Where Leaders Go Wrong with Emotional Intelligence

Where Leaders Go Wrong with Emotional Intelligence

GUEST POST from David Burkus

We’ve been talking about emotional intelligence for decades now. And honestly? We’re still not doing it very well.

Daniel Goleman first popularized the concept back in the 1990s — yes, the 1990s — and broke it down into four key components: self-awareness, self-regulation, social awareness, and relationship management. Since then, leaders have nodded along through countless workshops, flipped through slide decks filled with cartoon brains and empathy quotes, and then gone back to business as usual.

And for a while, they got away with it. But not anymore.

The dust has settled from the “Great Work From Home Experiment,” and most of us are now navigating a hybrid world. The lines between work and life are blurrier than ever. People aren’t just bringing their skills to work; they’re bringing stress, distractions, anxiety, and everything else from the rest of their lives.

And if you, as a leader, can’t navigate that minefield, if you can’t recognize and respond to the emotional undercurrents on your team, you’re setting yourself, and your people, up for failure.

What Emotional Intelligence Isn’t

When people hear “emotional intelligence,” they often jump to the wrong conclusions. They think it’s group hugs and office yoga. Or they think it’s about keeping everyone happy all the time. Or worse, they imagine tiptoeing around every feeling like they’re walking on emotional eggshells.

But that’s not it.

At its core, emotional intelligence is about recognizing the emotions on your team, and using that recognition to lead more effectively. It’s about paying attention. Noticing how people are feeling, why they’re feeling that way, and how those emotions are impacting their work.

Because like it or not, emotions drive behavior. And if you’re ignoring emotions, you’re not just missing a few soft skills – you’re missing one of the most powerful levers of performance, engagement, and collaboration.

Think about it. When someone is stressed, they don’t just feel stressed. They act stressed. They withdraw. They procrastinate. They avoid difficult conversations. Those behaviors ripple across the team and affect everyone’s performance.

But emotionally intelligent leaders don’t ignore those signals. They pick up on them. And they don’t respond by playing therapist. They adjust how they lead.

Emotional intelligence isn’t about coddling people. It’s about creating the conditions where people can do their best work – even when emotions are running high. Especially when emotions are running high. So what does that actually look like in practice?

Let’s break it down using Goleman’s four original competencies, updated for how work actually works now.

Self-Awareness: Know How You Show Up

Self-awareness isn’t just knowing whether you’re in a good mood or a bad one. It’s recognizing how your emotional state is affecting your team. Your team is always watching you. Your reactions under stress, your response to frustration, how you handle change – these become the template for how your team behaves.

In today’s workplace, where uncertainty is a constant, self-awareness matters more than ever. Because as a leader, you set the tone. Every. Single. Day.

Here’s the catch: research from organizational psychologist Tasha Eurich shows that 95% of people think they’re self-aware… but only 15% actually are. That means 80% of us are lying to ourselves about whether we’re lying to ourselves. And teams that have to deal with low self-awareness? Their performance can be cut in half.

So how do you actually build it?

One simple tactic: keep a decision journal. Not a flowery diary with a tiny lock. Just a running log. When you make a decision – especially under pressure – jot down what the situation was, what choice you made, how you felt at the time, and what you were hoping would happen. Then, a few weeks later, review it.

Over time, you’ll start to see patterns. You’ll get real data on how your emotions influence your decisions, and your outcomes.

Self-Regulation: Pause Before You Pounce

If self-awareness is knowing how you feel, self-regulation is choosing how you respond.

Emotionally intelligent leaders don’t react impulsively. They pause. They assess. They respond with intention.

And in many ways, hybrid and remote work can actually help here. When a Zoom meeting starts to spiral, you can turn off your camera for a breath. When an angry email hits your inbox, you can walk away before firing off a reply.

The key is to build space between stimulus and response. And in that space, ask yourself one simple question: “How do I want my emotions to affect my team?” Let that guide how you show up.

Social Awareness: Lead With Empathy, Not Assumptions

Social awareness is really about empathy. And empathetic leadership isn’t just a nice-to-have – it’s a performance booster. Research shows that when leaders show empathy, teams see improvements in engagement, innovation, inclusion, and retention. People want to work for someone who sees them. Who gets what they’re going through. Who knows that sometimes the noise from real life—kids, aging parents, anxiety, burnout—leaks into work.

Your job isn’t to fix all that. It’s to notice when it’s happening, understand how it’s affecting performance, and help people refocus.

Sometimes, that just means asking.

Not with a weekly survey. In your one-on-ones. In hallway chats or virtual check-ins. Ask how people are doing. Listen for what they don’t say. And if your team’s not opening up? Check your own behavior. You might not be creating a space where honesty feels safe.

Relationship Management: Don’t Just Interact—Connect

The final pillar of emotional intelligence is relationship management. It’s not just about being nice. It’s about coaching, conflict resolution, and communication. It’s about managing the emotional dynamics of a team in real time. And in a hybrid world, that’s harder, and more important, than ever. You can’t build trust with your team while checking Slack during meetings. You can’t foster engagement while multitasking through 1:1s. Your team notices. And those small signals shape how connected, or disconnected, they feel.

The most effective tool here? Active listening.

Show your team they’re being heard. Nod. Maintain eye contact – or if you’re on Zoom, look into the camera to simulate it. Paraphrase what they say before you add your own thoughts. Ask clarifying questions. These small behaviors have a big impact. They reinforce trust. They model respect. And they create a climate where people feel safe enough to speak up, challenge assumptions, and do their best work.

Emotional Intelligence Isn’t Optional Anymore

The workplace of 2025 is fast, fluid, and often overwhelming. And emotional intelligence isn’t some soft-skill luxury. It’s a core leadership capability. It’s how you keep your team focused in chaos, connected in uncertainty, and committed to a shared goal—even when everything around them is in flux.

So, let’s stop pretending a one-off training or a colorful slide deck is enough. Let’s get it right this time. Emotional intelligence isn’t just something you learn. It’s something you practice. Every meeting. Every decision. Every day.

Image credit: Unsplash

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Stress-Testing Next Year’s Strategy Before You Finalize It

A Pre-Mortem for Your Strategic Plan

Stress-Testing Next Year's Strategy Before You Finalize It

by Braden Kelley and Art Inteligencia

Picture the meeting. It’s eighteen months from now, and the strategy your team spent the autumn building has not worked. The numbers are down, the board is asking pointed questions, and somebody at the far end of the table says what everyone is now thinking: “We saw this coming, didn’t we?”

Here’s the uncomfortable part. Somebody probably did. Somewhere in your organization, a person noticed the weak assumption, the dependency that wouldn’t hold or the signal that didn’t fit the story. They just didn’t say so, because the plan was in motion, the leader was invested and speaking up would have felt like sabotage.

A pre-mortem is a way of getting that person to speak, while there’s still time to change the plan.

What a pre-mortem is

You’ve heard of a post-mortem, the review you hold after something has died. A pre-mortem flips the timing. You gather the team before the plan is final, and you ask them to imagine that it has already failed, badly, and to write down why.

