Four Pillars of Innovation

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People, Learning, Judgment and Trust

Four Pillars of Innovation

GUEST POST from Mike Shipulski

Innovation is a hot topic. Everyone wants to do it. And everyone wants a simple process that works step-wise – first this, then that, then success.

But Innovation isn’t like that. I think it’s more effective to think of innovation as a result. Innovation as something that emerges from a group of people who are trying to make a difference. In that way, Innovation is a people process. And like with all processes that depend on people, the Innovation process is fluid, dynamic, complex, and context-specific.

Innovation isn’t sequential, it’s not linear and cannot be scripted.. There is no best way to do it, no best tool, no best training, and no best outcome. There is no way to predict where the process will take you. The only predictable thing is you’re better off doing it than not.

The key to Innovation is good judgment. And the key to good judgment is bad judgment. You’ve got to get things wrong before you know how to get them right. In the end, innovation comes down to maximizing the learning rate. And the teams with the highest learning rates are the teams that try the most things and use good judgement to decide what to try.

I used to take offense to the idea that trying the most things is the most effective way. But now, I believe it is. That is not to say it’s best to try everything. It’s best to try the most things that are coherent with the situation as it is, the market conditions as they are, the competitive landscape as we know it, and the the facts as we know them.

And there are ways to try things that are more effective than others. Think small, focused experiments driven by a formal learning objective and supported by repeatable measurement systems and formalized decision criteria. The best teams define end implement the tightest, smallest experiment to learn what needs to be learned. With no excess resources and no wasted time, the team wins runs a tight experiment, measures the feedback, and takes immediate action based on the experimental results.

In short, the team that runs the most effective experiments learns the most, and the team that learns the most wins.

It all comes down to choosing what to learn. Or, another way to look at it is choosing the right problems to solve. If you solve new problems, you’ll learn new things. And if you have the sightedness to choose the right problems, you learn the right new things.

Sightedness is a difficult thing to define and a more difficult thing to hone and improve. If you were charged with creating a new business in a new commercial space and the survival of the company depended on the success of the project, who would you want to choose the things to try? That person has sightedness.

Innovation is about people, learning, judgement and trust.

And innovation is more about why than how and more about who than what.

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Measuring Human vs. AI Satisfaction

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Insights from Customer Contact Week (Nashville)

LAST UPDATED: October 30, 2025 at 1:00PM

Measuring Human vs. AI Satisfaction

by Braden Kelley

One of the sessions I had the opportunity to attend at Customer Contact Week in Nashville featured Brian Cantor (Customer Management Practice), Gene Kropfelder (Ally Financial) and Scott Rhinehart (Alorica). Two overarching themes of the session were that bringing technology and human elements together continues to be a challenge and that with every interaction you are either building or destroying trust. Gene highlighted that his outlook is that people don’t need another bank they need a better bank, and this mindset is something that he tries to help permeate throughout the organization. Two ways they try to be that better bank in customer experience terms is by first, creating transparency around the wait times in different support channels and second, by trying to make every interaction feel different/better than the interactions they have with Ally’s competitors.

Artificial Intelligence (AI) was obviously a focus of the session and the fact that personalization requires knowing the interactions a customer has had and what they’ve been exposed to, but AI (or humans) have to leverage that knowledge in a helpful and not a creepy way.

When it comes to AI, it’s not what AI can do, but what does it do better and what activities should it take on. Implementing AI in any situation should start with a shared understanding of what problem you’re trying to solve, clearly defined outcomes (work backwards from these, utilizing well-defined checkpoints), a well-communicated WIFM (What’s in it for me?), and a crawl/walk/run approach. Don’t go too big too fast.

We have learned along the way that artificial intelligence can be very useful for helping support agents lean in with the information they (and customers) need. AI as a tool to summarize research query responses versus a list of search results (links). AI can help us understand where agents are spending the time and serve as a knowledge base auditor (article inconsistencies, gaps, etc.).

One important question that came up is “How can we help with handle time?”:

  • CSAT and NPS are important but handle time is important not just for the company (cost) but also for the customer – so they can get back to their life

A more challenging question that all contact center managers must ask is “How do you measure AI satisfaction versus human satisfaction?”:

  • The reason this is a challenging questions is because most of the easily solvable questions will go the AI and the cases that often don’t have a good resolution will be the ones that go to humans
  • At the same time we need to consider the impact of automating certain types of calls (often the easy ones). Will this make the human’s job more stressful (no easy calls)? Will they need more breaks as a result?
  • In the quest for efficiency be careful not to have your humans provide a robotic experience (reading scripts, no authority or decision making capability or flexibility)

Our humanity (both customer and employee) will always be important. Both have to learn to trust AI outputs, impacting the rate and amount of adoption, especially if you don’t have a plan for how to build that trust over time. And, it is important to have human retain accountability – AI generated, human reviewed before submission (i.e. call summaries). Finally, it is important to understand that while we may view the digital native generations as preferring a technological solution, the reality is that even the younger generations will still reach out for a human touchpoint on important questions/issues. So we humans are safe – for now.

