A Different Sequence
Piece 6 of 7 · The Human Thesis
Five pieces ago, I opened this series with a sentence that has been carrying weight over the last six weeks. We are building the Titanic in plain sight. Then four witnesses. Véliz on prediction. The Amodeis on belief. Brynjolfsson on the economics. And then last week, the view from inside the rooms where real work is happening.
The case is built. The wounds are named. The verdict is in.
This week is different. This week I am going to offer you something to do.
What I am about to walk through is the operating model my co-founder Jason Averbook and I have been building at Now to Next, studied across hundreds of transformations, over years of watching what works and what doesn’t. It’s not new. It wasn’t invented. We didn’t sit in a conference room and brainstorm it. We watched it. We watched the same gaps appear in engagement after engagement, in industry after industry, at every scale — and we designed a sequence to fill the gaps we kept finding.
The sequence is called 4SET. The first time you read it, it will look obvious. The second time, you might note it’s in the opposite order from how most organizations are running it. The third time — usually after you have tried to apply it inside your own organization — you will realize that the order is the entire point, and that getting the order right is harder than it looks.
Let me show you.
Most organizations start at the toolset and work backward.
Here is what happens in nearly every enterprise AI initiative right now. The board approves an AI strategy. The CIO scopes a tool stack. The CTO leads vendor evaluation. The CHRO is asked to build an enablement workstream. The Chief AI Officer — if there is one — translates the strategy into pilots. Training programs roll out. Champions are appointed. Adoption metrics get tracked. Quarterly business reviews celebrate the percentage of employees who have logged into the new tools at least once.
Somewhere down the list, in the appendix slide of someone’s deck, there’s a reference to culture or mindset or change leadership. It rarely has a budget attached. It rarely has an owner.
That’s not an accident. Ownership defaults to whoever already controls the budget line — usually IT, or procurement, or whichever function owns the infrastructure spend. It is rarely whoever should own the transformation. This isn’t a strategy failure. It’s a default nobody decided on, and it’s why toolset ends up first even when everyone in the room agrees it should be last. It’s the workstream everyone agrees is important and nobody actually does, because the operational pressure is on the tools and the tools are where the visible activity lives.
This is the standard sequence. Toolset first. Skillset second (training people on the tools). Heartset third, sometimes, in the form of engagement surveys and town halls. Mindset last, if at all, usually as a reactive measure when adoption has stalled and someone needs to figure out why.
The standard sequence produces standard results. License utilization in the low double digits. Pilot pile-up without scale-up. The ROI gap your CEO can feel but cannot name. The Five Wounds your workforce is carrying but cannot articulate. The 3.4 readiness score we walked through last week.
The standard sequence doesn’t fail because the people running it are incompetent. It fails because it is structurally backwards. You are trying to install behavior before you have built the belief that produces the behavior. You are training people on tools they do not yet trust. You are asking them to embody a future they have not yet been invited to imagine. You are demanding adoption metrics from a workforce that has not been given the conditions for embodiment.
The standard sequence is what we keep telling our clients to interrupt. Here is what we tell them to do instead.
The 4SET sequence.
Four words. They look simple. The order is everything.
Mindset. Heartset. Skillset. Toolset.
We start at mindset and work forward. The reasoning is structural, not sentimental. Each step in the sequence builds the conditions for the next. Skip a step and the steps that follow do not hold. Run it backward — which is what most organizations are doing right now — and the foundation is never there.
Let me walk you through each step.
Mindset.
This is the layer most enterprises skip entirely. It is also the layer everything else rests on.
Mindset is how you see what is possible with AI, and I’m not talking about another Art of the Possible workshop or prompting party. Those have their place, but this refers to openness, curiosity, agency. Not training. Not enablement. Not a mandatory module in the learning management system. The actual cognitive posture a person brings to the work.
Two people can be looking at the same AI tool: One sees a threat to their job. The other sees an instrument that frees them from work they never liked or gives them a thought partner where they really needed one. Same tool. Same context. Same training. Completely different mindset. The first person will resist adoption no matter how good the enablement is. The second person will run ahead of the rollout. Mindset is what determines which one shows up in your workforce, and it is upstream of every other variable you are trying to manage.
Ask any workforce what it wants first, and the answer is almost never a tool. It’s almost always the same request: help me understand what this actually is, what it means for my work, what I’m allowed to do with it. That’s a mindset request. Enterprises hear it and route it to a training module anyway.
