NowBuilder™: Turning Long-Term Foresight Into This Year’s Strategic Priorities

NowBuilder™: Turning Long-Term Foresight Into This Year's Strategic Priorities

by Braden Kelley and Art Inteligencia

A ten-year vision and a one-year budget speak two completely different languages, and most organizations never actually build the translator between them. The vision lives in a deck somewhere, inspiring and directionally right. The budget lives in a spreadsheet, built around this year’s known commitments. Nobody owns the job of making sure the two are actually talking to each other, which is exactly how a genuinely good preferable-future exercise ends up having zero measurable effect on what gets funded next quarter.

Backcasting instead of forecasting

The specific technique that closes this gap is one most strategy teams have heard of and few actually practice with any rigor: backcasting. Forecasting starts from today and projects forward, asking what’s likely to happen. Backcasting starts from your preferable future and works backward, asking what would have to be true three years from now for that future to be arriving on schedule — and then what would have to be true one year before that, and what would have to be true starting now. It’s a deceptively simple reversal, and it’s the single most effective move for turning an inspiring but abstract future into a concrete near-term action, because it forces specificity at every step backward instead of letting the vision stay comfortably vague.

Finding the moves that survive contact with uncertainty

Not every action that would help you reach your preferable future is equally safe to commit to today, because you’re not actually certain that future is the one that arrives. This is where the idea of a no-regret move earns its keep: an action worth taking regardless of which of your mapped futures actually materializes. Building a more flexible operating model, investing in a capability your organization is clearly underweight in no matter how the market shifts, strengthening a relationship or a data asset that pays off across multiple scenarios — these are the moves to prioritize first, precisely because committing to them doesn’t require you to bet the organization on one version of the future being right.

The moves that only make sense if your probable future specifically arrives, and would actively hurt you if a different future showed up instead, need a different treatment entirely. Those belong staged as real options — decisions you’ve deliberately prepared for but haven’t yet committed resources to, triggered by specific signposts rather than locked into this year’s roadmap by default.

Why sequencing matters more than most roadmaps admit

A roadmap that lists everything as equally urgent isn’t really a roadmap — it’s a wishlist with a shared deadline. Real sequencing means being honest about dependencies (what has to happen before something else becomes possible), capacity (what your organization can actually execute on simultaneously without diluting all of it), and the specific signposts that would tell you it’s time to accelerate or pause a given initiative. Skip this step, and even a well-backcast set of near-term moves ends up competing for the same quarter’s attention with no clear priority among them.

The step most translation efforts skip: ownership

A near-term move without a named owner tends to survive exactly as long as the enthusiasm from the planning session that produced it — usually a matter of weeks. The organizations that actually execute against a foresight-informed roadmap are the ones that assign a specific person to each near-term priority before the room disbands, not afterward in a follow-up email nobody reads closely.

This is what NowBuilder™ is built to do

This is the specific job NowBuilder™ handles inside FutureHacking™: taking your probable and preferable futures and running the room through backcasting, identifying genuine no-regret moves versus future-specific options, sequencing them against real dependencies and capacity, and assigning ownership before anyone leaves — so the output isn’t another deck, it’s an actual set of near-term priorities with names attached to them. It’s the half of the methodology that turns “here’s what could happen” into “here’s what we’re doing about it, starting now.”

Where to start

If your team hasn’t run a structured foresight exercise yet, the free FutureHacking Signal Picker is the right place to begin — it’s the foundational first stage the rest of the methodology, NowBuilder™ included, builds on.

Something new I’m building

I’m also finishing a second tool — the FutureCanvas Picker — that carries a planning team through the full arc in one sitting: from signals, to the trends they suggest, to a genuine set of possible futures, narrowing to your most probable future, and mapping the path to your preferable one. It’s the closest thing I’ve built yet to running a complete FutureHacking™ session on your own.

I’m opening early access to a select group first — strategic planners, CSOs, and leaders actively running planning processes right now — because I want real feedback from people using it under real deadline pressure before it’s available more broadly. If that’s you, and you’d like to be considered for early access, reach out and let me know — I’ll be following up personally with the first few who get in.

A future worth building toward deserves more than a deck. It deserves a name next to this quarter’s first move toward it.

Image Credits: Gemini

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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Evolving New Ideas

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Evolving New Ideas

GUEST POST from Mike Shipulski

Before there is something new to see, there is just a good idea worthy of a prototype. And before there can be good ideas there are a whole flock of bad ones. And until you have enough self confidence to have bad ideas, there is only the status quo. Creating something from nothing is difficult.

New things are new because they are different than the status quo. And if the status quo is one thing, it’s ruthless in desire to squelch the competition. In that way, new ideas will get trampled simply based on their newness. But also in that way, if your idea gets trampled it’s because the status quo noticed it and was threatened by it. Don’t look at the trampling as a bad sign, look at it as a sign you are on the right track. With new ideas there’s no such thing as bad publicity.