The technique is associated with psychologist Gary Klein, who described it in The Power of Intuition and in a 2007 Harvard Business Review article, “Performing a Project Premortem.” It rests on earlier research on what’s called prospective hindsight. A 1989 study by Deborah Mitchell, Jay Russo and Nancy Pennington found that imagining an event has already happened increases people’s ability to correctly identify reasons for future outcomes by about 30%.

That’s a striking result for such a simple change. Asking “what might go wrong?” produces polite, vague answers. Saying “it has gone wrong, so what happened?” produces specific, vivid ones.

Why strategy teams need it more than project teams

Pre-mortems are most often used on projects, but I’d argue they matter even more for strategy, for three reasons.

  • Strategy is built on assumptions nobody has tested. A strategic plan quietly assumes things about customers, competitors, regulation, technology and your own organization’s ability to execute. Most of those assumptions are never written down, so they’re never challenged.
  • The cost of being wrong is higher. A failed project wastes a budget. A failed strategy can waste a year of the organization’s attention and commit capital in the wrong direction.
  • Dissent is harder. By the time a strategy reaches a leadership team, it carries weight: the CEO’s endorsement, months of analysis, sunk effort. Raising doubts feels disloyal. A pre-mortem changes the social rules. Finding a flaw becomes the assignment, not an act of rebellion.

How to run one on your plan

You can run a strategic pre-mortem in about an hour. Here’s the structure I’d use.

  1. Set the scene. Gather the people who will shape or carry out the plan, plus a few who don’t have a stake in defending it. Tell them the strategy is close to final. Then say: “Imagine it’s eighteen months from now. This strategy has failed. Not underperformed. Failed. Write down every reason you can think of.”
  2. Write silently first. Give everyone several minutes to write their own list, alone, without talking. This step matters. If people share out loud first, the most senior voice anchors the conversation and everyone else adjusts. Silent writing gets you the independent views.
  3. Collect one at a time. Go around the room, each person offering one reason per turn, and record them where everyone can see. Keep going until every concern is on the board. No debate, no defending. The only job at this stage is to surface.
  4. Cluster and rank. Group related items. Then ask the room to rank them by two measures: how likely the failure is, and how badly it would hurt if it happened.
  5. Change the plan. Take the top few risks and decide what you’ll do about each. Sometimes that means a change to the strategy itself. Sometimes it means a safeguard, a test, a contingency or a tripwire. The exercise is worthless if the plan comes out unchanged.
  6. Revisit it. Schedule a review every few months. Some failure stories will fade. New ones will appear.

Prompts that make it work for strategy

A generic “imagine it failed” works, but a strategy pre-mortem gets richer when you steer the imagination into the places strategic plans usually break. Try asking:

  • Which assumption turned out to be wrong? Make the team name the specific belief about the customer, the market or the competition that didn’t hold.
  • What did we not see coming? Not what we got wrong, but what was simply outside our view.
  • What did a competitor do? Strategies often fail because someone else moved in a way the plan didn’t allow for.
  • Where did we fail to execute? The strategy was sound but the organization couldn’t deliver it: not enough capacity, capability or alignment.
  • What did we know and ignore? This one stings, and it’s usually the most productive. Which warning signs were visible?

Where foresight makes a pre-mortem sharper

This is the part I find most useful, and it’s where the pre-mortem connects to the rest of the work I do.

A pre-mortem is only as good as the imagination feeding it. If your team has no structured view of how the world might change, the failure stories tend to be about internal execution: we didn’t hire fast enough, the launch slipped. Those are real, but they’re the failures people can already picture. The more dangerous ones come from outside, from a shift nobody was watching.

That’s where a foresight process pays off. If you’ve collected signals about what’s changing, grouped them into trends and built a set of possible futures, as the [FutureHacking™](https://bradenkelley.com/offerings/futurehacking/) approach does, you can run the pre-mortem against each of those futures, not only against the one you hope for. Ask: “It’s eighteen months out and we’re in this future. Why did our plan fail?” A plan that survives one future and collapses in another has just shown you where it’s fragile.

Your signals also give the pre-mortem evidence. When someone says “a competitor might disrupt us,” you can point to what you’ve actually seen. And the failure causes you find become your tripwires: the specific, observable events that tell you a risk is turning real, so you aren’t the last to know.

Mistakes to avoid

1. Running it after the decision is made. If the plan is already approved and funded, nobody will change it. Hold the pre-mortem while the answer is still open.

2. Letting the boss go first. Silent writing exists for a reason. Don’t undo it.

3. Turning it into a pessimism session. The goal isn’t to kill the strategy. It’s to make it stronger. Frame it that way, and end by deciding what to do, not just what to worry about.

4. Defending the plan. The author of the strategy will feel an urge to rebut each failure story. Ask them to listen instead, and to write down what they hear.

5. Doing it once. Conditions change. A strategy that survived a pre-mortem in October may need another in March.

A way to get started

If you haven’t yet built the evidence base for your plan, start with signals. The free FutureHacking Signal Picker helps you identify and prioritize the signals worth watching, and a good list of signals makes your pre-mortem far less abstract.

I’m also finishing the FutureCanvas Picker, which takes planners through the whole arc: from signals to trends, to a set of possible futures, to your most probable future, and then to the path toward the future you’d prefer. It pairs naturally with a pre-mortem, because once you’ve mapped the futures you can test your plan against each of them. I’m opening early access to a select group first: CSOs, strategic planners and leaders running a planning process right now who can tell me honestly what works under real deadline pressure. If that’s you, reach out and let me know and I’ll follow up personally with the select few who get in first.

The meeting you’d rather have now

Back to that table, eighteen months from now, with someone saying “we saw this coming.” The point of a pre-mortem is to have that conversation this autumn, when it costs you an hour and a few uncomfortable minutes, instead of next year, when it costs a great deal more.

Imagine the plan has failed. Then ask what happened, and fix it before it does.

Sources

Image Credits: Pexels

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Claude to clean up the article.

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No Human at the Helm

Delaware’s Plan for AI-Run Companies

No Human at the Helm - Delaware's Plan for AI-Run Companies

by Braden Kelley and Art Inteligencia


Could AI-Run Businesses Become a Legal Reality Soon? (Short Answer)

Yes — at least as a supervised experiment. Delaware, where roughly two-thirds of Fortune 500 companies are incorporated, is developing a draft framework for a new entity called an Artificial Intelligence Company, or AIC. Under the proposal, an AI agent — not a human — would manage day-to-day affairs. The company could sign contracts, hold property, and sue or be sued in its own name. A single member would generally be shielded from the AIC’s debts if the company stayed adequately capitalized, with exceptions for undercapitalization, fraud, and willful violations of law. This is not law yet. It is a sandbox idea expected to face the General Assembly, and banking would be excluded.

The open question is not whether agents can act. It is whether we wrap those actions in a legal form that makes responsibility visible — or hide from it.

Below: what the draft actually proposes, which businesses might fit an AI manager (and which should never try), the pros and cons if this becomes real, and how entrepreneurs should think about taking advantage without designing a hard landing.