HALLOWEEN BONUS: Save 30% on the eBook, hardcover or softcover of my latest book Charting Change (now in its second edition) — FREE SHIPPING WORLDWIDE — using code HAL30 until midnight October 31, 2025

Image credits: Customer Management Practice

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Doing the Basics Can Be Surprising

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Doing the Basics Can Be Surprising

GUEST POST from Shep Hyken

Just in case you didn’t know, I have a weekly customer service and Customer Experience (CX) podcast, Amazing Business Radio, that has been running for 12 years. Each week, I get the honor of interviewing some very smart people. Recently, I met Eric Stone, author of Jumpstart Your Workplace Culture. One of the topics we discussed was the WOW experience.

Before we go further, I have always had concerns about organizations that attempt to WOW their customers at every interaction. It’s just impossible to do. The concept of WOW is often associated with going above and beyond what’s expected, so much so that the customer is “blown away” by the experience. The problem is that’s not always possible. That’s why I took it out of my vocabulary and started using the word amazing to describe what I think WOW really is all about, which is a consistent and predictable experience that ALWAYS meets, and only occasionally exceeds, the customer’s expectations. It’s easy to say, “The people at that company are amazing.” It’s a little clunky and awkward to say, “The people at that company are WOW.”

That’s why I loved what Stone said: “Wow is a surprise.” In other words, it’s not about every experience. It’s about the occasional opportunity to surprise a customer. It could be something big, such as handling an emergency situation in which employees go above and beyond. As I said earlier, you can’t count on that type of opportunity for every transaction, but what you can do is find little ways to surprise your customers. For example, if you return a call quickly, the customer might say, “Wow, that was fast!” That’s not an over-the-top or above-and-beyond experience, but it’s a small surprise.

With that in mind, let’s use the return phone call as an example. When you know people are often surprised and make comments like, “Wow, that was fast,” simply because you called them back quickly, don’t just say, “Thank you,” and feel good about the experience. Instead, operationalize the experience. In other words, make quickly returned calls part of your official process. Set a standard, such as all calls are to be returned within two hours (or whatever the appropriate length of time you feel is right for you).

With that as an example, what other interactions do you have with customers that need to be formally operationalized? Here are a few ideas to get your creative juices flowing:

  • All phone calls are to be answered within three rings.
  • A promise to only transfer a customer once, knowing you’ll get the customer to the right person the first time.
  • Proactive updates to inform customers before they reach out to you asking for information.
  • Following up after a resolution to make sure a customer is still happy.
  • Acknowledging customer milestones, such as congratulating them on their 10th order with you, their birthday, etc.

None of these ideas are above-and-beyond types of WOW experiences, but they will cause a customer to notice. Use these examples to help you get creative about finding other simple or small WOWs you can operationalize and make part of the customer’s regular experience when they do business with you.

HALLOWEEN BONUS: Save 30% on the eBook, hardcover or softcover of Braden Kelley’s latest book Charting Change (now in its second edition) — FREE SHIPPING WORLDWIDE — using code HAL30 until midnight October 31, 2025

Image Credits: Pexels

This article was originally published on Forbes.com

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The Intersection Between Ethics and Metaphysics

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The Intersection Between Ethics and Metaphysics

GUEST POST from Geoffrey A. Moore

Ethics partners with metaphysics in order to create strategies for living. Metaphysics provides the situation analysis and ethics the prescribed course of action. The two are indispensable to one another. Metaphysics without ethics is idle speculation, ethics without metaphysics, arbitrary action. Taken together, however, they supply our fundamental equipment for living.

In that context, ethics is chartered to help us “do good.” It has two central questions to answer: What kind of good should we want to bring about? and What is the right way to achieve that end? Each one raises its own set of issues to work through.

With respect to what is good, the core issue is that, in English at any rate, the word good has three distinct meanings. It can refer to what is pleasurable, what feels good. It can refer to what is fit for purpose, what works good. And it can refer to actions beneficial to others, what I would argue simply is good. Importantly, these three dimensions can team up with one another to create as many as eight different categories of goodness, illustrated by the table below:

Geoffrey Moore Pleasurable Effective Table

Many of the ethical quandaries philosophers wrestle with arise from trying to unite some or all of these categories into a single concept of goodness. This is simply a mistake. That said, the type of goodness that is most proper to ethics is benevolence, actions beneficial to others (see rows 1,2, 5, and 6). It need not concern itself with either pleasure or effectiveness, both of which, while certainly desirable, are intrinsically amoral.