The eureka moment is a lie. New ideas reveal themselves slowly, even to the person with the idea. They start as an old problem or, better yet, as a successful yet tired solution. The new idea takes its first form when frustration overcomes intellectual inertia a strange sketch emerges on the whiteboard. It’s not yet a good idea, rather it’s something that doesn’t make sense or doesn’t quite fit.

The idea can mull around as a precursor for quite a while. Sometimes the idea makes an evolutionary jump in a direction that’s not quite right only to slither back to it’s unfertilized state. But as the environment changes around it, the idea jumps on the back of the new context with the hope of evolving itself into something intriguing. Sometimes it jumps the divide and sometimes it slithers back to a lower energy state. All this happens without conscious knowledge of the inventor.

It’s only after several mutations does the idea find enough strength to make its way into a prototype. And now as a prototype, repeats the whole process of seeking out evolutionary paths with the hope of evolving into a product or service that provides customer value. And again, it climbs and scratches up the evolutionary ladder to its most viable embodiment.

Creating something new from scratch is difficult. But, you are not alone. New ideas have a life force of their own and they want to come into being. Believe in yourself and believe in your ideas. Not every idea will be successful, but the only way to guarantee failure is to block yourself from nurturing ideas that threaten the status quo.

Image credits: 1 of 1,550+ FREE quotes for your presentations at http://misterinnovation.com

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Customer Experience Without Exceptions

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Customer Experience Without Exceptions

GUEST POST from Shep Hyken

This article answers the question: How can organizations deliver consistently excellent customer service even when customers are difficult, rude, or unappreciative?

Last year, I wrote an article on a “rule” in customer service that I called the Reality Rule: Treat customers well, regardless of how they treat you. This was inspired by a book I read, Give Hospitality by Taylor Scott. In the article, I talked about four rules.

So, first a little review:

The Golden Rule: We all know this one. Do unto others as you would have them do unto you. It shows up everywhere, from religion to leadership to customer service training.

The Platinum Rule: My friend Dr. Tony Alessandra came up with this one. Do unto others as they’d like done unto them. I’ve always loved this spin on the Golden Rule, because not all customers want the same experience.

The Employee Golden Rule: I came up with this years ago. Again, it’s a spin on the Golden Rule, but it’s focused on how employees are treated. Do unto employees as you want done unto customers. You can’t expect great customer service to come from employees who aren’t treated well by their managers, leaders, and colleagues.

The Rosa Parks Rule: In Scott’s book, there is a quote attributed to Rosa Parks, who in 1955 refused to give up her seat on a bus in Montgomery, Alabama, defying the racial segregation laws: Nothing in the Golden Rule says that others will treat us as we have treated them. It only says that we must treat others in a way that we would want to be treated.

I’m bringing all of this up again, because it’s worth doing so. And I have more to say about this.

If Rosa Parks’ words were to apply to business, they remind us that great customer service is unconditional.

By the way, this isn’t easy. I’ve seen great customer service reps go to great lengths to help customers, only to be met with indifference, rudeness, and a lack of appreciation. This can cause even the best of us to think, “Why should I care if they don’t?”

There is an answer: It shouldn’t matter!

How you respond is always your choice. Creating an amazing experience isn’t just for customers who say please and thank you. It’s for every customer, even if they are angry, rude, or difficult to please. By the way, abuse is never acceptable. There is always a line that should not be crossed.

The point is this: when you remove expectations, you stop being disappointed, you stop being resentful, and you start delivering consistent and intentional experiences that get customers to say, “I’ll be back!”

Image Credits: Unsplash

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AI and the Enterprise

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Quo Vadis?

AI and the Enterprise

GUEST POST from Geoffrey Moore

Everybody gets that AI is going to change the world, but nobody is really clear as to how, which would be OK except that CIOs everywhere are under pressure to invest now, at a time when things are still forming, norming, and storming. We need to step back for a moment and survey the landscape so we can prioritize where we should engage first and why.

Let me suggest the following framework as a point of departure:

I submit that most of the value of enterprise IT today is delivered by the two systems highlighted in bold—the systems of record, which include finance, HR, supply chain, purchasing, and the like, and the systems of engagement, which include sales, service, marketing, commerce, and the like. These are the mission-critical stakeholder systems that define every enterprise’s relationship with its customers, partners, investors, regulators, and employees. They are quite simply indispensable, and as such, they are the anchor tenants of the Stack, with all the other elements ultimately justifying their existence by being in service to one or the other of the two.

Both systems rose to prominence in the 1990s as client-server applications running atop the Internet, and in this century, they have been advanced dramatically by two sets of adjacent sets of systems, the systems of infrastructure, which include cloud computing, mobile computing, data management, cybersecurity, and hyperscale outsourcing on demand, and the systems of collaboration, which include video conferencing, file sharing, messaging, threaded discussions, e-signatures, and the like. These have enabled companies to expand both the external reach of their systems of engagement and the internal productivity of their systems of record.