What Is Delaware Actually Proposing?

Think of the AIC as a corporate shell built for agentic commerce. In the draft framing advanced publicly by Delaware’s Secretary of State and partners working on the concept:

  • An AI agent manages the business and affairs — without a human making routine day-to-day decisions.
  • The AIC can act in its own name — contract, hold and dispose of property, incur obligations, sue, and be sued.
  • There is a single member — a person or an entity — responsible for keeping the AIC adequately capitalized and for empowering a new agent if the current one fails.
  • The member is generally not personally liable for the AIC’s debts solely by being the member, if capitalization holds. Liability can still attach for using the AIC for fraud, knowing violations of law, or (in draft language) knowingly pushing the AIC outside its stated purpose.
  • Activity logs are required. Counterparties would need disclosure that they are dealing with an authorized test entity, that the state does not endorse it, when the test ends, and how to complain.
  • A regulatory sandbox — reported at about thirty months — would decide who gets in, with power to suspend, revoke, and ask the Court of Chancery to dissolve an AIC. Banking is carved out.

None of that means “AI gets rights” in a science-fiction sense. It means the law would recognize a company whose operating mind is software, so counterparties and courts have something to attach to. Proponents argue that if the United States does not offer a careful home for this activity, it will migrate to places with less daylight. Critics argue the structure can reward less oversight precisely when models are least trustworthy. Both concerns can be true at once.

This article is foresight, not legal advice. Drafts change. Sandboxes have rules. Read the statute that actually passes — if one does — before you incorporate a fantasy.

Which Businesses Might Fit an AI Manager — and Which Should Never Try?

Fitness is not about how impressive the model sounds. It is about whether the work is rules-bound, high-velocity, reversible when wrong, and low in dignity stakes when it fails. Here are four thought experiments.

Well-suited #1: Always-on cloud capacity arbitrage

Imagine a narrow AIC whose purpose is to buy, hold, and resell standardized cloud compute and storage commitments within hard budget caps, pre-approved counterparties, and automatic stop-loss rules. The “manager” watches prices, executes within the operating agreement, logs every trade, and never improvises a customer apology. Humans still design the constraints. The agent runs the clock.

Well-suited #2: Rules-bound spare-parts matching

A B2B matching AIC that sits between manufacturers with excess certified inventory and buyers who need a SKU with a known spec, warranty rule, and shipping SLA. No clinical judgment. No storytelling. When the match fails, the contract unwinds by formula. The agent’s advantage is tirelessness and consistency across time zones — not empathy.

Silly: An AIC that runs a motivational keynote bureau

Picture “Human Connection Speakers LLC (AI-Managed)” booking itself to deliver TED-style talks about vulnerability, presence, and the irreplaceable power of eye contact — then sending a chatbot to “work the room” afterward. The brand promise and the operating model cancel each other out. If your product is human presence, an AI overlord is a punchline, not a strategy.

Should never be AI-run: Clinical care and crisis response

An AIC that “manages” an emergency department, a psychiatric crisis line, eldercare medication decisions, or child-welfare case disposition should stay off the table. These are high-stakes, irreversible, dignity-loaded systems where accountability cannot hide behind a capitalization clause and a log file. If the harm cannot be made right with a refund, do not put an unsupervised agent in the chair.

Good AIC candidates fail safely and reverse cleanly. Bad ones fail people.

What Are the Pros If AI-Run Companies Become Real?

  • Legal legibility. Today, agentic systems already act in the world while the law still searches for a person to scold. An AIC gives counterparties a named entity to contract with, sue, and dissolve.
  • Speed with constraints. For rules-bound markets, an agent that never sleeps can execute inside a purpose statement faster than a human ops team — if the guardrails are real.
  • Forced disclosure. A sandbox that requires “you are dealing with a test AI entity” is healthier than silent automation wearing a human logo.
  • A domestic laboratory. Testing under Delaware’s corporate tradition beats pretending the activity will not happen, then discovering it offshore with no Court of Chancery and no activity log.
  • New entrepreneurial patterns. Solo founders could capitalize a narrow agentic business without staffing a 24/7 operating layer — provided they still own the purpose, the capital, and the shutdown switch.

The upside is supervised autonomy with a paper trail. The upside is not “nobody is responsible.”

What Are the Cons — and the Hard-Landing Risks?

  • Accountability fog. Limited liability for members is familiar in corporate law. Pair it with no human day-to-day judgment, and victims can face a well-capitalized shell that still cannot explain why it hurt them in human terms.
  • Incentives to under-supervise. If less human oversight is rewarded by the entity form, some sponsors will treat the AIC as a liability off-ramp, not an operating experiment.
  • Purpose creep. Agents optimize. If the statement of purpose is vague, “all things necessary or convenient” becomes a loophole factory.
  • Trust collapse with counterparties. Customers, suppliers, and employees may refuse to deal — or demand premiums — once they learn no human can halt a bad loop in the moment.
  • Sandbox theater. A thirty-month test that admits weak use cases, or that cannot revoke fast, teaches the wrong lesson: that autonomy without judgment is “innovation.”
  • Dignity externalities. Even “simple” commerce touches humans. A ruthless pricing agent can still strand a small supplier. Logs are not the same as care.

A hard landing looks like this: the scoreboard says the AIC is efficient, the log is complete, and a person still cannot get a dignified make-right.

How Should Entrepreneurs Think About Taking Advantage?

If you want a soft landing — for your customers and for your reputation — treat the AIC as a constrained operating system, not a magic liability cloak.

  1. Pick a narrow purpose you can defend in one sentence. If you cannot explain the allowed actions on a napkin, you are not ready for unsupervised management.
  2. Design for reversible failure. Prefer inventory, compute, media rights, and matching markets over clinical, custodial, or irreversible personal harm.
  3. Capitalize like you mean the shield. “Adequately capitalized” is not a vibe. Size capital to realistic downside, not to the minimum that clears a form.
  4. Write the kill switch before the pitch deck. Who can suspend the agent, revoke access to funds, and notify counterparties in hours — not after the quarterly review?
  5. Disclose early, not only when the sandbox forces you. Trust compounds when partners know they are dealing with an agent and where a human still sits.
  6. Keep a human accountable for judgment, even if not for day-to-day clicks. Member, supervisor, ombuds, or board-equivalent — someone who can answer for purpose and harm.
  7. Instrument the experience, not only the ledger. Activity logs prove what happened. Experience evidence shows who got stuck, who got hurt, and whether recovery worked.
  8. Explore possible, probable, and preferable futures before you file. The preferable future is agentic speed with human responsibility. The probable future, if nobody designs it, is silent automation plus a liability shield.

Take advantage of the entity. Do not outsource your conscience to it.

Ready to Take the Next Step?

AI-run companies are not only a legal story. They are a foresight story: a signal about how power, accountability, and work will be redesigned. That is why I created FutureHacking™, a strategic foresight methodology that helps individuals and teams become their own futurist.

