• s these tools become more capable, many organizations hope they will spark higher levels of creativity, enabling employees to generate more novel and impactful ideas.
• A recent Gallup survey found that only 26% of employees who use generative AI report improvements in their creativity.
• We find that generative AI can indeed boost employee creativity, but the gains are not universal. Specifically, employees with stronger metacognition—the ability to plan, evaluate, monitor, and refine their thinking—are more likely to experience creative gains from using generative AI,
• to unlock AI’s potential for boosting workplace creativity, organizations must go beyond simply rolling out new tools; they also need to invest in developing employees’ metacognition and promote the thoughtful, strategic use of AI so employees can translate AI outputs into more effective creative performance.
• Employees produce more creative ideas when they have sufficient cognitive job resources.
• These resources include two key elements: a) information and knowledge, and b) the opportunity to adjust work methods and tasks, such as switching between complex and simple tasks and taking mental breaks.
• First, by expanding knowledge: Although employees’ own knowledge is limited, generative AI can provide large amounts of information within seconds. This expands employees’ knowledge base and enables them to integrate insights across domains. Second, by freeing mental capacity: When generative AI handles tasks such as summarizing texts, managing data, and drafting content, it reduces employees’ cognitive overload, allowing them to redirect resources to complex problem-solving.
• However, access to AI tools alone does not guarantee that employees can acquire the cognitive resources needed for creativity
• For example, employees with strong metacognition usually think through the steps to perform a task, keep track of how effective their approach is, and adjust when they notice a lack of progress.
• By contrast, employees low in metacognition are more likely to accept AI’s first answer, rely on default outputs, and fail to check whether AI’s suggestions are accurate or relevant.
• To examine these ideas in real work settings, we conducted a field experiment with 250 employees at a technology consulting firm in China.
• Employees with stronger metacognition became more creative when they used AI—they generated ideas that were judged as more novel and more useful.
• Generative AI does not automatically make employees more creative. What matters is whether employees have the metacognition to use AI in a reflective way.

• 1. Help employees use AI to expand the cognitive job resources that fuel creativity.

   

  Generative AI can enhance employees’ creativity by expanding their access to information and knowledge and freeing up their mental capacity for creative problem-solving. Leaders should encourage employees to use AI to gather diverse information, explore multiple angles, and offload routine tasks to restore cognitive capacity.

• 2. Raise awareness that metacognition is the engine of AI-supported creativity.

   

  Leaders might assume that integrating generative AI into their workflows will automatically make all employees more creative. Yet our research shows that creative gains tend to occur among employees who can actively monitor their own thinking and then evaluate, question, and refine AI outputs.

• 3. Build metacognitive skills through targeted and scalable training.

   

  Leaders should consider employees’ metacognitive abilities when implementing AI and invest in developing these abilities through training. Notably, metacognitive skills can be strengthened through various methods. Companies can offer short training sessions that introduce metacognition and walk employees through real examples of AI errors, asking them to anticipate, detect, and correct those mistakes.

• 4. Design workflows that promote active, iterative engagement with AI.

   

  Leaders should design workflows that position AI as a thinking partner rather than a shortcut. Instead of encouraging employees to use AI for quick answers, leaders should establish processes that involve generating multiple perspectives, comparing and critiquing AI outputs, and refining ideas across several rounds.

• Caveats and Limitations

   

  When applying these insights, leaders should consider several limitations. First, our findings are based on a single organization in China. Although the underlying mechanisms are likely to generalize, people’s attitudes toward AI may vary across countries and industries.
• Second, other personal traits—for example, motivational traits such as a strong desire to learn or to pursue ambitious goals—may also influence how effectively employees engage with AI to enhance creativity.
• hird, our study examined short-term effects within a single week. The long-term consequences of sustained AI use remain open questions.
• In sum, our research shows that generative AI can meaningfully enhance creativity—but only for employees with strong metacognition. By pairing AI deployment with deliberate support for metacognitive thinking, organizations can unlock deeper insights, accelerate innovation, and ensure that employees drive the tool rather than letting the tool drive them.
• As generative AI becomes woven into global workflows, cultivating employees’ metacognition will be what separates organizations that are merely adopting AI from those that are truly unlocking its creative power.