Now, we are seeing the emergence of a third set of systems, still nascent, consisting of systems of intelligence, which include predictive AI and generative AI, and systems of autonomy, which include agents. Both are based on advanced statistical software that can learn, and both are driven by machine learning feasting on all the data it can hoover up, including massive troves of log files that were never before examined except forensically.

The question before us is how will systems of intelligence and systems of autonomy impact the operation of the Stack as we have known it to date.

The first claim of this framework is that the AI-enabled systems will operate in the middle of the Stack, not at the top, and not at the bottom. That is, they will not displace any of the other systems but rather, will enhance them dramatically by operating behind the scenes. To get to more specific claims, let’s look at each layer of the stack one at a time.

Systems of Infrastructure

With respect to our systems of infrastructure, the most astounding impact of AI to date is the realization of just how much compute and storage it can consume. To generate a competitive Large Language Model (LLM) requires a hyperscale compute footprint that only a handful of companies have the resources to deploy. When we hear that Microsoft has invested $13 billion in OpenAI, we can rest assured that the bulk of that will come in the form of compute service. Ditto for Amazon, Meta, and Google.

Everyone else will need to license one or more of these LLMs and then adapt it for use in their own Stack. These adaptations, in turn, will rely heavily on first-party data from the enterprise’s own systems of record and engagement, supplemented by data from their systems of collaboration, as well as licensed second-party data, and publicly available third-party data. Extracting all that data, normalizing the metadata, filtering out the information that is protected by data sovereignty regulations, and staging the rest in a data lake for real-time use cases represents the most immediate challenge for CIOs today.

For many real-time applications in the physical world, latency issues will dictate that some processing needs to be done at the edge rather than in the core. This calls for a new kind of PC server armed with a GPU as well as a CPU, running a real-time operating system, connected under next-generation cyber-security protection. By contrast, digital-only applications for the virtual world, including most use cases for systems of record and systems of engagement, require modest infrastructure changes, if any. The current generation of co-pilots all run on the current end-user platforms, with the heavy lifting all being done by either in the core.

Systems of Record

Systems of Record, as we have already noted, are the foundation of enterprise operations. They are intentionally conservative by design. This ensures their integrity, but at the expense of ease of use and adaptability to circumstance. AI changes this game dramatically.

Generative AI augments the click-based UI of the core system, which users need to be trained on, with a natural language interface that is much more forgiving and can be learned through trial and error. Advanced use cases can be created through prompt engineering, allowing a general-purpose LLM to be used in tandem with proprietary enterprise data to ensure privacy, integrity, and relevance. These prompts can be reused and are likely to become an important reservoir of trade secrets.

Predictive AI is an even bigger game-changer. We have been doing this long before the current AI wave but via software that is preprogrammed and does not learn. Machine learning allows for continuous discovery of next-best actions, be they for predictive maintenance, fraud detection, energy optimization, demand forecasting, or product sourcing. It is like having a six-sigma black belt on duty 24/7.

Agents are a bit more dicey, particularly for regulated applications or ones that pose liability issues. Here a human-in-the-loop co-pilot model is likely to prevail for a long time to come, even after it has been shown conclusively that agents can do the job as well as, or even better than, humans. Think X-ray diagnosis or self-driving cars.

Systems of Engagement

We are skipping up to the top because systems of record and systems of engagement have much in common. That said, they are less conservative because they must continuously adapt to unpredictable workflows based on prospect, customer, transaction type, market, and use-case.

Generative AI has a much bigger role to play in these market-facing applications because, in addition to all the UI benefits mentioned earlier, it can actually substitute for human beings in Level One interactions, including creating and running email marketing campaigns, fielding customer service requests through both email and chat, supplying field engineering professionals with on-demand technical support information, alerting sales professionals to next-best actions, and the like.

Predictive AI, on the other hand, is more of a stretch because human factors are less predictable than system behaviors. Nonetheless, data-driven decision-making trumps intuition in the long run, and advanced statistical software that learns outperforms even the best humans eventually, as our friends at DeepMind taught us with respect to the game of Go. Sales forecasting and market campaign attribution are two areas in particular where there is low-hanging fruit to pick. It just takes more patience and, given the reputational risks involved in market-facing interactions, a more prudent approach to rolling anything out at scale.

Agents fall somewhere in between in that, for highly routine interactions, they can shine and actually provide better customer service than humans, as we all learned many years ago from ATM machines who trumped in-bank tellers from day one. One thing to guard against here is to measure the success of such programs by cost savings instead of friction reduction, the latter creating a much bigger payoff in customer loyalty, active use, and churn reduction.

Finally, co-pilots for all customer-facing functions are really a no-brainer. They do not lose context, they do not lose track of time, and they do not get bored with routine. In addition, they are great at prompting and taking a first cut at any natural language task. The critical issue here is focus. The more tuned the co-pilot is to your specific business, the more impactful its contributions will be.