Focusing on actions beneficial to others, the core of ethics is prescriptive, offering behavioral guidelines that are most likely to generate benevolent outcomes. This is the realm of virtue. Once again, however, there is more than one dimension to take into account, leading to more than one kind of virtue. In this case, it is determined by the situation or context in which the action is undertaken, what we called in The Infinite Staircase the geography of ethics.

The geography of ethics is organized into four zones divided by two defining axes. The first axis distinguishes between society and community, the former being the realm of impersonal third-party relationships, the latter that of personal first-and-second-party relationships). This is essentially the distinction between them and us, and while in its polarized form it can be highly disruptive, it is nonetheless universally observed and absolutely essential to managing human relationships.

The second axis addresses the degree of contact involved, contrasting global situations which involve large populations that have little to no direct contact with each other versus local situations where we participate in exchanges with people we encounter in our daily lives. There is still a distinction between them and us, but local relationships require us to enact and incorporate our responses into our everyday behavior.

When paired, the axes generate four zones, each highlighting a different virtue:

Geoffrey Moore Geography of Ethics

Kindness is unique in that it is the only virtue that is universally valued. It is anchored in unconditional love, something that we as mammals have all personally experienced in our infancy, else we would not be alive today. Unlike the other virtues called out here, it does not depend upon the resources of culture, language, narrative, and analytics to activate itself. Once we engage with those forces, we will find ourselves increasingly at odds with people who have opposing views, but prior to so doing, we are all one family. Kindness, thus, is the glue that holds community together, and as such it deserves our greatest respect.

Fairness comes next. The ability to play fair, something children learn at a very early age, sets us apart from all other animals. That’s because it calls upon narrative and analytics to operationalize itself. Specifically, it asks us to imagine a situation in which we are the other person, and they are us, and to then determine whether or not we would endorse the action under consideration. This is the first bridge to connect us with them, and thus is the foundation for social equity and inclusion. Importantly, it is distinct from kindness, for it is possible to be kind without being fair and to be fair without being kind. Kindness by nature is personal, fairness by nature is impersonal, and together they govern our day-to-day ad hoc relationships.

To scale beyond local governance we must transition from the essentially intuitive disciplines of kindness and fairness to the more formalized ones of morality and justice. Both the latter are essential to social welfare, but neither comes into being easily, and each poses challenges humankind continues to struggle with.

Morality is the actionable extension of metaphysics. It teaches us how to align our behavior with the highest forces in the universe, be they sacred or secular. It does so through inspirational narratives that recruit us into imitating role models and committing to values we will live by, and if necessary, be willing to die for. These values are captured in moral codes that assist our day-to-day decision-making. We judge ourselves and others in terms of how well our actual behavior measures up to these codes.

In this way morality becomes foundational to identity. As such, we want it to be both stable and authoritative. Religion provides stable authority by holding certain texts and traditions to be both sacred and undeniable. This works fine up to the boundaries of the religious faith, but beyond that, it encounters disbelief and unbelief, as well as counter-beliefs, all of which deny such authority. The question for the believers then becomes, is such denial acceptable, or must it be confronted and overcome?

Call this the challenge of righteousness. Deeply moved by their own commitments, the righteous seek to impose moral sanctions on entire populations that do not share their views. The current engagement with abortion rights in the U.S. is a relatively benign example. Conservative parties empowered by the recent action of the Supreme Court are challenging a secular tradition of tolerance that is deeply ingrained in American culture. This tolerance is anchored by the First Amendment’s guarantee of religious freedom, itself a product of the European Enlightenment’s efforts to counteract more than a hundred years of sustained religious warfare between Protestants and Catholics, fueled by righteousness of a similar kind. At present, the First Amendment still has the upper hand, but in other societies, we have watched the opposite unfold, and it can leave deep rents in the social fabric.

Whereas conservatives on the right are challenged when they seek to bend the domain of morality to their ends, progressives on the left are equally challenged when they seek to bend the domain of justice to theirs. Justice represents society’s best attempt to institutionalize fairness at scale. It is comprised of two domains—legal justice and social justice. Legal justice represents the rule of law. It is foundational to safety and security, ensuring accountability with respect to personal acts, laws, elections, and dispute resolution. Social justice, in contrast, represents a commitment to equity. It is aspirational, anchored in empathy for all those who are disadvantaged.