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• Productivity improves in pockets of the organization. Yet, somewhere between year one and year three, momentum fades.
• Transformation programs quietly stall, not because the technology failed, but because the organization never evolved how decisions were made,
• Most transformation discussions focus on what to implement. Fewer address how the organization itself must change to sustain that implementation.
• The operating model defines how work actually gets done. It includes decision rights, funding mechanisms, accountability structures, performance incentives, and governance rhythms.
• In many enterprises, digital initiatives are layered onto legacy operating models designed for stability, predictability, and functional silos.
• The result is friction. Teams are asked to move faster, but approvals remain slow. Leaders want agility, but funding cycles are rigid.
• Initial automation or AI initiatives often target contained problems. They improve a single workflow, optimize a specific function, or enhance a narrow customer interaction. These efforts succeed because they operate within existing structures and require minimal organizational change.
• Leadership interprets this success as proof that the transformation strategy is working, but scaling is different from piloting.
• Without explicit changes to how decisions are made and who owns outcomes, transformation stalls.
• In mature digital organizations, decision rights are clear.
• Technology teams build solutions, but business leaders retain final authority. Data teams generate insights, but operational leaders decide whether to act on them.
• AI models make recommendations, but no one is accountable for trusting or challenging the output.
• This ambiguity creates hesitation. Teams defer decisions upward. Leaders delay action. Innovation slows.
• Many enterprises still fund transformation initiatives as projects rather than capabilities. Budgets are approved annually, tied to fixed scopes and predefined deliverables
• Digital capabilities, however, require continuous investment.
• Leaders often underestimate how deeply funding mechanisms shape behavior. Teams optimize for project completion rather than long-term impact
• Cross-functional initiatives involve multiple stakeholders, each with partial responsibility but no clear ownership.
• When outcomes disappoint, accountability fragments. Issues are attributed to data quality, user adoption, integration complexity, or regulatory constraints.
• This diffusion of responsibility is one of the most damaging effects of a misaligned operating model.
• process optimization alone cannot overcome structural misalignment.
• True transformation requires aligning process excellence with operating model evolution.
• First, leaders must explicitly define decision ownership for digital capabilities.
• Second, funding models must shift from project-based investment to capability-based investment.
• Third, accountability mechanisms must be strengthened, not diluted, as complexity increases.
• Digital transformation is often described as a journey. In practice, it is a discipline.
• The difference is not ambition or investment. It is whether leadership is willing to redesign how the organization works, not just what tools it uses.
• The true test of digital leadership is not whether an organization can adopt new technology. It is whether leaders can evolve the operating model to support continuous change.
• Technology enables possibility. Operating models determine whether that possibility becomes performance
• Digital transformation does not break because leaders lack vision. It breaks because the organization remains designed for a world that no longer exists.

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• Full utilization1 means trying to keep people busy close to 100% of the time.
• In many kinds of modern work, it quietly produces the opposite: long delays, missed commitments and a constant sense of urgency without real movement.
• Organizations don’t succeed or fail on effort alone.
  They succeed or fail on flow2.

• Somewhere between planning meetings and delivery dates, many organizations lose sight of something simple:

  Customers don’t see your internal effort.
  They experience waiting.
• A system can look full and still move slowly.
  Sometimes because it’s full.

• Most lead time isn’t spent on actual work.
  It’s spent waiting.

  Waiting for decisions.
  Waiting for handoffs.
  Waiting for capacity.
  Waiting for “the right moment.”
• You can perfect every step and still disappoint customers
  if most of the time is lost between them.

• There’s a simple relationship that shows up almost everywhere work moves through a system:

  Lead Time3 = Work in Progress4 ÷ Throughput5

• The more unfinished work you allow into the system, the longer it takes for anything to reach the customer.
• Little’s Law doesn’t tell you where time is lost.
  It tells you why it accumulates.
• Not because engineers are slow.
  But because the work has to move through builds, tests, iterations, suppliers and external constraints.

• When you look closely, those 18 months break down unevenly.

  Actual working time: roughly 6 months
  Designing hardware, writing firmware, bringing up boards, debugging, testing, fixing, integrating.

  Waiting and idle time: roughly 12 months
  Waiting for parts.
  Waiting for build slots.
  Waiting between EVT, DVT and PVT.
  Waiting for test results, approvals, lab availability, supplier lead times and manufacturing readiness.

• You reduce the time work spends sitting idle between steps:

  • fewer parallel initiatives competing for the same labs and decision-makers

  • clearer prioritization between hardware and firmware work

  • faster decisions between builds

  • earlier supplier and part choices to avoid blocked builds

• A project that’s “almost done.”
  A decision that just needs one more alignment.
  A release that slips by another week (again).