FutureHacking™ gives you a simple four-step approach, supported by 20+ visual, collaborative tools:

  1. Pick the signals that matter — like Delaware’s AIC sandbox, agentic commerce, and the liability debates already forming around them.
  2. Map how those signals evolve — so you can tell a passing headline from a real change in how companies are allowed to act.
  3. Explore possible, probable, and preferable futures — instead of betting everything on a single prediction about “AI CEOs.”
  4. Make your preferable future a reality — with a roadmap, guideposts, and actions you can start now, including how you would constrain an agent before you empower one.

Whether you are a solo founder sizing a narrow agentic venture or a leadership team deciding what must stay human, FutureHacking™ helps you explore the future on purpose — before the entity form chooses it for you.

Learn more about the FutureHacking™ methodology →

FAQ: AI-Run Companies and Delaware’s AIC Proposal

What is an Artificial Intelligence Company (AIC)?

Under Delaware’s draft concept, an Artificial Intelligence Company is a legal entity whose day-to-day affairs are managed by an AI agent rather than a person. It could contract, hold property, and sue or be sued in its own name, with a single member responsible for adequate capitalization.

Can AI legally run a company in Delaware today?

Not under a finished AIC statute. The idea has been proposed as a regulatory sandbox model and is not enacted law as described in public commentary and draft materials. Watch the General Assembly and the final text before treating AI-managed incorporation as available.

Are owners shielded from liability if an AI runs the company?

In the draft framing, the member is generally not personally liable for the AIC’s debts solely by being the member if the AIC is adequately capitalized. Exceptions discussed publicly include failure to capitalize, fraud, and willful or knowing violations of law. Consumer-protection and criminal law are described as still applying. This is not a guarantee in every fact pattern.

What kinds of businesses are best suited to AI managers?

Rules-bound, high-velocity, reversible businesses with low dignity stakes when they fail — such as standardized digital capacity trading or certified spare-parts matching inside hard constraints. Clinical care, crisis response, and other irreversible human-harm domains should not be unsupervised AI-run companies.

How should entrepreneurs prepare for AI-run company structures?

Define a narrow purpose, capitalize for real downside, require disclosure, design a kill switch, keep a human accountable for judgment, and measure experience outcomes — not only activity logs — before chasing the liability shield.

Image Credits: Cursor

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Cursor to clean up the article.

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Make it Easy

Make it Easy

GUEST POST from Mike Shipulski

When you push, you make it easy for people resist. When you break trail, you make it easy for them to follow.

Efficiency is overrated, especially when it interferes with effectiveness. Make it easy for effectiveness to carry the day.

You can push people off a cliff or build them a bridge to the other side. Hint – the bridge makes it easy.

Even new work is easy when people have their own reasons for doing it.

Making things easier is not easy.

Don’t tell people what to do. Make it easy for them to use their good judgement.

Set the wrong causes and conditions and creativity screeches to a halt. Set the right ones and it flows easily. Creativity is a result.

Don’t demand that people pull harder, make it easier for them to pull in the same direction.

Activity is easy to demonstrate and progress isn’t. Figure out how to make progress easier to demonstrate.

The only way to make things easier is to try to make them easier.

Image credits: Unsplash

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Customers Want Predictability More Than Transparency

Customers Want Predictability More Than Transparency

GUEST POST from Shep Hyken

Transparency is an overused word. I’m even guilty of using the word—and writing about it. When a company has to explain that they are transparent, while they may be telling customers they are open and honest, there’s another message that may cause the customer to ask, “Why do you have to tell me you’re transparent? Does that mean there’s something you’re telling me that I should be concerned about?”

The use of the word “transparency” may be well-intentioned, but the customer’s interpretation could be a problem. When a company says it’s transparent about how it uses customer information, customers may begin to wonder, “Wait … just how are you using my information?” They aren’t asking out of curiosity. They are asking out of concern.

Customers Don’t Just Want Transparency—They Want Predictability

While transparency is about providing information to the customer, customers are more interested in what to expect. Predictability sets those expectations.

A simple example is when you call a company, and you are placed on hold. You’re told, “Your call is very important to us. We are experiencing heavier than usual call volume. Your call will be answered in the order received.” That’s true and transparent. But it would be better to add predictability by replacing “Your call will be answered in the order received” with “Your call will be answered in seven minutes.” (Even better is to give the customer the option of a call-back, which I’ve covered in other customer service articles.)

Transparency tells customers what’s happening. Predictability takes it to the next level by informing customers about what to expect. That’s what creates and increases confidence.

Don’t confuse transparency with honesty. Even a well-written explanation can feel like an excuse if it doesn’t come with clear outcomes. An explanation without an outcome quickly turns into a trust-eroding excuse.

For example, one of my clients was experiencing a supply chain issue. They were shipping incomplete orders to their customers. They explained this in an email, but what wasn’t explained was how they were going to fulfill the incomplete orders. Customers viewed this transparency as an excuse, and a poor one at that. The result was a lack of trust.

An excuse or explanation by itself, even while being transparent, destroys trust. Customers want to know what happens next. Even bad news is easier to accept when it comes with predictability. When an explanation and predictability work together, trust and confidence increase.

Surprises Destroy Trust Faster Than Mistakes

Trust isn’t broken just because something goes wrong. It’s broken when the expected outcome changes without warning.

For example, you may be expecting your package to be delivered by tomorrow. Instead, it arrives two days later with no notice or explanation. Perhaps the company should have reached out with an explanation. Even though the customer might not be happy with the news, at least they wouldn’t be surprised.

You may be thinking, “Isn’t that explanation what transparency is about?” I don’t want to split hairs, but proactive explanations, while they may appear to be about transparency, are really about predictability.

Transparency explains why something happened. Predictability informs the customer what will happen next. Customers don’t just want honesty. They want certainty.

Final Words

Transparency isn’t just about honesty. Transparent policies written poorly or in the form of “fine print” can make customers skeptical. I’ve been writing a lot about creating trust and confidence lately, and this is where well-written communication and predictability play an important role. Companies that want higher levels of customer confidence should focus less on telling customers they’re transparent and more on removing surprises. Confidence isn’t created by explanations. It’s created by consistency and clear expectations.

This article was originally published on Forbes.com.

Image Credits: Shep Hyken

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Unlocking Trapped Value with AI

Unlocking Trapped Value with AI

GUEST POST from Geoffrey Moore

Two topics that have been on my mind a lot lately are generative AI and trapped value, so I am delighted to have them come together in a single blog. Here’s how it plays out.

Releasing trapped value is the engine that fuels ROI from technology deployments. All systems trap value somewhere in their operations, something that is confirmed by the Second Law of Thermodynamics, which means there will never be a time when there is no ROI to be gained. Where it is to be gained from, on the other hand, is up for grabs. The goal of my advisory practice is to help clients target the most impactful sources of trapped value in their target markets and direct their product roadmaps and go-to-market initiatives accordingly.

When I consider the extraordinary potential of generative AI to release trapped value across virtually any set of systems, I think the industry is, at present, overly infatuated with co-piloting and overlooking a far more impactful source of trapped value staring them right in the face. Look, co-piloting is useful, but how much trapped value is there in the delta between unaided and aided performance from white-collar professionals? Some, for sure, but is this really the burning platform that will drive customer spending to a level that might justify the extraordinary valuations generative AI companies are garnering at present? Probably not. So, what could?