Systems of Collaboration

First of all, we need to appreciate the fact that systems of collaboration are a differentiating source of information for any enterprise. That is, more than any other source, they represent the quality and texture of all your relationships in flight, not just their progress toward achieving your targeted outcomes. They embody who you are and what you care about. By including such data in data lakes that feed your AI models, not only will you improve your systems of collaboration themselves but also make better recommendations to your systems of record and engagement.

Specific to improving collaboration itself, AI excels at summarizations that cut through the clutter of long communication threads, timely reminders that keep workflows from bogging down, and sentiment analyses that can detect concerns and improve tone. The more your enterprise depends on nuanced knowledge work, the higher such improvements need to be on your priority list.

Systems of Intelligence

As you can see from the foregoing, in my view, we should not think of systems of intelligence as a separate layer in the Stack but rather as a technology infusion that changes its very nature. That is, our core business systems are poised to become more intelligent. Indeed, a fruitful metric might be something like a “system IQ test,” which CIOs could use both to assess their current state and to target their future state, all to be done by overlaying and integrating a layer of advanced statistical software that learns. As the BASF slogan used to say, “We don’t make the product, we make it better.”

What we should not endorse is the notion that Systems of Intelligence, or indeed any form of AI, is going to “take over.” Yes, they could have unintended consequences, but no they could not have Game of Thrones ambitions.

With one possible exception.

Systems of Autonomy

Systems of autonomy are the natural extension of systems of intelligence wherever the task in question can be done better by a machine than a person. We use them to place digital advertising, to get astronauts to space stations, to orchestrate the Internet, to run the GPS applications that help us navigate the world, to detect military threats, to prevent spam. They are an indispensable part of the digital transformation we are still in the midst of, and their role will only increase going forward.

The question is when you take the human out of the loop, who or what is in charge? This is not an intractable problem, but it is also not one that will get solved theoretically. This one will require lived experience, a history of successes and blunders, some pleasant surprises, and some very unpleasant unintended consequences. We should not be shocked. Life never stops evolving, and natural selection never stops operating. We just need to step up.

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

— Image credit: Gemini, Geoffrey Moore

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When a Bad Review Goes Viral

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Turning a PR Moment Into a Real Fix

When a Bad Review Goes Viral

by Braden Kelley and Art Inteligencia

The moment a bad customer experience goes viral, an organization snaps into a very specific and very well-rehearsed mode: crisis response. Legal gets looped in. Communications drafts a statement. Someone reaches out to make the customer whole, quickly and visibly. All of that is necessary, and none of it answers the only question that actually matters once the immediate fire is out: was this a genuine one-off, or is it the first time anyone’s noticed something that’s been happening quietly for a while?

The PR response and the real diagnosis are two different jobs

Here’s the trap I’ve watched organizations fall into repeatedly: the PR response feels like it resolved the problem, because from the outside, it did. The story dies down. The affected customer is satisfied, or at least quiet. Leadership moves on, understandably relieved. But making one visible customer whole and understanding whether the underlying failure is systemic are two completely different exercises, and only one of them actually happened. The apology addressed the symptom that became public. It said nothing about whether the same failure is quietly happening to other customers who simply didn’t have a large enough platform for anyone to notice.

The customer who went viral is rarely the only one it happened to

This is the part that gets lost in the relief of a crisis passing: the customer whose complaint went viral usually isn’t unique in what happened to them. What’s unique is that they had an audience willing to amplify it. For every customer with the platform and inclination to make a bad experience public, there are likely dozens or hundreds who had the same experience, said nothing beyond a quiet complaint or no complaint at all, and simply left — or stayed, resentfully, waiting for a better alternative to come along. A viral incident isn’t the problem. It’s a visibility event for a problem that was probably already there, silently costing you customers who never made noise about it.

Why this moment is actually a rare opportunity, if you use it right

There’s an uncomfortable but useful truth about the period right after a public incident: it’s often the single easiest moment to get real budget and leadership attention for a genuine diagnostic effort. The urgency that made the crisis painful is the same urgency that can get a proper audit approved in days instead of the usual months of internal advocacy. Most organizations waste this window by spending it entirely on the public-facing fix and the internal postmortem meeting, then let the appetite for deeper investigation fade along with the news cycle. The smarter move is using that same window to actually find out whether this was a true outlier or a known category of failure hiding in plain sight.

What the diagnosis actually needs to answer

A proper look at this needs to walk the exact touchpoint that failed, the way the customer who went viral actually experienced it — not the documented process, the real one, with whatever gaps and workarounds have accumulated around it. It needs to check the existing data for any pattern of similar complaints that never individually rose to leadership’s attention, because a string of quiet, unconnected complaints about the same root issue often exists well before one of them goes public. And it needs a clear-eyed read on whether this really was a rare edge case, because overcorrecting an entire process based on a single dramatic incident carries its own cost — reacting to n=1 with organization-wide policy changes can create new friction for the vast majority of customers who never experienced the original problem at all.