• In practice, throughput in knowledge-heavy work is often capped by things that don’t show up on a plan or org chart:

  • limited attention.

  • coordination overhead.

  • context switching.

  • dependence on other people’s availability

• Real work is uneven by nature:

  • some tasks are quick, others messy.

  • work arrives in waves.

  • priorities shift mid-stream.
• High utilization works best when work is standardized, predictable and heavily buffered.

• Here, waiting doesn’t look like a queue.
  It looks like stalled projects, delayed releases and work that is “almost done” for weeks.

  Applying the same utilization logic without the same buffers usually backfires.

• “Idle time is waste.”

  It sounds sensible.
  It’s also misleading.

  Slack8 isn’t about comfort or perks.
  It’s what allows a system to stay stable when reality refuses to cooperate.

• You don’t need formulas to recognize it.

  • everything is urgent

  • simple tasks wait for “the right moment”

  • planned work gets displaced by the latest escalation

  • projects start easily and finish painfully

  • people are busy, yet outcomes arrive late

• In practice:

  • limit what counts as active.

  • finish before starting more.

  • make queues visible.

  • protect slack deliberately.

  • pay attention to time, not just effort.

• • List what’s actively in motion

  • Notice what’s actually moving

  • Pause the rest (explicitly)

  • Watch one signal: time to market (or time to completion)

• Full utilization feels responsible.
  In many kinds of work, it’s a slow-motion failure mode.

• Ask something more uncomfortable:

  What are we starting that we don’t need to start yet?

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• Erreur #1 : Les valeurs définies (uniquement) par la direction

      
• La direction les a définies tout seule, parfois avec les RH, avec l’aide d’un consultant marketing. Résultat ? Personne ne se les approprie.

• Erreur #2 : Des valeurs sans contexte ni action

      

  « Innovation » OK, mais qu’est-ce que cela veut dire quoi concrètement ? Quelles activités, dans mon comportement, expriment le mieux cette valeur ? Quels sont les contextes où cette valeur doit s’exprimer en priorité ?

• rreur #3 : Le leader ne les incarne pas

      

  J’ai vu des CEO prêcher la « transparence » puis refuser de partager les datas ou informations clés avec les équipes. J’ai vu des managers valoriser « l’autonomie » puis valider chaque décision de leurs collaborateurs.
• chaque valeur est décrite en termes de comportements et compétences au travers de leurs BlaBlaPrinciples.

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• The disconnect between AI adoption and business results has reached a critical point. While technology deployments accelerate and employee enthusiasm remains high, organizations continue to struggle with extracting meaningful productivity gains or measurable ROI from their AI investments.
• 65% are excited about using AI at work and 77% take training when it’s offered. Among those using AI tools, 62% report time savings, with employees in AI-relevant roles saving an average of 1.5 hours daily.
• 87% of talent acquisition pros use AI tools daily or weekly, with more than two-thirds rating themselves as highly familiar with the technology.
• that 83% scored within the lowest two categories, indicating middling achievements with the technology. Less than 1% reached high intelligence maturity levels and only 5% achieved high automation maturity.
• 8% of retail organizations still lack advanced automated screening despite operating in high-volume hiring environments.
• “The question every CHRO should be asking their team is: How fast can they get AI to work for their business?”
• AI policies, or the lack thereof, are a consistent hot spot in these studies. Organizations with documented governance frameworks report dramatically better outcomes
• . Among organizations with formal AI policies, 82.5% of respondents report high confidence using AI responsibly.
• The benefits of formal policies extend beyond confidence. Organizations with documented AI governance show higher daily or weekly usage rates and are more likely to increase AI budgets in the coming year.
• only 37% of surveyed organizations have formal AI policies. Another 36% rely on informal guidelines and 15% have no policy at all.
• AI deployment decisions are often made without HR involvement, leading to poor adoption and misaligned expectations between employees and executives.
• 88% say their organizations have not realized significant business value from AI tools
• 47% are investing in training and 41% have implemented enterprise licenses or AI use policies. However, only 3 out of 10 report that specific workgroups are actively applying AI tools to improve workflow processes.
• Organizations need better integration with existing HR systems, clear governance and usage guidelines, stronger fraud detection capabilities, more transparency into AI outputs and workflows that support human judgment rather than attempting to replace it, according to the Elly.ai research.
• The Phenom report identifies five key opportunities for organizations to enhance outcomes: automating hiring workflows to reduce time-to-hire; deploying automated interview scheduling to boost candidate conversion; leveraging AI matching to improve quality-of-hire; applying real-time intelligence to increase interview effectiveness; and embracing AI infrastructure that addresses unique industry and business needs.
• dividual productivity gains as workers experiment with AI; workgroup productivity as teams embed AI into workflows; and organizational productivity through comprehensive digital transformation
• identifies workforce transformation and talent management as one of five major risk trends alongside AI governance, catastrophe risks, supply chain disruption and global resilience.
• . It appears that success with workplace AI will not come from additional technology purchases or renewed enthusiasm campaigns. It will come from building the governance frameworks, integration strategies and change management processes that the current wave of AI adopters largely skipped.