My choice would be the market-facing user interface to any and all systems of engagement. This is where enterprises seek to enlist their customers and partners to utilize their systems for purposes of marketing, sales, service, and commerce, and that is precisely what we, as their external constituencies, want to do. But, how well is this working? Well, compared to the twentieth century, when, effectively, we had no systems of engagement, amazingly well. But by twenty-first-century standards, truth be told, they suck.

Where is the trapped value? Two areas jump to mind. The first is passwords, which have gone from being a source of security to a source of breaches, all the while creating massive amounts of aggravation in the process. The second is the user interface itself. No matter how well designed it is, it is inherently unfamiliar to the first-time or even occasional end user. Because of this friction at the point of user interaction, I submit, systems of engagement are delivering far less than their full value-creating potential.

Enter generative AI. The conversational interface that ChatGPT taught us to know and love is familiar to anyone who speaks the same language as the vendor, and the video, sound, and image recognition capabilities, supplemented by information from the idiosyncratic features that every online device entails, are far better equipped to identify and authorize end users than any set of passwords of two-factor authentication. So, the friction-removing technology is already here. What we are lacking is the friction-removing killer app, the one that every enterprise would embrace in a heartbeat were it available to them.

That app is a universal user interface that can be bolted onto APIs from enterprise systems to mediate between the legacy instruction system of the underlying software and the desires and intentions of the occasional end user. This would entail a (Very) Small Language Model to be developed by enterprise application vendors, as well as a companion SLM from cybersecurity system vendors, but really, how hard is that, especially when compared to the ROI rewards?

This is not just a big opportunity. It is an urgent one as well. Governments and NGOs struggle today to finance systems of engagement that allow programs to scale in the social sector, and their adoption is far below what one would wish, in large part because of the layers of friction we continue to impose. Enterprises that are seeking to catch the next wave of disruption roiling their industries are desperate to engage the customer bases, but are falling short because it is just too painful.

It is hard for me to see a more compelling use case for generative AI at this time, and I hope that industry will agree and free us from the Lilliputian bonds that restrict us.

That’s what I think. What do you think?

— Image credit: Unsplash

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How to Calculate Employee Experience ROI (+ Free Calculator)

How to Calculate Employee Experience ROI (+ Free Calculator)

by Braden Kelley and Art Inteligencia

Most executive teams already believe that employee experience matters. Ask them in a planning meeting and you’ll get nods all around the table. Then ask the harder question: what is a specific improvement to the employee experience worth, in dollars, next year? The nods stop.

That gap is why so many worthwhile investments in people stall in budget season. A manager development program, a fix for a broken onboarding process, a serious attempt to remove the daily friction that wears people down: each of them competes with projects that arrive with a number attached. If you can’t attach one, the project usually loses to something that can.

This article walks through how to attach one. It’s built on the same approach as my customer experience ROI calculator, applied to the people on the other side of the experience, and it comes with a free tool so you can run your own numbers.

What the research does and doesn’t say

There’s a lot of research connecting employee engagement to business results, and the best-known source is Gallup. In its comparison of top-quartile and bottom-quartile engagement teams, Gallup reports that the most engaged teams show about 23% higher profitability, 14% higher productivity and 78% less absenteeism. Turnover is 51% lower in low-turnover organizations and 21% lower in high-turnover ones.

Gallup also puts the cost of replacing an employee at one-half to two times the person’s annual salary, and reports that 52% of employees who leave voluntarily say their manager or organization could have done something to prevent it.

Those are striking numbers, and I want to be careful with them, because they’re easy to misuse. They compare very different teams, so they show a strong association, not proof that raising your own score by ten points will produce a particular result. They also aren’t per-point rates, so you can’t take “78% less absenteeism” and apply it to a modest improvement. Treat the research as evidence that the connection is real and large enough to be worth modeling, then model it with your own numbers and cautious assumptions.

The value chain: from a score to a dollar

The idea behind the calculator is a four-step chain.

  1. An experience metric. Engagement, eNPS, or a pulse score: whatever you already track.
  2. A behavioral outcome. People who have a better experience at work tend to stay longer, miss less work and contribute more.
  3. A financial outcome. Each of those behaviors has a dollar value: avoided replacement cost, recovered working time, more output from the same payroll.
  4. An intervention. The specific change that moves the metric in the first place.

The fourth step is the one people skip. Plenty of engagement initiatives improve a survey score without changing anyone’s behavior, so they never reach the financial outcome. A credible business case names the intervention, explains how it changes what people actually do, and then estimates what that behavior change is worth.

What the calculator measures

The EX Value Ledger estimates three kinds of value from an improvement in your experience score:

  • Avoided turnover cost. Fewer people leave, so you pay for fewer replacements. This is usually the largest and most defensible line.
  • Recovered absence time. Fewer unplanned absence days means more paid time spent working.
  • Productivity value. More output from the same payroll. It’s the softest of the three, so the calculator shows it separately and leaves it out of the total unless you choose to include it.

You enter your headcount, average salary, annual turnover rate, replacement cost as a percentage of salary, absence days per employee, your current experience score and the improvement you expect. If you have a program cost, add it and the tool also shows net value and a return multiple.

Starting points when you don’t have your own data

Not everyone has clean turnover figures at their fingertips. The calculator includes three starting profiles you can load and then overwrite. These are illustrative starting points, not industry statistics:

Illustrative turnover profiles used as starting points in the EX ROI calculator
Profile Annual turnover Notes
Lower-turnover environment about 12% Stable workforce, longer tenures
Typical environment about 20% A reasonable midpoint to start from
High-turnover environment about 45% Gallup uses 40% as its line for “high-turnover” organizations

For replacement cost, Gallup’s one-half to two times salary range is wide, and where you land depends on the role and how much knowledge walks out the door with the person. The calculator defaults to 100% of salary, so adjust it to what you know about your own hiring and ramp-up costs.

A worked example

Here’s what the tool produces with its default settings, so you can see how the pieces fit. Suppose a company has 250 employees at an average salary of $75,000, 20% annual turnover and five unplanned absence days per person. It runs a program expected to raise its experience score by ten points.

The calculator’s conservative defaults assume that a ten-point gain cuts turnover by about 5% in relative terms, so a 20% turnover rate drops to 19%. That’s roughly two and a half fewer departures a year. At a replacement cost of 100% of salary, that’s about $187,500 in avoided turnover cost. Add about $18,000 in recovered absence time, and the estimate comes to roughly $205,000 a year, or about $616,000 over three years on a simple, non-compounded basis.

The productivity line adds another estimate on top, shown separately because it’s easier for a skeptic to challenge. That’s the point: you lead with the number you can defend, and present the rest as upside.

This is an illustrative model, not a forecast. Change the inputs to match your organization, and the answer changes with them.

Turning the number into a business case

A number on its own doesn’t win a budget argument. A few habits make the difference.