Mindset work is not motivational speaking. It’s not poster slogans. This isn’t the CEO video at the all-hands. This is the slow, deliberate work of helping people examine the beliefs they’re bringing to AI — and giving them the time, space, and permission to update those beliefs.
This is what most organizations cannot stomach. Mindset work doesn’t show up on a dashboard. It doesn’t produce a neat quarterly metric. It doesn’t have a deliverable that can be presented to the board. It is also the single highest-leverage investment in the entire sequence, because every step that follows is multiplied by the mindset that precedes it.
The irony is that most organizations interview for mindset — growth mindset, curiosity, adaptability — and then never come back to it once the hire is made. The work is not unfamiliar. It is unpracticed at scale. In fact, every system we build is designed to measure the outputs of mindset rather than the mindset itself.
Skip mindset and you spend the next three years working twice as hard to produce half the outcomes.
Heartset.
If mindset is how you see what is possible, heartset is what you believe about yourself in an AI-augmented workplace.
Heartset is confidence. Belonging. It’s the answer to the question, do I still matter here? It is the felt sense of safety that allows a person to bring their full capability to the work, to make mistakes without fear of being replaced, to learn out loud without losing standing.
Heartset is where the Five Wounds live. Fear of Replacement is a heartset wound. Loss of Mastery is a heartset wound. Identity Disruption is a heartset wound. The workforce is not going to embody a new way of working while it is bleeding from five places nobody is treating.
In the actual rooms where this work happens, the ratio is almost never balanced. When people are finally given room to say what they’re feeling about AI, most of the time in the room goes to naming the fear, not building the skill. Not because people are fragile. Because the fear has nowhere else to go until someone opens the room for it.
This is also the layer most enterprises confuse with the engagement survey. The engagement survey measures heartset retroactively, at the lowest possible resolution, with questions designed to be answered politely. Heartset work is what produces the conditions that would make those survey scores rise in the first place. The work is direct. It is relational. It happens in conversations, in offsites, in the small moments where a leader names the wound out loud and gives the person carrying it permission to set it down.
And heartset, done right, is not a feelings exercise. The organizations that take it seriously build it the way they’d build any other system: visible guardrails, stated boundaries on what AI will and won’t decide, a clear answer to who’s accountable when it’s wrong. Trust isn’t a mood you improve. It’s infrastructure you design.
Heartset is what the bleeding-edge organizations we described last week were trying to build. The champions networks. The communities of practice. The psychological safety work. They were doing real heartset work and not yet getting to embodiment, because the mindset layer underneath was incomplete and the skillset layer above was being scaled too fast.
The order matters. Mindset opens the door. Heartset walks through it. Without the door open, no amount of relational work lands.
Skillset.
Now we are in the layer most enterprises actually invest in. This is where the training programs live. This is where the prompt engineering certifications live. This is where the AI literacy curriculum lives.
But skillset, in our sequence, means something more specific than tool training.
Skillset is the judgment, creativity, and relational capabilities that remain distinctly human as AI takes over more of the routine work. It is the ability to verify what AI produces, to challenge it when it is wrong, to know when to use it and when to put it down. It is the discernment to recognize that the polished output is workslop, the courage to flag it, and the skill to do the work properly without becoming dependent on the tool.
The most underrated skill in an AI-augmented workplace is the ability to think without the AI. Not as a refusal of the technology. As the underlying capability that lets the technology actually amplify your work instead of dissolving it. The MIT cognitive debt finding I cited two weeks ago is what happens when skillset gets confused with toolset. People become operationally fluent and cognitively atrophied. The skillset layer is what prevents that.
Vivienne Ming’s recent research on human-AI collaboration gives this a name and a number. She ran an experiment putting people through real prediction tasks with AI, EEG monitors attached, and found three distinct patterns. Most people handed the problem to the AI outright — their brain activity on the task dropped to something closer to watching television than working. A second group used the AI only to confirm what they already believed, and performed worse than the AI operating alone. A third group, a small one, stayed in genuine back-and-forth with the tool: pushing, questioning, refusing the easy answer. That group beat the best humans working alone and the best AI working alone, and it barely mattered which AI model they used. What predicted the outcome was the human, not the tool.
Ming calls that third group cyborgs. We’d call it skillset. Same finding, different vocabulary.