Turning a bad week into a real fix

The organizations that come out of a viral moment genuinely stronger aren’t the ones with the best-worded apology. They’re the ones that used the moment’s unusual clarity and unusual leadership attention to actually find out whether they had a true one-off or a systemic gap, and fixed the right thing instead of just the visible thing.

If you’re in or just past this kind of moment and want an honest answer to which one you’re actually dealing with, a Customer Experience Audit scoped to the failed touchpoint is built for exactly this kind of diagnosis. And if you want a fast sense of what a silent, unaddressed version of this problem could be costing beyond the one visible incident, the CX ROI Calculator is a good place to start.

Download the Customer Experience Audit Checklist as a PDF

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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Do These 3 Things to Tell a Compelling Story

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Do These 3 Things to Tell a Compelling Story

GUEST POST from Greg Satell

There’s a great, although perhaps apocryphal, story about Franz Kafka and a little girl. The relatively unknown author—he wouldn’t achieve great fame until after his death—met a young girl who lost her doll. Kafka helped her look for it, but to no avail. The doll was lost forever and the girl was heartbroken.

But then Kafka told her a story. The next day he brought her a letter from the doll. “Please do not mourn me, I have gone on a trip to see the world.” Kafka would bring her letters telling her of the doll’s adventures. He eventually bought her another doll and gave it to her with another note that said, “my travels have changed me.”

As the story goes, after Kafka’s death the girl found another letter hidden in the replacement doll that said, “Everything that you love, you will eventually lose, but in the end, love will return in a different form.” We can’t all write like Kafka, but with a little bit of knowledge and some practice, we can all learn to tell stories that give meaning and purpose to our messages.

1. Create Tension

The first element of any story is its exposition, which is the world you build around the story. It includes the setting, the characters and other background information. That’s where most people start their stories, but it can be a trap, like when your mother starts a story about meeting someone at a drugstore and ten minutes later you’re still hearing about the grandchildren and wondering what the point is.

Master storytellers start with a tension or conflict, like the girl losing her doll in the Kafka story or a James Bond movie when it starts with him hanging from a helicopter and you have no idea why. It’s the tension or conflict that keeps our attention because we want to see it resolved. The Metamorphosis, the only novel Kafka ever finished, began with:

“As Gregor Samsa awoke one morning from uneasy dreams he found himself transformed in his bed into a gigantic insect.”

Another element of tension and conflict is the characters themselves. David Mitchell, author of Cloud Atlas, points out that we find characters like Darth Vader more interesting than one dimensional characters like Superman because they lack moral clarity. It is that ambiguity that makes them interesting and provokes thought and discussion.

Notice how in the story with the girl, the resolution to the lost doll creates its own tension. What will the letters say? Will she appreciate them, learn from them or see through the ruse? We appreciate the ending of the story not only because the tension was resolved, but in a way that honored the feeling of loss that created it.

2. Manage The Cognitive Budget

Born in the late 13th century, William of Ockham was a giant of his age. As one of the few intellectual lights of medieval times, his commentaries on reason, logic and political theory are studied even today. His ideas about the separation of church and state were literally centuries ahead of their time and formed the basis for our own constitutional principle.

Yet he’s best known for Ockham’s Razor, sometimes known as the “principle of parsimony.” Often, the principle is interpreted as “Keep It Simple Stupid,” but that’s not quite right. Notice how the Kafka story, although it’s short, has a sort of elegant complexity to it. The notion of a doll traveling around the world having adventures certainly isn’t simplistic.

A much more accurate translation would be, “entities should not be multiplied beyond necessity.” In other words, if something doesn’t need to be there, it shouldn’t be. Everything you include, should be intentional and have a purpose. Even a subplot needs to play a role in the overall narrative. Anything else just distracts from what you’re trying to say.

A useful device I use for applying Ockham’s razor is to imagine my audience, whether that is a reader or a listener, as having an internal “cognitive budget” they are willing to devote to whatever I’m trying to tell them. Then I judge everything I include by the standard of, “is this worth using up my cognitive budget?”

So be cautious and respectful with your audience’s attention. If you have any doubts whether it needs to be there, it probably doesn’t. Take it out and see if anything meaningful is lost. If not, keep it out and don’t look back.

3. Create “Buts” And “Therefores”

Being mindful of your audience’s cognitive budget doesn’t mean stories should be linear. In fact, you want twists and turns. South Park creators Trey Parker and Matt Stone call this their But & Therefore Rule. Learning how to master it can dramatically improve the way you tell stories and engage audiences.