The challenge is that legal justice can reinforce, even institutionalize, social injustice, as both our prison and homeless populations bear witness. This is further exacerbated by failed autocratic states exporting their disadvantaged populations to democratic nations, creating crises of immigration around the world. In response, progressives committed to social justice often seek to subvert legal controls in order to create more equitable outcomes, turning a blind eye to illegal immigration and encampments, as well as misdemeanor crimes like shoplifting and drug use. This has the unintended consequence, however, of encouraging free riders to further exploit these looser controls, pushing the boundaries of tolerance ever closer to intolerability, as cities like San Francisco, Portland, and Seattle can testify.

To operate successfully at scale, both morality and justice call for a balance between accountability and empathy. The righteous tend to withdraw empathy in the name of accountability, the progressives to withdraw accountability in the name of empathy. Neither approach suffices. Citizenship calls for us all to hold these two imperatives in tandem, even when they pull us in opposite directions.

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

HALLOWEEN BONUS: Save 30% on the eBook, hardcover or softcover of Braden Kelley’s latest book Charting Change (now in its second edition) — FREE SHIPPING WORLDWIDE — using code HAL30 until midnight October 31, 2025

Image Credit: Unsplash and Geoffrey Moore

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The Indispensable Role of CX

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Insights from CCW’s 25-Year Journey

LAST UPDATED: October 28, 2025 at 12:00PM
The Indispensable Role of CX

by Braden Kelley

I recently had the privilege of sitting down with Mario Matulich, President of Customer Management Practice, at Customer Contact Week (CCW) in Nashville. As an organization celebrating its 25th anniversary, CCW has been a critical barometer for the entire customer experience and contact center industry. Our conversation wasn’t just a look back, but a powerful exploration of the strategic mandate facing CX leaders today, particularly how we manage innovation and human-centered change in an era dominated by AI and tightening budgets.

CCW at 25: The Hub for Benchmarking and Breakthroughs

Mario underscored that CCW is far more than just a conference; it’s a living repository of industry knowledge. Professionals attend for actionable takeaways, which primarily fall into three categories: benchmarking performance against industry leaders, learning about new trends (like Generative AI’s impact), and, critically, sourcing the right vendors and capabilities needed to execute their strategies. It’s where leaders come to calibrate their investment strategies and learn how to do more with their finite resources.

Mario MatulichThis pursuit of excellence is driven by a single, powerful market force: The Amazon Effect. As Mario put it, customers no longer judge your experience solely against your industry peers. They expect every single touchpoint with your company to be as seamless, intuitive, and effective as the best experience they’ve had anywhere. This constantly escalating bar for Customer Effort Score (CES) and Customer Satisfaction (CSAT) makes a complacent CX investment a near-fatal strategic mistake. The customer experience must always be top-tier, or you simply lose the right to compete.

The Strategic Disconnect: CX vs. The Contact Center

One of the most valuable parts of our discussion centered on the subtle, yet crucial, distinction between a Customer Experience (CX) professional and a Contact Center (CC) professional. While both are dedicated to the customer journey, their scope and focus often differ:

  • The CX Professional: Often owns the entire end-to-end customer journey, from marketing to product use to support. Their responsibilities and definition of success are deeply influenced by where CX sits organizationally — is it under Marketing, Operations, or the CEO?
  • The CC Professional: Focused on the operational efficiency, quality, and effectiveness of the voice and digital support channels. Their reality is one of doing a lot with a little, constantly asked to manage complex interactions while being, ironically, often looked to as a prime source of cuts in a downturn.

Social media, for instance, is still a relevant customer service channel, not just a marketing one. However, the operational reality is that many companies, looking for cost-effective solutions, outsource social media support to Business Process Outsourcing (BPO) providers, highlighting the ongoing tension between strategic experience design and operational efficiency.

“Being a CX leader in your industry is not a temporary investment you can cut and reinstate later. Those who cut, discover quickly that regaining customer trust and market position is exponentially harder than maintaining it.” — Braden Kelley

AI in the Contact Center: From Hypothesis to Hyper-Efficiency

The conversation inevitably turned to the single biggest factor transforming the industry today: Artificial Intelligence. Mario and I agreed that while the promise of AI is vast, the quickest, most immediate win for nearly every organization lies in agent assist.

This is where Generative AI tools empower the human agent in real-time — providing instant knowledge base look-ups, auto-summarizing previous interactions, and drafting responses. It’s a human-centric approach that immediately boosts productivity and confidence, improving Agent Experience (AX) and reducing training time.

However, implementing AI successfully isn’t a “flip-the-switch” deployment. The greatest danger is the wholesale adoption of complex technology without rigor. True AI success, Mario noted, must be implemented via the classic innovation loop: hypothesis, prototyping, and testing. AI isn’t a solution; it’s a tool that must be carefully tuned and validated against human-centered metrics before scaling.