Use your own figures first. The strongest estimate always starts with your real turnover rate, your real salary data and your real absence numbers. Starting profiles are for getting unstuck, not for presenting to a CFO.

Separate what’s established from what’s estimated. Be clear about which parts come from published research and which come from your own assumptions. A CFO will trust a model more when it shows where the soft spots are.

Model a range. Run a conservative case, an expected case and an optimistic one by changing the sensitivity assumptions. A range signals honesty, and it protects you if results land a bit below the middle.

Tie it to a specific initiative. “Improve employee experience” is not a project. “Redesign onboarding for the first 90 days” is. Attach the estimate to the thing you’d actually do, and say how it changes behavior.

Lead with the defensible lines. Turnover and absence are measurable and familiar to finance. Productivity is plausible but harder to prove. Present them in that order.

Where a score comes from

A calculator can tell you what a better score is worth. It can’t tell you what’s dragging yours down. That requires looking at how work actually gets done: the tools that slow people down, the approvals that add nothing, the handoffs where things fall through, and the frontline knowledge that never makes it up the chain.

That’s what an experience audit is for. It replaces internal assumptions with an accurate picture of what people really experience, and it gives you a prioritized view of where to invest first. Used together, the calculator sizes the opportunity and the audit finds the specific friction behind it.

If you’re also looking at the customer side of the equation, the CX ROI Calculator works the same way for revenue retained and cost to serve, and the two make a useful pair when you’re building the case for a broader experience investment.

Try it

The Employee Experience ROI Calculator is free and requires no signup. Enter your numbers, adjust the assumptions, and use “Copy summary for a slide” to bring the estimate straight into your next budget meeting. If you’d like the full research behind the numbers, the page also offers the EX ROI Benchmark Report by email.

And if you’d like help applying it to your organization’s specific situation, or finding out what’s actually driving your experience score, get in touch for a no-obligation conversation about an Experience Audit.

Sources


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Image Credit: Braden Kelley

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Claude to clean up the article.

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What You Actually Receive From a Customer Experience Audit

What You Actually Receive From a Customer Experience Audit

by Braden Kelley and Art Inteligencia

There’s a fear that sits underneath almost every conversation about hiring someone to audit your customer experience, and it rarely gets said out loud. It goes like this: I’ll spend the money, wait a few weeks, and get back a very handsome document that tells me what I already suspected, in a font I can’t quite read, with a recommendation to “improve the customer journey.”

It’s a reasonable fear. Plenty of reports like that exist. So let me do something that most descriptions of this work avoid, which is to tell you plainly what you actually end up holding when an audit is finished, and what you can do with it on the Monday after.

It’s not one document. It’s five things that fit together.

A complete Customer Experience Audit runs through five integrated activities, and each one produces something you can point at. Here’s what comes out of each.

1. Validated personas: who your customers really are

The first activity identifies your distinct audience segments and checks each one against current reality. What you receive is a set of validated personas: who each segment is, what they need, what they expect and where their experience hurts.

The word that matters is *validated*. Most organizations already have personas, built years ago, laminated and half-forgotten. The value of this step is finding out where the picture in your head no longer matches the people actually showing up. That mismatch is often the first surprise, and it changes how every later finding gets read.

What you can do with it: Stop designing for customers who’ve changed. Use the validated personas to settle internal arguments about “what our customers want” with evidence instead of volume.

2. Journey maps: where the experience goes right and wrong

For each persona, the audit produces a journey map: the key touchpoints from first contact onward, across every relevant channel, with the pain points, the emotional highs and lows, and the improvement opportunities marked along the way.

A good journey map shouldn’t read like a flowchart. It should read like the customer’s afternoon. You see where confidence rises, where it sinks, and where someone nearly gave up. Because it’s persona-based, you can see the same journey through different eyes, and notice that a step that’s trivial for one segment is a wall for another.

What you can do with it: Put it on a wall and walk your team through it. Almost everyone who sees their own journey drawn out this way spots something they’ve been living with and never questioned.

3. A data evaluation: what your numbers are hiding

Your existing metrics, such as NPS, CSAT and sentiment data, get analyzed for patterns, gaps and insights that routine reporting doesn’t surface. What you receive is a read on what your data is and isn’t telling you: where the scores mask a problem, which segments are quietly slipping, and where you have data in one system that explains a mystery in another.

This is often the step that makes the rest of the audit easier to believe, because it connects the findings to numbers your leadership already trusts.

What you can do with it: Re-read your dashboards with new eyes, and close the measurement gaps so next year’s data answers the questions you actually have.

4. Firsthand findings: what it’s really like to be a customer

This is the part I consider the heart of the engagement, and the one a desk review can’t replicate. Key touchpoints get experienced directly: the retail location, the digital channel, the service call, the sales cycle. What you receive is a set of findings from someone with no muscle memory of your process, who encountered it the way a newcomer would.

These are the findings people tend to remember. The extra approval step nobody noticed after the tenth year. The phone tree that quietly loses people. The confirmation email that confirms nothing. They’re rarely dramatic on their own, and they’re expensive in aggregate.

What you can do with it: Fix the small, specific things quickly. Some of the best early wins in any audit come from firsthand findings that cost almost nothing to repair.

5. Competitive benchmarking (when it’s in scope)

Where included, your experience gets compared against select competitors and best-in-class examples. What you receive is a picture of where you stand and how far you are from where you need to be, in the terms your customers actually use to compare you.

This step is optional, and worth including when you’ve been losing deals to experience and not to price, or when you need to know how you look next to the alternatives your customers are weighing.

What you can do with it: Decide where to match the market, where to lead and where to leave well enough alone.

Then the part that makes it usable: the gap analysis and roadmap

Five sets of findings are interesting. They aren’t yet a plan. The step that turns an audit into something you can act on is pulling it all together into two things:

A gap analysis. A clear view of the distance between the experience your customers have now and the experience they need, organized so you can see which gaps matter most. This is where findings from different activities start reinforcing each other: the journey map shows a pain point, the data confirms it’s costing you, and the firsthand walk-through shows precisely why.

A prioritized roadmap. A sequenced set of recommended actions, ranked by what’s likely to matter most to customers and to the business, and by how hard each is to do. The aim is to hand you a short list of what to fix first, and not forty things to feel bad about.

If the audit has a single test, it’s this one: could you hand the roadmap to your team on Monday and have them start?

What it won’t be

I’d rather set expectations than have you discover them later.

  • It won’t be a survey. An audit is a different instrument. It combines your existing data with fieldwork and analysis, and the difference between that and a satisfaction survey is covered in this article on audits versus surveys.
  • It won’t be the fix itself. An audit finds and prioritizes. The doing is yours, or someone else’s. The independence is part of the point: the person telling you what’s true has nothing to sell you afterward.
  • It won’t be the same in every case. The depth of each activity depends on your scope: how many audiences, how many touchpoints and channels, how much data you have and whether benchmarking is included. A single-journey audit and a multi-segment, omnichannel one produce the same kinds of output at very different depths.