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• put together a short biweekly report, made up of a direct update from every team, that they could scan.
• since most teams were already doing the underlying update work for internal team reasons, but because, for many people, it signaled micromanagement.
• ​I could sympathize with the team’s concerns. If the intent was to avoid real conversations, then the pushback was warranted. If the report were used to nitpick details or hound middle managers, that would also be a problem
• can only have so many direct conversations, and I don’t want to walk into those based on second-hand information or outdated context. This keeps me up to speed so that when I do talk with teams, the conversation is actually useful. It’s not a replacement for that at all. And it is not a replacement for me talking with my direct reports and skip levels.
• ​What was really being resisted (I think) was the idea that a senior leader might stay continuously exposed to the messy reality of the organization without first having it filtered, summarized, and made safe
• What would be so wrong with a CPO maintaining a direct line of sight into the work?

• When Peter Drucker originally described management by objectives (MBO), the overlap with mission command was real:

  1. Focus on outcomes, not tasks

  2. Clear intent instead of micromanagement

  3. Decentralized execution

  4. Trust that people closest to the work decide how

  5. Judgment over compliance. Objectives guide thinking, not replace it
• Drucker’s original conception of MBO was closer to mission command than many later interpretations suggest. He described objectives as “direction,” emphasized decentralization as a way to extend leadership responsibility (rather than to shirk it), and grounded management in judgement.
• In mission command, intent is meant to be directional, and success is contextual. Judgement remains at the forefront. In MBO, targets often become contracts.
• In mission command, leaders are expected to remain continuously exposed to unfolding reality. Feedback is frequent and direct, and is used to tune intent in real time. Escalation in mission command is routine, expected, and safe. It’s not reserved for emergencies
• In mission command, accountability is grounded in trust and judgment. In MBO, it often turns into a reporting exercise.
• In tech, we throw around words like “empowerment” and “agency,” but they’re framed primarily in terms of individual autonomy within a theoretically meritocratic system, not in terms of shared doctrine, operating mechanisms, or collective discipline.
• They espouse empowerment, ownership, and accountability, but don’t have time for practice, or “work around the work.” They ask for loyalty, yet people are often in and out before they really need to own the results of their work. They talk about “relentlessly experimenting on behalf of our customers,” but fail to turn that spirit inwards. They frown on anything that smacks of politics, bureaucracy, or process, but simultaneously rely on rigid filtering and implicit rules about escalation that make it unsafe to surface problems early.
• You’ll often find companies with five to seven layers of management, but none of the cultural reinforcement those layers assume.
• Considering all of this, maybe the team’s pushback was warranted. They were correctly sensing that, in most tech organizations, the conditions for this kind of exposure simply aren’t in place. Without shared doctrine, safe escalation, etc., direct visibility feels like a threat.