The Mandate for Enduring Investment

A recurring theme was the strategic folly of viewing CX as a cost center. In a downturn, the contact center is often the first place management looks for budgetary reductions. Yet, the evidence is overwhelming: CX leadership is not a temporary investment. When you are leading in your industry in customer experience, that position must be maintained. Cut your investment at your peril, and you risk a long, painful road to recovery when the market turns. The CX team, despite being resource-constrained, often represents the last line of defense for the brand, embodying the human-centered change we preach.

As CCW moves into its next 25 years, the lesson is clear: customer expectations are only rising. The best leaders will leverage AI not just to cut costs, but to augment their people and apply the innovation principles of rigorous testing to truly master the new era of customer orchestration. The commitment to a great customer experience is the single, enduring investment that will future-proof your business.

HALLOWEEN BONUS: Save 30% on the eBook, hardcover or softcover of my latest book Charting Change (now in its second edition) — FREE SHIPPING WORLDWIDE — using code HAL30 until midnight October 31, 2025

Image credits: Customer Management Practice

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

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Reduce Innovation Risk with this Nobel Prize Winning Formula

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Reduce Innovation Risk with this Nobel Prize Winning Formula

GUEST POST from Robyn Bolton

As a kid, you’re taught that when you’re lost, stay put and wait for rescue. Most executives are following that advice right now—sitting tight amid uncertainty, hoping someone saves them from having to make hard choices and take innovation risk.

This year’s Nobel Prize winners in Economics have bad news: there is no rescue coming. Joel Mokyr, Philippe Aghion, and Peter Howitt demonstrated that disruption happens whether you participate or not. Freezing innovation investments doesn’t reduce innovation risk.  It guarantees competitors destroy you while you stand still.

They also have good news: innovation follows predictable patterns based on competitive dynamics, offering a framework for making smarter investment decisions.

How We Turned Stagnation into a System for Growth

For 99.9% of human history, economic growth was essentially zero. There were occasional bursts of innovation, like the printing press, windmills, and mechanical clocks, but growth always stopped.

200 years ago, that changed. Mokyr identified that the Industrial Revolution created systems connecting two types of knowledge: Propositional knowledge (understanding why things work) and Prescriptive knowledge (practical instructions for how to execute).

Before the Industrial Revolution, these existed separately. Philosophers theorized. Artisans tinkered. Neither could build on the other’s work. But the Enlightenment created feedback loops between theory and practice allowing countries like Britain to thrive because they had people who could translate theory into commercial products.

Innovation became a system, not an accident.

Why We Need Creative Destruction

Every year in the US, 10% of companies go out of business and nearly as many are created. This phenomenon of creative destruction, where companies and jobs constantly disappear and are replaced, was identified in 1942. Fifty years later, Aghion and Howitt built a mathematical model proving its required for growth.

Their research also lays bare some hard truths:

  1. Creative destruction is constant and unavoidable. Cutting your innovation budget does not pause the game. It forfeits your position. Competitors are investing in R&D right now and their innovations will disrupt yours whether you participate or not.
  2. Competitive position predicts innovation investments. Neck-to-neck competitors invest heavily in innovation because it’s their only path to the top. Market leaders cut back and coast while laggards don’t have the funds to catch-up. Both under-invest and lose.
  3. Innovation creates winners and losers. Creative destruction leads to job destruction as work shifts from old products and skills to new ones. You can’t innovate and protect every job but you can (and should) help the people affected.

Ultimately, creative destruction drives sustained growth. It is painful and scary, but without it, economies and society stagnate. Ignore it at your peril. Work with it and prosper.

From Prize-winning to Revenue-generating

Even though you’re not collecting the one million Euro prize, these insights can still boost your bottom line if you:

  • Connect your Why teams with your How teams. Too often, Why teams like Strategy, Innovation, and R&D, chuck the ball over the wall to the How teams in Operations, Sales, Supply Chain, and front-line operations. Instead, connect them early and often and ensure the feedback loop that drives growth
  • Check your R&D and innovation investments. Are your R&D and innovation investments consistent with your strategic priorities or your competitive position? What are your investments communicating to your competitors? It’s likely that that “conserving cash” is actually coasting and ceding share.
  • Invest in your people and be honest with them. Your employees aren’t dumb. They know that new technologies are going to change and eliminate jobs. Pretending that won’t happen destroys trust and creates resistance that kills innovation. Tell employees the truth early, then support them generously through transitions.

What’s Your Choice?

Playing it safe guarantees the historical default: stagnation. The 2025 Nobel Prize winners proved sustained growth requires building innovation systems and embracing creative destruction.

The only question is whether you will participate or stagnate.