The idea is that each element of a story is either a causal connection or a twist. There should never be a simple, “and then” linking two elements together. You can see Parker and Stone explaining the concept in this video:

We can see the concept at work in the Kafka story. Kafka found the girl crying, THEREFORE he helped her look for it BUT they couldn’t find it THEREFORE she was heartbroken. BUT he brought her the letter telling her the doll was going on a trip to see the world. BUT eventually he bought her a new doll. Kafka dies and their relationship ends, BUT he left her one final note.

Each THEREFORE reinforces the element that precedes it and each BUT creates a new twist. Everything has a purpose and nothing is there that doesn’t need to be, which is what makes the story so elegantly compelling.

We All Need To Learn To Leverage The Power Of Story

Some years back I was invited to visit the Institute for Advanced Study in Princeton. Over the years many of the world’s greatest minds have taken up residence there. It was where Einstein, along with other giants like Oppenheimer, von Neumann and Gödel, would reside until his death in 1955. It is a place, for me at least, in which stories permeate from every corner and crevice.

There is a common room in the main building, Fuld Hall, where tea is served every afternoon and, if you know the stories, you can almost hear the din of legends arguing, cajoling and discussing pathbreaking ideas when you enter. That is the power of story. It can imbue even inanimate objects with meaning. Without the stories, Fuld Hall is just a red brick building.

Look at great leaders throughout history, from General George Patton to Martin Luther King Jr. to Steve Jobs, and they all used the power of story to anchor an enterprise with a sense of mission and destiny. It was undoubtedly a big part of their success. We need to learn to tell better stories, if we are to give meaning to others and build faith in a common endeavor.

Stories, as Hollywood mogul Peter Guber has put it, provides emotional transport for ideas. Emotions are like little yellow highlighters in our brains, providing markers that tell us, “remember this, it’s important.” It is, of course, crucial to get your facts straight, but if you don’t learn how to tell a story, those facts will be easily forgotten.

Do yourself and those around you a favor. Learn how to tell stories and tell them well. Life’s too short to be boring or to be bored.

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

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Leadership Choices That Determine Whether or Not Your Business Grows

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Take a Hike!

Leadership Choices That Determine Whether or Not Your Business Grows

GUEST POST from Robyn Bolton

I recently listened to a podcast in which the speaker talked about his hike to Machu Picchu. He spoke about the difficulty of the hike and the moments when his confidence wavered. “But ultimately,” he said, “I was so compelled and pulled onward by the opportunity to see such a wonder, that I was able to push through.”

That was not my experience.

Many years ago, I did the same hike (in three days instead of four due to a scheduling error). And at no time did a feel “compelled and pulled onward.” In fact, about halfway through the first day’s hike, I had a complete meltdown in the middle of a beautiful grove of flowering trees. Luckily, I was so far behind the rest of my group that only my guide saw and heard the half-hour, expletive-laden beating of walking sticks against trees as I accused him of leading us to our deaths.

A few hours later, we reached our camp and the sherpas gave me tea and popcorn as they prepared dinner. I don’t know what was in the tea, but I felt much better after a cup and was grateful that a steady supply was offered throughout the next two days.

WHY you start matters

It was not the “opportunity to see such a wonder” that put me on the path. It was FOMO (fear of missing out), knowing that my friends were going on an adventure and not wanting to miss out.

Opportunity or FOMO. One of those is at the start of every journey and steels your mindset for the work ahead. If you see opportunity, you’re optimistic, resilient, and maybe even a bit idealistic. If you’re afraid, you rush through things, missing important signals and only seeing how far behind you are.

Companies do the same thing with innovation. They see a new technology, trend, or framework appear, sense an opportunity to use it to kickstart growth and leapfrog competition, and they start building. Or they see a new business model or competitor gain share and rush to mimic their approach.

WHAT you choose along the way determines how you end

It wasn’t “knowing where my journey was going, and what the journey was all about” that kept me moving forward. It was the knowledge that, unless I planned to join one of the Indigenous communities we passed through, I had to keep going.

No matter how you start, you will face a choice – continue, stay, or turn back – and that choice determines how your journey ends. If you turn back to the old ways because the new ways failed, you’re giving up. If you stay where you are, you’re stuck somewhere between the safety of what you knew and the opportunity ahead. If you keep going, you’ll stay ahead of those you never started, turned back, or stopped AND you’ll achieve the opportunity that “compelled and pulled [you] onward.”

Companies face the same decision moment with innovation. There’s a market downturn, geopolitical uncertainty, or a major global event, so executives shut down anything that’s not mission-critical while they wait out the uncertainty. A new leader takes the helm and wants to put her mark on the organization, so she rejects the old strategies and approaches and institutes her own, ignoring the counsel of others in the organization. A new competitor suddenly finds itself embroiled in controversy or bankruptcy, and executives chuckle and shake their heads because they knew all along that the only way that works is the old way.

What do you choose?

Do you start because you see the opportunity to do better or because you’re afraid of losing out?

When you face the inevitable challenge, do you turn back to “how we’ve always done things,” take up residence where you are because it’s good enough, or do you bravely persevere?