Skillset work is also where Hands, Heads, and Hearts come into play — and I want to spend a beat here because this is the second framework that lives inside the sequence.
A second framework, briefly. Hands, Heads, Hearts.
The 4SET sequence is one framework. There is a second one that lives alongside it, doing different work on a different object.
4SET operates on capability. What you build in your workforce, in what order. Mindset, heartset, skillset, toolset. The sequence is about the people doing the work.
Hands, Heads, Hearts operates on the work itself. What AI does — and does not do — across three layers of every job. The framework is about how work gets designed, not about how the workforce gets built.
The architecture is three verbs mapped to three layers.
Hands — automate execution. This is the layer AI is replacing. The routine work, the administrative work, the documentation work. The tasks that used to take hours and can now take seconds. AI is not assisting here. AI is doing the work. The hands of the workforce are being freed for something else, and the question of what that something else is is the entire point of getting the framework right.
Heads — augment thinking. This is the layer AI is changing without replacing. Analysis. Insights. Decisions. AI is not thinking for humans here. AI is making humans better at thinking — faster pattern recognition, broader information synthesis, more rigorous testing of conclusions. The heads of the workforce are still doing the work. The work is just better because the AI is in the loop. (I refuse to say humans in the loop. So punk, I know.)
Hearts — amplify purpose. This is the layer AI cannot touch. Trust. Judgment. Meaning. Connection. The work humans do for reasons AI does not have. The hearts of the workforce are not being automated and not being augmented. They are being amplified — because as routine work compresses and analytical work accelerates, the relative weight of the work that requires a human heart goes up, not down.
This is where trust goes to die.
The substitution-versus-augmentation choice from three weeks ago — the Turing Trap, the headcount KPI, the Dorsey path versus the Palsule path — does not actually get made in a strategy deck or a board meeting. It gets made here, at the Hands, Heads, Hearts layer, in a thousand small decisions about what AI does to which part of which job. Are we automating this hands work, or augmenting the worker who does it? Are we augmenting the thinking at the heads layer, or replacing it with model output and calling it efficiency? Are we amplifying the hearts work, or hollowing it out by removing the relational labor underneath?
Most enterprises never make these decisions explicitly. They buy tools, deploy them, and let the substitution-or-augmentation outcome emerge from whatever the vendor scoped. The aggregate of those un-made decisions is the substitution choice the entire workforce ends up living with, made by no one in particular, communicated through silence, and absorbed by the people who carry the wounds.
This is the mechanism behind every trust failure we are seeing inside enterprise AI rollouts right now. Lack of trust is really a lack of transparency. Lack of transparency is really a lack of decision-making at this layer. The workforce’s trust does not collapse because leadership lied. It collapses because leadership never decided, and the undecided choice was communicated as silence, and the silence was read as the worst-case interpretation. Trust dies in the absence of a stated intent.
The Hands, Heads, Hearts framework is the place where the decision can finally be made visible. Making it visible is what produces trust. Refusing to make it is what kills trust before the rollout even starts.
Automate. Augment. Amplify. Execution. Thinking. Purpose. Three layers of every job, three different relationships with AI, three different design questions for the people redesigning the work.
The two frameworks run in parallel. 4SET builds the capability of your workforce in the right sequence. Hands, Heads, Hearts redesigns the work itself across the three layers and the three relationships. Most enterprises do neither well. They invest in toolset capability while automating hands-only work, and call it transformation.
Back to the sequence.
Toolset.
Most enterprises start here. We end here.
Toolset is how effectively you design and deploy AI tools with reimagined workflows. It is the platform decisions, the vendor evaluations, the integration architecture, the use case selection, the deployment timeline. It is the layer most of the enterprise AI conversation is currently happening in. It is also, in our sequence, the last layer to do real work in — not because it doesn’t matter, but because it cannot do its work without the three layers underneath.
A toolset deployment without mindset produces resistance. A toolset deployment without heartset produces wounds. A toolset deployment without skillset produces workslop. A toolset deployment without all three — which is what most organizations are doing right now — produces the ROI gap your CEO can feel but cannot name.
When the three foundation layers are in place, toolset becomes the easiest part of the work. The workforce knows what they are for. They have the cognitive and emotional capacity to absorb new tools without anxiety. They have the judgment to evaluate the tools critically and the courage to push back on the ones that are not helping. The tools land on prepared ground. Adoption is not a metric anyone needs to track, because adoption is what happens when the rest of the conditions are right.