HALLOWEEN BONUS: Save 30% on the eBook, hardcover or softcover of Braden Kelley’s latest book Charting Change (now in its second edition) — FREE SHIPPING WORLDWIDE — using code HAL30 until midnight October 31, 2025

Image credit: Wikimedia Commons

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The Voicebots are Coming

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Your Next Customer Support Agent May Not Be a Human

LAST UPDATED: October 27, 2025 at 1:00PM
The Voicebots are Coming

by Braden Kelley

Last week I had the opportunity to attend Customer Contact Week (CCW) in Nashville, Tennessee and learn that the familiar, frustrating tyranny of the touch-tone IVR (Interactive Voice Response) system is finally ending. For too long, the gateway to customer service has felt like a maze designed to prevent contact, not facilitate it. But thanks to the rapid evolution of Conversational AI — fueled by Generative Large Language Models (LLMs) — the entire voice interaction landscape is undergoing a revolutionary, and necessary, change. As a thought leader focused on human-centered change, innovation and experience design, I can tell you the future of the call center isn’t purely automated; it’s intelligently orchestrated.

The voicebot — the modern AI-powered voice agent — is moving past its days as a simple chatbot with a synthesized voice. Today’s AI agents use Natural Language Processing (NLP) to understand intent, context, and even tone, allowing them to handle complex, multi-step issues with startling accuracy. More importantly, they are ushering in the era of the bionic contact center, where the human agent is augmented, not replaced. This hybrid model — where AI handles the heavy lifting and humans provide empathy, complex reasoning, and necessary approvals — is the key to achieving both massive scale and superior Customer Experience (CX).

Overcoming the Voice Friction: The Tech Foundation

The shift to true voice AI required overcoming significant friction points that plagued older systems:

  • Barge-In and Latency: Modern voicebots offer near-instantaneous response times and can handle barge-in (when a customer interrupts the bot) naturally, mimicking human conversation flow.
  • Acoustic Noise: Advanced speech recognition models are highly resilient to background noise and varied accents, ensuring high accuracy even in noisy home or car environments.
  • Intent Nuance: LLMs provide the deep contextual understanding needed to identify customer intent, even when the customer uses vague or emotional language, turning frustrated calls into productive ones.

The Dual Pillars of Voice AI in CX

Conversational AI is transforming voice service through two primary deployment models, both of which reduce Customer Effort Score (CES) and boost Customer Satisfaction (CSAT):

1. Full Call Automation (The AI Front Line)

This model is deployed for high-volume, routine, yet critical interactions. The voicebot connects directly to the company’s backend systems (CRM, ERP, knowledge base) to pull personalized information and take action in real-time. Crucially, these new AI agents move beyond rigid scripts, using Generative AI to create dynamic, human-like dialogue that resolves the issue instantly. This 24/7 self-service capability slashes queue times and dramatically lowers the cost-to-serve.

2. Human-AI Collaboration (The Bionic Agent)

This is where the real human-centered innovation lies. The AI agent handles the bulk of the call — identifying the customer, verifying identity, diagnosing the problem, and gathering data. When the request hits a complexity threshold — such as requiring a policy override, handling an escalated complaint, or needing a final human authorization — the AI performs a contextual handoff. The human agent receives the call along with a complete, structured summary of the conversation, the customer’s intent, and often a recommended next step, turning a frustrating transfer into a seamless, empowered human interaction.

OR, even better can be the solution where a single human agent provides approvals or other guidance to multiple AI voice agents that continue owning their calls while waiting for the human to respond (possibly simultaneously helping the customer with additional queries) before continuing with the conversation through to resolution.

Customer Contact Week Nashville

“The most powerful application of voice AI isn’t automation, it’s augmentation. By freeing human agents from transactional drudgery, we elevate them to be empathic problem solvers, enhancing both their job satisfaction and the customer’s outcome.” — Braden Kelley

Measuring the Success of the Handoff

The quality of the transitions between AI and human is the true measure of success. Leaders must track metrics that assess the efficacy of the handoff itself:

  • Repeat Story Rate: The percentage of customers who have to repeat information to the human agent after an AI handoff. This must be near zero.
  • Agent Ramp-up Time (Post-Transfer): The time it takes for the human agent to absorb the AI-generated context and take meaningful action. Lower is better.
  • Post-Handoff CSAT: The customer satisfaction score specifically captured after a complex AI-to-human transfer, measuring the seamlessness of the experience.

The Agentic Future

The voicebots are indeed coming, and they are bringing with them the most significant shift in customer service since the telephone itself. The next evolution will see agentic AI — bots that can dynamically choose between multiple tools and knowledge sources to resolve novel problems without being strictly pre-scripted. The challenge for leaders is to ensure that as this technology scales, our focus remains firmly on the human experience, leveraging the best of AI’s speed and the best of human empathy to create a truly effortless and satisfying customer journey.