Most importantly, when you face the challenge, do you take a break, talk and listen to the people around you, and have some tea and popcorn before you make your choice?

Image credit: Pexels

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FutureSignals™: How to Separate Real Trends From Noise Before Your Next Planning Cycle

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by Braden Kelley and Art Inteligencia

Ask any leadership team to name a trend shaping their industry, and you’ll get an answer within seconds. Ask them to explain why they believe it’s real, as opposed to something that just happened to come up in three meetings this month, and the room usually goes quiet. Most organizations aren’t short on trends. They’re short on a way to tell which of the dozens of things competing for the label “trend” actually deserve it.

Why this matters more than people think

Every strategic plan I’ve ever reviewed has been shaped, at least in part, by trends nobody rigorously tested. Not because the people building the plan were careless, but because there’s rarely a formal step in the process for testing a trend before acting on it. Something gets mentioned by a well-regarded analyst, or shows up in three unrelated conversations in the same month, and it quietly graduates from “thing I noticed” to “trend we’re planning around” without ever passing through anything that could reasonably be called scrutiny. Get that step wrong at the start of a planning cycle, and everything built on top of it inherits the mistake.

What actually separates a signal from noise

A genuine signal has a few characteristics that noise almost never does, and it’s worth being explicit about them rather than trusting instinct alone.

It shows up from more than one independent direction. A single source, however credible, is a data point, not a trend. When you start seeing the same underlying shift from unrelated sources — a customer behavior pattern, an unrelated industry’s earnings call, a regulatory conversation, a technology adoption curve — that convergence is a much stronger indicator than any one source repeating itself loudly.

It has a mechanism, not just a correlation. Noise often sounds like a trend because two things happened around the same time. A real signal comes with an explanation for why it’s happening — an incentive that changed, a cost curve that shifted, a constraint that got removed — not just a coincidence in timing that a confident narrator strung together after the fact.

It’s sustained, not spiking. A lot of what gets treated as a trend is really just a spike — a burst of attention that fades within a quarter once the news cycle moves on. Real signals tend to persist and, more tellingly, tend to keep showing up even after the initial attention around them fades.

The traps that let noise through anyway

Even knowing these criteria, a few predictable biases let noise slip past them constantly. Recency bias — whatever you read most recently feels more significant than it actually is, purely because it’s fresh. Authority bias — a signal feels more credible because someone senior or well-known repeated it, regardless of whether they did any more rigorous evaluation than anyone else in the room. And the most dangerous one, confirmation bias — signals that happen to support what leadership already wants to believe about the future get waved through with far less scrutiny than signals that would require uncomfortable change. I’ve watched this last one derail more planning cycles than any of the others combined, because it’s the hardest one for a room to catch in itself.

Building a genuine filter, not just a gut check

The fix isn’t more research, exactly — it’s a consistent set of questions applied to every candidate signal before it earns a place in your planning conversation: Where else is this showing up, independently? What’s the actual mechanism driving it, and does that mechanism hold up under a second look? Has it persisted past its first burst of attention? And, the uncomfortable one worth asking every time — would we be this quick to believe it if it pointed toward a future we didn’t want?

This is exactly what FutureSignals™ is built to do

This filtering discipline is the first formal stage of FutureHacking™, and I built a specific component — FutureSignals™ — around exactly this problem: giving a team a structured, repeatable way to gather candidate signals and stress-test them against real criteria, before any of them get promoted into “trend we’re planning around” status. It’s the foundation the rest of the methodology builds on, because every later stage — the trends you map, the futures you build, the roadmap you eventually commit to — is only as good as the signals it started from.

Where to start

The free FutureHacking Signal Picker puts this exact discipline into practice, at no cost, before your next planning cycle — a genuinely useful way to arrive at your next strategy conversation with signals that have actually been tested, rather than whatever happened to come up most recently.

Something new I’m building

Most planning cycles fail long before the strategy gets built. They fail at the moment someone mistakes a spike for a signal, and nobody in the room had a way to catch it.

Image Credits: Gemini

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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Time to Stretch Yourself

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Time to Stretch Yourself

GUEST POST from Mike Shipulski

If the work doesn’t stretch you, choose new work. Don’t go overboard and make all your work stretch you and don’t choose work that will break you. There’s a balance point somewhere between 0% and 100% stretch and that balance point is different for everyone and it changes over time. Point is, seek your balance point.

To find the right balance point, start with an assessment of your stretch level. List the number of projects you have and sum the number of major deliverables you’ve got to deliver. If you have more than three projects, you have too many. And if you think you take on more than three because you’re superhuman, you’re wrong. The data is clear – multitasking is a fallacy. If you have four projects you have too many. And it’s the same with three, but you’d think I was crazy if I suggested you limit your projects to two. The right balance point starts with reducing the number of projects you work on.