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•  

  Intelligent automation is often judged by whether processes continue to run. Bots execute. Models produce outputs. Work flows from one step to the next. On the surface, everything appears to function as promised. Yet many organizations walk away disappointed.
• Productivity gains fall short of forecasts. Decision cycles remain slow.
• When automation underperforms, investigations usually focus on execution mechanics.
• In many cases, automation does exactly what it was designed to do.
• The breakdown happens afterward, when organizations struggle to convert automated output into timely, coordinated decisions that drive outcomes.
• If processes were truly broken, work would stop. What usually stops instead is value.
• Value realization is not an automatic byproduct of automation. It depends on organizational conditions that often receive far less attention than tools or architectures.
• Decision authority must be clear. Someone must be empowered to act on what automation reveals
• Across automation programs, the same patterns appear repeatedly. Automated systems surface exceptions faster than leaders can respond. Insights arrive, but ownership is unclear. Escalation paths exist, but only on paper. Governance forums are designed for stability, not speed. Benefits are tracked locally while costs accumulate across the enterprise.
• None of this prevents a process from running. All of it prevents value from materializing.
• A core reason for this breakdown is a mismatch between system maturity and organizational maturity.
• . Processes now execute with a speed, consistency, and scale that were previously unattainable. Operating models, however, often remain anchored in slower, consensus-driven decision structures designed for a different era.
• The result is friction. Systems move quickly. Organizations hesitate. Value accumulates in theory but dissipates in practice.
• As programs scale, those conditions fade. Ownership fragments across teams. Governance layers multiply. Decisions route through committees. Metrics expand without clarity on who is accountable for outcome
• Many organizations still evaluate intelligent automation through the wrong lens. Adoption rates, bot counts, and cycle-time improvements dominate reporting. These measures say little about whether automation is actually improving performance.
• who is accountable for converting automated execution into business outcomes? If the answer is unclear, value will remain elusive regardless of technical success.
• In this environment, the limiting factor is no longer what systems can do. It is what organizations are prepared to do with what systems already deliver.
• Those that treat automation as a value system challenge, rather than a process challenge, will pull ahead.
• Intelligent automation rarely fails because processes stop working. It fails because organizations are not designed to capture the value automation makes possible.
• The future of intelligent automation will be shaped less by advances in capability and more by whether organizations can redesign themselves to realize the value those capabilities already provide.

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• If 2023 was the year of AI’s awakening and 2024 the year of its frantic adoption, then it’s safe to say 2025 will be remembered as the year it all came crashing back down to reality.
• increased cancer risk and hostile takeovers of local infrastructure, not to mention skyrocketing electricity bills.
• data centers powering the AI gold rush make horrible neighbors.
• AI is helping corporations increase the rate at which they exploit workers — both on the job and at home.
• he hatred is so palpable that Americans have even taken to accusing human customer service agents of being AI when they don’t get their way.
• With newfound tools to pump out ultra-realistic content at an industrial scale, Facebook scammers, art forgers, and racist influencers are also getting in on the fun.
• Pause AI, a group calling for a halt of AI development until we can figure out what the hell is going on. 2025 even gave us our first anti-AI hunger strikes, spontaneous protests by activists in San Francisco and London.
• Vermont Senator Bernie Sanders, for example, recently kicked off a campaign calling for a pause on the “unregulated sprint to develop and deploy AI.
• who have defied their own Republican party in voicing opposition to AI initiatives.
• “I’m not buying the narrative that they are trying to sell us on this,” DeSantis said at a recent roundtable on AI, according to the Washington Times.

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• Change management is a systematic approach to guiding individuals, teams, and organizations from their current state to a desired future state. It focuses on preparing, supporting, and enabling people to embrace new ways of working driven by organizational changes such as the introduction of new technologies or systems, process improvements, cultural transformations, mergers and acquisitions, and leadership transitions.

• 1. ADKAR is enough

   

  Many change managers believe that the ADKAR change model (awareness, desire, knowledge, ability, and reinforcement) is enough and that ADKAR training is the hallmark of ‘you know what you are doing’ in change managemen
• Change requires context. Without that context, you miss a major piece of the change and create both adoption risk and people risk. What makes up that environment? Culture, leadership maturity, employee maturity, power dynamics, psychological safety, timing, necessity, and risk tolerance.”

• 2. Change is a project with a defined end date

   
• “Keeping an explicit and expected iteration cadence will allow teams to stabilize in their process versus the expectation of disruption due to the implementation of change.”

• 3. Change is just a communication exercise

   

• Leaders continue to believe that if you just communicate enough times people will get on board and behaviors will change.

   

  “This myth lives in the gap between formal logic (the plan, the deck, the rationale) and lived reality (the local constraints, risks, and unspoken fears people are navigating),”

• 4. People resist change

   

  “In my research on behavioral economics and change management, I consistently find that one belief quietly undermines even the best process and artificial intelligence (AI) initiatives,” says Kelly Monahan, founder of Beyond the Desk. “It’s the idea that people resist change. It is a convenient narrative for leaders, but a damaging one for the workforce. It shifts accountability away from how change is designed and toward employees themselves.”