🤖 Companies to Watch in AI Voicebots

The voicebot space is rapidly evolving, driven by generative AI, and the recent Customer Contact Week (CCW) in Nashville highlighted several key players. Companies to watch in this generative AI voicebot and contact center space include market-leading platforms like NICE, Genesys, Zoom and Five9, all of whom are heavily integrating generative and agentic AI features—such as real-time coaching and automated post-call summaries — into their core Contact Center as a Service (CCaaS) offerings.

Beyond the traditional CCaaS providers, specialist AI firms like Replicant, Voice.AI and ASAPP (who had a significant presence at the event) continue to stand out by focusing on either full end-to-end voice automation for complex transactions or providing advanced Human-in-the-Loop AI features to augment live agents, particularly in regulated industries like financial services.

Additionally, major cloud vendors like Google Cloud and AWS (Amazon Connect) are increasingly aggressive, leveraging their foundational AI models to provide scalable, next-generation AI agents and contact center platforms, ensuring they remain transformative forces in customer experience (CX) automation.

HALLOWEEN BONUS: Save 30% on the eBook, hardcover or softcover of my latest book Charting Change (now in its second edition) — FREE SHIPPING WORLDWIDE — using code HAL30 until midnight October 31, 2025

Image credits: Customer Management Practice, Google Gemini

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

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The Future of Humanity in an Artificially Intelligent World

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The Future of Humanity in an Artificially Intelligent World

GUEST POST from Greg Satell

The Argentinian writer Jorge Borges had a fascination with a concept known as the infinite monkey theorem. The idea is that if you had an infinite amount of monkeys pecking away at an infinite amount of typewriters, they would randomly create the collected works of Tolstoy and every other masterwork ever written (or that could be written).

The theorem, which has been around for at least a century, is troubling because it calls into question what it means to be human. If we can be inspired by something that could so easily be randomly generated, then what does it mean to be meaningful? Is meaning just an illusion we construct to make ourselves happy?

In recent years, the rise of artificial intelligence has transformed this theoretical dilemma into an intensely practical issue. In a world in which machines are taking over work long thought of as intensely human, what is the role of human labor? How do we create value that is distinct from what machines can do faster and cheaper? The answers will shape our future.

Machines Replacing Humans

The first industrial robot, called Unimate, was installed on an assembly line at General Motors in 1961. Since then, robots have become highly integrated into our economy. They do dangerous jobs, like bomb disposal, as well as more prosaic ones, like running warehouses and legal discovery.

And lest you think that automation only applies to low-skill, mechanical jobs, robots are also invading the creative realm. One book written by a machine was even accepted as a submission for the prestigious Hoshi Shinichi Literary Award in Japan. There are now robots that advise physicians, movie producers and music executives. Some even write songs.

Yet these things have been, for the most part, going on in the background. Very few of us see robots at work in factories, much less evaluating scripts, reading mammograms or doing research for lawyers. However, the introduction of ChatGPT brings the power of AI to anyone with an Internet connection.

If you haven’t tried it yourself yet, here’s an example: I asked it to explain the infinite monkey theorem to me and this is what I got:

The infinite monkey theorem states that a monkey hitting keys at random on a typewriter keyboard for an infinite amount of time will almost surely type any given text, such as the complete works of William Shakespeare. The theorem is often used as a humorous illustration of the basics of probability theory. However, the theorem is actually a misnomer, since the chances of a monkey randomly typing a meaningful sequence of characters is incredibly small, and the concept of an “infinite” amount of time is physically impossible.

And just for fun, I asked it to produce a one paragraph promotional biography of Greg Satell:

Greg Satell is a thought leader in innovation and business strategy, with over 20 years of experience in the industry. He is a popular speaker and author, known for his bestselling book “Mapping Innovation: A Playbook for Navigating a Disruptive Age.” Greg has also held executive roles at leading companies and has a track record of driving innovation and growth through effective business strategies.

Each of these responses appeared in seconds and, while they are not what I would have chosen to write myself, they are more than passable. With the technology advancing at this pace, how can we compete with the machines?

The Automation Paradox

In 1900, 30 million people in the United States were farmers, but by 1990 that number had fallen to under 3 million even as the population more than tripled. So, in a manner of speaking, 90% of American agriculture workers lost their jobs due to automation. Yet those out-of-work farmers weren’t impoverished. In fact, the 20th century was an era of unprecedented prosperity.