This is the inversion most enterprises resist. They want to start at toolset because that’s where the budget is, that is where the visible activity lives, and that is what the board is asking about. The longer they start there, the longer they spend trying to fix the consequences of having started in the wrong place.
Why the order matters.
I want to spend a paragraph on why each step has to precede the next, because the order is the entire claim.
Mindset has to come before heartset because what you believe about yourself depends on what you believe is possible. A workforce that thinks AI is going to replace them cannot feel safe inside the rollout, no matter how much heartset work you do. The mindset has to update first. Otherwise heartset is hopeless work on top of an unstable foundation.
Heartset has to come before skillset because skill-building requires risk-taking, and risk-taking requires safety. If a worker is afraid of being replaced, she is not going to expose her gaps to learn new skills. She is going to perform competence she does not have, hide what she does not know, and protect her standing rather than expand her capability. The heartset has to be there before the skillset can grow.
Skillset has to come before toolset because tool deployment without skill produces workslop and cognitive debt. If the workforce has not built the discernment to evaluate AI outputs critically, they will either over-trust the tools and produce poor work, or under-trust the tools and revert to manual workarounds. Either way the toolset investment is wasted. The skill has to be there before the tool can do its work.
This is the sequence. Each step is the precondition for the next. Run it in this order and the work compounds. Run it backward — toolset, skillset, heartset, mindset — and each step gets harder rather than easier, because you are trying to build the lower steps on top of a structure that is already shaped wrong.
Diagnostic before prescription.
One more piece, and then I will hand off.
The 4SET sequence is not a checklist. It is not a methodology to be applied uniformly across every organization. We do not show up at a client and say here is the sequence, run it. We show up and assess where the organization actually is.
Most organizations are not at zero. They have done some mindset work, somewhere. They have built some heartset infrastructure, somewhere. They have some skillset programs running. They have a lot of toolset activity. The work is not to start the sequence from scratch. The work is to identify where the gaps are, in what order, and to sequence the next investments accordingly.
This is what we mean when we say we diagnose before we prescribe. Always. Every engagement begins with a baseline assessment of where the organization is across all four layers, across Hands, Heads, and Hearts. We co-articulate the KPIs the business needs. We do not arrive with predetermined endpoints. Starting points and ending points depend entirely on the organization’s actual journey.
Real implementation doesn’t run the sequence once for the whole enterprise. It runs in parallel, one initiative at a time, each cycling through its own mindset, heartset, skillset, and toolset around a single outcome. Nobody gets to skip the diagnostic because another team already ran theirs.
This is how we get from Now to Next.
The reason this matters is that the standard consulting model produces prescriptions before diagnoses. The vendor sells the tool. The consultant produces the deliverable. Neither one has assessed whether the organization is ready for what is being prescribed. The result is a hundred billion dollars of enterprise AI investment producing the 95% pilot failure rate everyone is now reading about.
You cannot fix that with better prescriptions. You can only fix it by starting with better diagnoses. The 4SET sequence is the diagnostic frame. Hands, Heads, and Hearts is the dimensional frame. Together they give you a way to look at any AI deployment in your organization and identify where it is going to fail before it does.
What this means for you.
You have a choice this week. You can keep running the standard sequence — toolset-first, with mindset as an afterthought — and accept the standard results. Or you can interrupt the pattern.
Interrupting the pattern does not require ripping up your current AI strategy and starting over. It requires three things.
One. Sequence audit. Look at the AI investment you have already made and ask yourself, honestly, what layer you started in. If you started at toolset, the next investments need to fill in the foundation, not extend the tooling. If you started at skillset with no heartset work, the next investments need to address the wounds. If you have done foundation work but it has been disconnected from the tooling layer, the work is to connect them.
Two. Dimensional check. Whatever step of the sequence you are working in right now, ask whether you are doing the work in Hands, Heads, and Hearts simultaneously. If you are only redesigning the operational layer, you are leaving two-thirds of the value on the table. If you are working on culture without redesigning the work itself, you are producing inspiration without infrastructure.