• , she explains how fear, identity threat, and loss aversion, not resistance, are what derail adoption.

   

• 5. Change needs new leaders or systems

   

  One of the biggest change management myths is the belief that transformation comes from swapping in new leaders or systems, says Scott Estill, managing partner at Lancor
• “In reality, over 90 percent of the work that drives real change is done by the same people who were already there, they just need clearer direction, faster feedback, and early wins to believe it’s possible.”

• 6. Change is a process problem

   

  Another common myth is the notion that change is primarily a process problem, rather than a human one, according to Helen Millar, founder of Hearty.
• he assumption is that if the strategy is sound, people will follow. In reality, resistance rarely comes from a lack of logic, it comes from fear, loss of identity, and a lack of trust.”
• , this myth is becoming even more costly. The pace of change is outstripping leaders’ emotional readiness to bring people with them.
• “Sustainable change only happens when leaders prioritize emotional intelligence, self-awareness, and genuine connection, not performative communication or compliance-driven engagement,”

• 7. Culture will follow structure

   

  “Absolutely not!” says Hulbert. “Culture only comes from taking sustained and deliberate decisions in line with a defined culture. Structure doesn’t deliver it – leadership and decision-making does.”
• change always brings fire drills! Crisis comms preparation, practice, and governance is essential – it will help protect the hard-won trust and your employer brand during the challenging times.”

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• We now have a few studies that point to good but not great marginal efficiencies when using Generative AI inside existing business structures and management systems to speed up existing tasks.

• the transformational potential of agentic AI depends upon readiness efforts in areas from technical and data infrastructure to organisation mapping, process discovery and explainability, and in particular, local training and context management.
• Making the rules of the road explicit is a key management activity as we progress towards programmable organisations, and this is why we include leaders as programmers
• the realisation that clearly stated high-level goals and instructions linked to all their necessary context is probably how the programmable organisation will be operated and guided in the future.
• We have argued for a long time that literate leadership (writing things down, collating knowledge and curating data) beats performative leadership (meetings, presence and a focus on simplistic decision-making
• The book begins by looking at accountability sinks - structures (rules, algorithms, or market pressures) where decisions are delegated so deeply into a system that no individual human can be held responsible for the outcome - and their role in the 2008 financial crash.
• But Davies also looks at management failings through the lens of cybernetics and specifically Stafford Beer’s Viable System Model (VSM), and how management’s reductive approach to complexity fell foul of Ashby’s Law of Requisite Variety (for a manager to control a system, their “regulatory variety” must match the “variety of the system” they are managing).
• By using attenuators to simplify their picture of the firm, such as share price or quarterly sales reports, managers have made the organisation blind to all the other important data and signals necessary to guide strategy
• It might seem that AI and the algorithmic era will make this situation even worse and therefore we should return to an imagined ‘good old days’ of personal, accountable managemen
• On the attenuation question, agentic AI enables us to maintain a detailed, objective picture of real-time operations within even a large, complex organisation, meaning the variety of the control system is able to match the variety of observable reality.
• Anybody who has seen an important, expert-designed project or proposal for action reduced to kindergarten images and bullet points so that a senior leader with the attention span of a goldfish can “decide” will surely welcome the fact that we can summarise without attenuation.
• Agentic AI can also help eliminate accountability sinks in several ways. For example, processes and rules of the road no longer need to be oversimplified to such an extent that people can be trained to follow them.
• Of course, this works only if we design our structures and rules with this in mind. The machine is only as good as what it ‘cares’ about. I

• “The purpose of a system is what it does.” - Stafford Beer
• AI critics such as the wonderful Cory Doctorow argue that AI is being sold on the promise of job cuts and cost savings, and that none of the surplus it produces will be returned to workers:
• for the first time in 22 years, work-life balance has overtaken pay as the most important job factor, according to Randstad’s 2025 Workmonitor report
• But at the same time, we have the opportunity to reshape those jobs that are not at risk through finding creative and human ways to combine people and AI.
• people are starting to explore how to quantify human-AI synergy to find the optimum collaboration.
• But let’s not pretend that this will not be very disruptive for many people’s lives, especially in a world where housing has become an asset class rather than a basic right. This will need a policy response as well as businesses taking some responsibility for the gravity of the shift away from full employment as a realistic goal.
• I already advise my mentees only to consider taking a job as a stepping stone to better things, and to avoid getting trapped in employment whilst developing their own independent income sources;

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