Now that you eliminated four or five projects and narrowed the portfolio down to the vital two or three, it’s time to list your major deliverables. Take a piece of paper and write them in a column down the left side of the page. And in a column next to the projects, categorize each of them as: -1 (done it before), 0 (done something similar), 1 (new to me), 2 (new to team), 3 (new to company), 5 (new to industry), 11 (new to world).

For the -1s, teach an entry level person how to do it and make sure they do it well. For the 0s, find someone who deserves a growth opportunity and let them have the work. And check in with them to make sure they do a good job. The idea is to free yourself for the stretch work.

For the 1s, find the best person in the team who has done it before and ask them how to do it. Then, do as they suggest but build on their work and take it to the next level.

For the 2s, find the best person in the company who has done it before and ask them how to do it. Then, build on their approach and make it your own.

For the 3s, do your research and find out who in your industry has done it before. Figure out how they did it and improve on their work.

For the 5s, do your research and figure out who has done similar work in another industry. Adapt their work to your application and twist it into something magical.

And for the 11s, they’re a special project category that live in rarified air and deserve a separate blog post of their own.

Start with where you are – evaluate your existing deliverables, cull them to a reasonable workload and assess your level of stretch. And, where it makes sense, stop doing work you’ve done before and start doing work you haven’t done yet. Stretch yourself, but be reasonable. It’s better to take one bite and swallow than take three and choke.

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Have You Achieved Your 2026 Customer Experience Resolutions?

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Have You Achieved Your 2026 Customer Experience Resolutions?

GUEST POST from Shep Hyken

In January many people (and companies) take time to reset and prepare to kick off the new year. Many even make a plan. How well have you done to achieve these twelve New Year’s resolutions in the form of what you should STOP doing! (By the way, earlier in the year I wrote a similar article featuring five of these for Forbes. Read the article here.)

Before we get into the list, I went back over a year’s worth of articles, looking for the concepts I said we should do for our customers. Then I flipped them around and, instead of creating a list of resolutions to do, made a list of what to stop doing. So, here are a dozen resolutions that begin with the word stop:

Stop trying to WOW every customer: To WOW the customer at every interaction is impossible. Instead, focus on consistent, predictable experiences that build trust and confidence with your customers.
Stop wasting your customers’ time: Wasting a customer’s time sends the message that you don’t respect them. A generic example of this is when you call customer support and, while waiting on hold for an unreasonable period of time, you hear a message repeated: “Your call is important to us.” Obviously not!
Stop thinking AI is the answer: The company that thinks they can eliminate the customer support department with AI-fueled customer service is quickly finding out they can’t. AI is an answer, but not the answer. It takes a balance between AI and humans to create the best customer service experience.
Stop making customers repeat themselves: Pay attention to what the customer is saying the first time. Take notes, so if they call back or talk to someone else, there’s a record, and the customer doesn’t have to start over.
Stop hiding behind company policy: It still surprises me to hear employees say, “That’s company policy.” When used the wrong way, those three words are customer loyalty killers. The policy should be to find ways to ensure customers come back.
Stop treating customer service as a cost center: When done well, customer service keeps customers coming back again and again. That’s marketing. When the ROI of your customer service reduces churn and adds to the bottom line, it’s a revenue generator.
Stop using acronyms and company jargon: Using initials and words used on “the inside” of a company may make customers uncomfortable. When they don’t understand or are confused because of what you say, you have to work hard to earn back their confidence.
Stop thinking surveys give you the best feedback: I’m a fan of surveys when done the right way. They get your customers’ feedback, but perhaps a better source of that important information is your front line. So, recognize frontline employees as a valuable source of customer feedback.
Stop thinking “We’ve already put our employees through customer service training”: Customer service training is not something you did. It’s something you do. It takes ongoing reinforcement of your original training to keep good employees customer-focused.
Stop thinking loyalty programs create loyalty: There are a few loyalty programs that create true customer loyalty, but realize that loyalty programs are usually marketing programs focused on getting customers to come back. There’s nothing wrong with that, but remember that repeat customers aren’t always loyal customers.
Stop assuming that if your customer doesn’t complain, they have nothing to complain about: Customers don’t always complain to you, but they will complain about you to their friends and colleagues. Silence does not necessarily mean happiness.
Stop solving problems, and start solving customers: I recently interviewed David Fuhr, the chief sales officer at Sweetwater, for an upcoming episode of Amazing Business Radio. When we were discussing problem-solving and complaints, he said, “We solve the customer.” He went on to say that you first solve the customer, as in resolving the issue and winning back their confidence, and then you work with the team to find out why there was a problem and how it can be prevented from happening again. That’s a perfect example of being customer-focused.

And there you have it. Twelve ideas of what to stop doing. There are many more, and not just from articles that I’ve written. Take a look at the processes that impact your customers. What do they complain about? Create your own list of what to stop doing. It’s not just what you do. Sometimes it’s what you don’t do that gets customers to say, “I’ll be back!”

Image Credits: Pixabay

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