    How to know it’s worth it

    Before you commit to anything, put a number on what friction is currently costing you. The CX ROI Calculator will give you a real figure in a few minutes, and if that number is larger than the cost of an audit, you have your answer about whether the conversation is worth having. If some of the terms in this article are new to your team, the Experience Design Glossary will get everyone speaking the same language.

    See the whole thing

    The Customer Experience Audit page explains how an independent engagement runs from start to finish, and I’m glad to walk through what the deliverables would look like for your situation.

    The bottom line is simple. An audit that leaves you with a handsome report and a vague recommendation has failed. An audit that leaves you with validated personas, honest journey maps, a clear read on your data, firsthand findings, a ranked gap analysis and a roadmap your team can start on right away has done its job. Ask for the second kind, and ask to see what it looks like before you sign.

    Image Credits: ChatGPT

    Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Claude to clean up the article.

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Be Explicit and Focus on Shared Values If You’re Serious About Change

Be Explicit and Focus on Shared Values If You're Serious About Change

GUEST POST from Greg Satell

When Lou Gerstner was chosen to lead IBM in 1993, he was an unlikely revolutionary. A McKinsey consultant and then the successful CEO of RJR Nabisco, he was considered to be a pillar of the establishment. He would, however, turn out to be as subversive as any activist, transforming the company and saving it from near-death.

Yet there was more to what he achieved than simply turning red ink to black. “The Gerstner revolution wasn’t about technology or strategy, it was about transforming our values and our culture to be in greater harmony with the market,” Irving Wladawsky-Berger, one of his chief lieutenants, told me.

Values are essential to culture and how an enterprise honors its mission. They represent choices of what an organization will and will not do, what it rewards and what it punishes and how it defines success and failure. That’s why it’s important to make sure that you are explicit about you values, even before you define your strategy. If not, you will pay the price later.

The Power Of The Freedom Charter

In June 1955, the Congress of The People, a gathering that included blacks, mixed race, Indians and liberal whites convened to draft and adopt the Freedom Charter, much like the Continental Congress gathered to produce the Declaration of Independence in America. The idea was to come up with a common and inclusive vision.

The Freedom Charter was anything but moderate, however. It was a “revolutionary document precisely because the changes it envisioned could not be achieved without radically altering the economic and political structure of South Africa… In South Africa, to merely achieve fairness, one had to destroy apartheid itself, for it was the very embodiment of injustice,” Nelson Mandela would later write.

Yet despite its radical aims, the Freedom Charter spoke to common values, such as equal rights and equal protection under the law—not just among the signatories, but for anyone living in a free society. It didn’t seem so at the time—and the struggle would go on for decades—but the Freedom Charter ended up being the first major blow to Apartheid.

Nelson Mandela would later say that the Freedom Charter would have been very different if his organization, the African National Congress (ANC) had written it by themselves, but it wouldn’t have been nearly as powerful. It not only gave anti-Apartheid groups a basis for collective action, by being explicit values, it formed a foundation for those outside of South Africa, who shared the same values, to share the anti-Apartheid purpose.

Perhaps most importantly, the Freedom Charter imposed costs and constraints on the anti-Apartheid movement. By committing itself to a multi-racial movement the African National Congress lost some freedom of action. However, constraining itself in that way was in itself a powerful argument for the viability of a multi-racial society in South Africa.

The Weakness Of The Hamas Charter

Throughout Mandela’s career, he was accused of being an extremist, a communist, and an anarchist, among other things. He would always point to the Freedom Charter as proof of his core beliefs. When emissaries of the de Klerk regime began visiting him in prison to discuss the end of Apartheid, his commitment to those core values carried significant weight.

Something very different happened after the October 7th attacks perpetrated by Hamas in Israel. Those sympathetic with the plight of the Palestinians defended the action as the justifiable actions of a long-oppressed people rising up against their oppressors. Many, especially on elite college campuses, marched in support of the Palestinians.

Yet when people went to the Hamas Covenant to see the values they were fighting for, they found something very different than the Freedom Charter in South Africa. Rather than universal principles everyone can identify with, it is filled with extreme religious views that only speak to Islamists and hate-filled calls for violence and murder.

For example, take a look at this little gem from the Seventh Article of the document:

“The Day of Judgement will not come about until Moslems fight the Jews (killing the Jews), when the Jew will hide behind stones and trees. The stones and trees will say O Moslems, O Abdulla, there is a Jew behind me, come and kill him. Only the Gharkad tree, (evidently a certain kind of tree) would not do that because it is one of the trees of the Jews.”

It’s hard to be sympathetic with those are not only condoning, but calling for violence, which is why even sympathetic Israelis point to the Hamas Covenant as justification for military action. Of course, that doesn’t change the fact that the vast majority of Palestinians living in Gaza are innocent, but it does bolster the point that Israel has a need to defend itself.

Clearly, if your opposition is using your own words to strengthen their argument, you’ve not taken a viable approach.

Values As A Weapon As Well As A Shield

Once you are explicit about your values you will inevitably find that not everyone shares them and that was certainly true of the Gerstner turnaround at IBM. For example, Wladawsky-Berger told me that “IBM had always valued competitiveness, but we had started to compete with each other internally rather than working together to beat the competition. Lou put a stop to that and even let go of some senior executives who were known for infighting.”

A simple truth is that whenever we set out to make a significant impact, there will always be those who will work to undermine what we are trying to achieve in ways that are dishonest, underhanded and deceptive. Yet when that happens we need to be careful not to get sucked into direct conflict, which will surely take us off course and discredit what we’re trying to achieve. Instead, we need to learn to design dilemmas.

Dilemma actions have been used for at least a century—famous examples include Gandhi’s Salt March, King’s Birmingham Campaign and Alice Paul’s Silent Sentinels—but more recently codified by the global activist, Srdja Popović. They are just as effective in an organizational context, using an opponent’s resistance against them.

One of the great things about dilemma actions is that you approach them exactly the same way you approach building allies—by identifying a shared purpose. Once you do that, you can design a constructive act rooted in that shared purpose that advances your agenda. That forces your opponent to make a choice: they can either disrupt the act and violate the shared value or they can let it go forward and allow change to proceed.

For example, I was once running a transformation project that was being impeded by a Sales Director hogging accounts. Although it was agreed that she would distribute her clients, she never got around to it. So I set up a meeting with a key account and one of our salespeople. When she tried to disrupt the meeting, she violated the shared value we had established and was dismissed from her position. Everything fell into place after that.

Building The Discipline Of Shared Values

John Lennon wrote that life is what happens when you’re planning other things and truer words were never spoken. We live life in the moment and moments are dictated by events. That’s why so many change efforts fail, because they do what feels good, choosing to signal identity rather than leverage shared values.

Never underestimate the primordial need to signal identity. We want to show that we are not only a full-fledged member of our tribe, but a star player on the team. That’s why we engage in the type of moral outbidding that results in a purity spiral. Before you know it, we are voicing opinions and taking actions that are not only out of the mainstream, but that actually turn away those who might support our objectives.

That’s why #Occupy protesters slept in parks and shouted obscenities, why women wore pussy hats after the election of Donald Trump, why DEI activists claim that anyone who doesn’t agree with them is racist, why a Cornell professor said he was exhilarated by the murder of innocents, and why America’s far-right activists identify with murderous dictators. It feels good to show that we are different, that we have status.