Three. Diagnostic discipline. Stop prescribing before you have diagnosed. Every initiative you are about to launch should begin with an honest assessment of where the workforce, the function, and the organization actually are. The diagnostic does not have to be elaborate. It does have to be honest. The reason the standard sequence produces standard results is that nobody pauses long enough to ask where they actually are before deciding where to go next.
These three moves do not require new budget. They do not require new vendors. They do not require new technology. They require new vocabulary, new measurement, and the willingness to slow down for the diagnostic work that the standard sequence is rushing past.
The organizations that do this are already pulling away. Not loudly, not visibly to the market yet. But the ones doing the embodiment work, in the right sequence, across the right dimensions, are producing outcomes the standard sequence cannot. They are the early signal of what comes next.
Next week, the close. The seventh piece in this series. The one where I will not introduce a witness, will not name a framework, will not surface new evidence. The one where I will ask you a single question and ask you to answer it out loud, or at least to yourself.
The question is the one that has been running underneath every piece in this series. You already know what it is.
The piece is called What You Choose to Interrupt.
I’ll see you next week.
Pictured: Brandenburg Gate in Berlin last week. Loaded history put simply: it began as a symbol of power, became a symbol of division, and survived to become a symbol of unity. Punk Berlin, I loved you.
Jess Von Bank is the co-founder of Now to Next, a transformation firm specializing in the human dimensions of enterprise AI deployment. Her book, Work Like a Mother, will be published in the fall of 2026.
This is the sixth in a seven-part series. Read Piece 1, Piece 2, Piece 3, Piece 4, and Piece 5 here. On the home stretch now.
Jess,
I so look forward to this series every week. I'm sure I'll go through withdrawls when it's all over.
I couldn't agree more with your take on this. Being an individual contributor (not an executive, manager or consultant) I live with this "out of sequence" thinking every day. I have lots of tools but have no idea what is expected of me or how I'm expected to work/think/do differently. It's been this way at multiple organizations. While I don't have a "Heartset" issue personally, I might be unique in that regard. I love what's possible from all things AI, and I also know enough to know that not everyone feels that way.
As I was reading through this, I started thinking about why C-Suite and senior leaders behave this way. You've called it out...starting with Mindset doesn't produce a pretty Claude-contstructed slide for the board that shows results. And I think that's something we need to name as well. I'm betting most C-Suite execs WANT to do this differently and likely agree with your philosophy and structure, but they're afraid of not having anything concrete to show for the next board meeting.
Which leads me to...who's talking to boards of directors...and PE firms...and VC firms? Who is helping them understand that if they truly want results, want to see the investments they make and approve bear fruit...they have to get on board this train as well.
And how many board members are operating from old playbooks, passed down from their predecessors, that no longer have relevance and are asking for results in a timeframe and language that is now extinct (thanks to Covid, AI, etc.). How many of them have recently (<5 years) come from operational roles and sat in seat in the modern era of trying to get all of this work done, strategically, at the speed of AI? Perhaps a "board bootcamp" to reintroduce them into the trenches. Nothing like experiencing it first hand...
I believe there's a reckoning there that I'd love to see you and Jason lead. What's that old saying...the fish stinks from the head? I think we need to consider retooling the PE and VC firms and their mindsets/expectations as well.
Maybe another Substack series for you Jess. :) Keep fighting the good fight!
Jess - I've loved this series, and this edition (unsurprisingly) hit just as hard. A really powerful punch around what needs to flip - e.g. moving toolset to the back of the planning bus, rather than the front-loaded approach most companies are taking. YES. And I'd love to learn more about how leadership teams are reckoning with creating the conditions to enable this.
Part of what I think is missing, is the top-down clarity of true north: leadership teams providing the strategic, longer-term view of where the company is going and why (both with AI, and directionally WHY the company exists). So much of the chaos I observe is in the gap from clarity around the long-view (with clear, frequent communication to support the progress against that view) and the daily operations up and down at all levels, being stuck in the now.
The challenge I see is that teams cannot begin to get curious on what AI means for their goals, their mindset, their heartset... and then skillset, toolset... without a clear and tangible view beyond what needs to be delivered this week or this quarter. The problem compounds, because leadership teams are ALSO stuck in the 'move faster, deliver now' cycle, trying to keep up with board-level mandates, so the conditions to set up the 4SET sequencing can feel elusive at best. The three step 'pattern interruption' check is useful, and there needs to be that moment of reckoning for the leadership team to deploy it. Can't wait for edition #7!