor · Leading With Data
A framework to use
Augmented Human Capability
This framework is part of the full guide. See the guide →
Human + Machine · Mike West, Editor · Leading With Data
The cheat sheet
- Reimagining MindsetJudge an AI initiative by whether it changes how work is organized, not by how many tasks it automates.
- Experimentation CultureCount experiments that reach a scale-or-kill decision, not experiments launched.
- Responsible AI LeadershipAssign a named executive to every AI process, with the authority to stop it.
- Data Supply ChainManage data by its delivery to a decision, not by volume stored.
- Fusion SkillsPrioritize the skills your next process redesign depends on rather than training all eight at once.
- Missing-Middle Collaboration RolesDesign for mutual amplification, not task allocation.
- Human Trust in AITrust comes from transparency, agency, and fair accountability, not accuracy alone.
- Augmented Human CapabilityLook for capabilities that did not exist before, not only faster tasks.
Human + Machine · Mike West, Editor · Leading With Data
Sources
- Human + Machine
Human + Machine · Mike West, Editor · Leading With Data