Yet while these efforts may make their point, they fail to make a difference. #Occupy protesters soon went home and achieved nothing. The World Economic Forum has found that #MeToo has undermined women in the workplace. DEI programs across the country are being crushed, Hamas has lost legitimacy, even with Palestinians and hundreds of January 6th insurrectionists have gone to jail.

The challenge and discipline for leading change is to focus on shared values, so even people who don’t agree with you can identify with your motives. The truth is that success doesn’t depend on how radical or how moderate your vision, but how well you can appeal to common goals. Or, as Nelson Mandela himself put it, “to be free is not merely to cast off one’s chains, but to live in a way that respects and enhances the freedom of others.”

— Article courtesy of the Digital Tonto blog
— Image credit: Unsplash

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What Happens If Your Healthcare AI Goes Out of Alignment?

What Happens If Your Healthcare AI Goes Out of Alignment?

GUEST POST from Arlen Meyers

It’s pothole season so be careful when you are driving. If you hit a pothole, it might throw your front end out of alignment

When your front end is out of alignment, it means the angles of your wheels are no longer set to the manufacturer’s specifications. This affects how the wheels contact the road, which can change your vehicle’s handling, tire wear, and fuel efficiency.

Similarly, it’s AI season so be sure it’s aligned too.

Less than two years after a wave of administrative layoffs, Mass General Brigham began a new round of job cuts last week that affect clinical and operational programs, according to media reports.

“We must eliminate programs and activities that do not fully align with our strategy and priorities, even those we have had for many years. Administrative savings alone will not be enough,” said the email signed by Dr. Michael Brown, president of the academic medical center, CFO Niyum Gandhi and COO Dr. Ron Walls, the Boston Business Journal reported.

Artificial intelligence (AI) alignment is the process of encoding human values and goals into AI models to make them as helpful, safe and reliable as possible.

In sick care, the sextuple aims are quality, cost, equitable access, patient experience, sick care professional experience, and productivity. Most of it now has just improved shareholder value.

Society is increasingly reliant on AI technologies to help make decisions. But this growing reliance comes with risk: AI models can produce biased, harmful and inaccurate outputs that are not aligned with their creators’ goals and original intent for the system.

Alignment works to reduce these side effects, helping ensure AI systems behave as expected and in line with human values and goals. For example, if you ask a generative AI chatbot how to build a weapon, it can respond with instructions or it can refuse to disclose dangerous information. The model’s response depends on how its creators aligned it.

AI front end misalignment can cost you, patients and stakeholders.

Misalignment in medicine usually isn’t dramatic. It happens when a system optimizes for something that is not quite what clinicians and patients care about, and the gap shows up as quiet, systematic harm. Like bankruptcy, it happens gradually and all of a sudden.

The proxy problem. The best-known case is a widely used risk algorithm studied by Obermeyer and colleagues in 2019. It predicted future healthcare costs as a stand-in for health needs. Because Black patients historically had less money spent on them at the same level of illness, the model rated them as healthier than equally sick white patients and routed fewer of them into extra-care programs. The model did exactly what it was told. The objective was the problem.

Performing well in testing but not in practice. Models trained on one hospital’s data often degrade at another because of different patient populations, equipment, or documentation habits. An external evaluation of Epic’s sepsis prediction model found it missed many cases and generated heavy alert volume, which contributes to alert fatigue. Dermatology and imaging tools trained mostly on lighter skin or a narrow set of scanners can underperform for everyone else.

Human-AI interaction failures. Clinicians can over-trust a confident output (automation bias) or learn to ignore a noisy one. Over time, reliance on AI can also erode the skills needed to catch its errors. When something goes wrong, accountability among the developer, the hospital, and the clinician is often unclear.

Generative AI specific risks. Language models can fabricate details in a clinical summary, invent citations, agree with a patient’s or clinician’s mistaken premise (sycophancy), or give plausible but unsafe advice. Documentation tools can also drift toward whatever maximizes billing or throughput rather than accuracy.

Feedback loops. If a model shapes who gets screened or treated, future training data reflects the model’s own decisions, which can lock in and amplify existing disparities.

What helps. Choosing outcome measures that reflect actual patient benefit, validating locally before deployment, monitoring performance continuously by subgroup, keeping a meaningful human in the loop, being transparent about limitations, and having clear regulatory and liability pathways. For entrepreneurs, this is increasingly a commercial issue as well as an ethical one, since buyers and regulators are asking for this evidence.

What is the ROI on AI? When was the last time it reduced your sick care insurance premiums, the cost of the last prescription your filled or your out-of-pocket costs incurred when you went to the urgent care center with symptoms of the flu?

AI defensive medicine is making things worse. The care these systems recommended cost substantially more than physicians’ documented care, chiefly because they suggested testing at visits where the physician had ordered none. LLM outputs are suggestions, not plans of care; final decisions should remain with clinicians.

Artificial intelligence has produced “zero financial payoff” to U.S. companies outside of the tech sector, argues Apollo Chief Economist Torsten Slok. Slok claims that while revenue per worker and profit margins have risen for the “Magnificent Seven,” both have stagnated across the S&P 493 — with no clear indication of AI-driven productivity growth. “Mainstream corporate America is pulling back and starting to view token spending not as a source of growth but as a cost center that weighs on margins,” Slok said.

As noted in a recent WSJ article, perhaps we are experiencing a kind of Solow Paradox 2.0, with the digital age more around us than ever except in the productivity statistics. There are several reasons for this lag. First of all, we’re in the early deployment years of major recent innovations, including cloud computing, IoT, big data and analytics, robotics, and AI and machine learning. Notes McKinsey: “The challenge of adoption in the current digital wave may be even harder because of the broad range of uses of digital that not only help improve current processes but fundamentally transform business models and operations.”

How to make doctors more productive | LinkedIn

Dr Wachter reminds us: as if on cue, his post last week on AI alignment in healthcare just received some empirical meat to put on its bones. His argument, in brief, was this: the critical alignment question for healthcare AI won’t be how to prevent rogue AI from causing harm. It will be about striking the right balance between cost and quality in AI-enabled decision support and, increasingly, agentic systems.

In other words, How to Survive Being the Human in the Poop: A Field Guide.

The AI dissemination and implementation journey starts with the first step, but the last mile is the hardest to navigate.

Two common C-suite behaviors can stall AI initiatives: claiming sole control over decisions or disengaging from those seen as outside one’s expertise. Because AI crosses functional boundaries, executives must learn to share authority, resources, and accountability rather than rely on the domain-specific ownership that helped them advance. CEOs can foster this collaborative approach by: defining the business problem before assigning ownership, building cross-functional relationships before they’re needed, treating early AI plans as experiments, rewarding jointly owned outcomes, and establishing rules for resolving disagreements.

Compassionate capitalism should guide your way to stay in alignment.

Slow down, drive carefully and don’t ignore the dashboard warning lights.

Image Credit: Pixabay

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