Consider this: Although the workforce in the US has more than doubled since 1950, labor participation rates remain close to all-time highs. Still, a recent report by the US Chamber of Commerce found that we have a massive labor shortage. In the highly-automated manufacturing sector, it estimated that even if every unemployed person with experience were employed, it would only fill half of the vacant jobs.

In fact, when you look at highly automated fields, they tend to be the ones that have major labor shortages. You see touchscreens everywhere you go, but 70% of openings in the retail sector go unfilled. Autopilot has been around for decades, but we face a massive global pilot shortage that’s getting worse every year.

Once a task becomes automated, it also becomes largely commoditized and value is then created in an area that wasn’t quite obvious when people were busy doing more basic things. Go to an Apple store and you’ll notice two things: lots of automation and a sea of employees in blue shirts there to help, troubleshoot and explain things to you. Value doesn’t disappear, it just shifts to a different place.

One striking example of this is the humble community bookstore. With the domination of Amazon, you might think that small independent bookstores would be doomed, but instead they’re thriving. While its true that they can’t match Amazon’s convenience, selection or prices, people are flocking to small local shops for other reasons, such as deep expertise in particular subject matter and the chance to meet people with similar interests.

The Irrational Mind

To understand where value is shifting now, the work of neuroscientist Antonio Damasio can shed some light. He studied patients who, despite having perfectly normal cognitive ability, had lost the ability to feel emotion. Many would assume that, without emotions to distract them, these people would be great at making perfectly rational decisions.

But they weren’t. In fact, they couldn’t make any decisions at all. They could list the factors at play and explain their significance, but they couldn’t feel one way or another about them. In effect, without emotion they couldn’t form any intention. One decision was just like any other, leading to an outcome that they cared nothing about.

The social psychologist Jonathan Haidt built on Damasio’s work to form his theory of social intuitionism. What Haidt found in his research is that we don’t make moral judgments through conscious reasoning, but rather through unconscious intuition. Essentially, we automatically feel a certain way about something and then come up with reasons that we should feel that way.

Once you realize that, it becomes clear why Apple needs so many blue shirts at its stores and why independent bookstores are thriving. An artificial intelligence can access all the information in the world, curate that information and present it to us in an understandable way, but it can’t understand why we should care about it.

In fact, humans often disguise our true intent, even to ourselves. A student might say he wants a new computer to do schoolwork, but may really want a stronger graphics engine to play video games. In much the same way, a person may want to buy a book about a certain subject, but also truly covet a community which shares the same interest.

The Library of Babel And The Intention Economy

In his story The Library of Babel, Borges describes a library which contains books with all potential word combinations in all possible languages. Such a place would encompass all possible knowledge, but would also be completely useless, because the vast majority of books would be gibberish consisting of random strings of symbols.

In essence, deriving meaning would be an exercise in curation, which machines could do if they perfectly understood our intentions. However, human motives are almost hopelessly complex. So much so, in fact, that even we ourselves often have difficulty understanding why we want one thing and not another.

There are some things that a computer will never do. Machines will never strike out at a Little League game, have their hearts broken in a summer romance or see their children born. The inability to share human experiences makes it difficult, if not impossible, for computers to relate to human emotions and infer how those feelings shape preferences in a given context.

That’s why the rise of artificial intelligence is driving a shift from cognitive to social skills. The high paying jobs today have less to do with the ability to retain facts or manipulate numbers—we now use computers for those things—than it does with humans serving other humans. That requires more deep collaboration, teamwork and emotional intelligence.

To derive meaning in an artificially intelligent world we need to look to each other and how we can better understand our intentions. The future of technology is always more human.

HALLOWEEN BONUS: Save 30% on the eBook, hardcover or softcover of Braden Kelley’s latest book Charting Change (now in its second edition) — FREE SHIPPING WORLDWIDE — using code HAL30 until midnight October 31, 2025

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

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Why Putting Employees First and Customers Second Works

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Why Putting Employees First and Customers Second Works

GUEST POST from David Burkus

What if your company announced that, moving forward, it would be place customers second on its list of priorities?

Sounds crazy. The customer is always right. Surely the customer is always first as well.

But that’s exactly what Vineet Nayar, CEO of HCL Technologies did over a decade ago. He announced that the company’s senior leaders would be placing the needs of employees first, and customers second. And the results have been spectacular.

How The Employees First Strategy Started

In 2006, Vineet Nayar, CEO of HCL, a digital engineering company based in India, boldly told his clients they were no longer the company’s top priority. Instead, the focus would be put on employees first. His belief was simple: happy employees make happy customers. Nayar labeled employees who actually interacted with customers as the “value zone,” where the real business magic happens — and any employee in the value zone received the dedicated focus of managers and support functions.

To bring this to life, he flipped the traditional management structure. He made the organizational chart look like an upside-down pyramid. Turning the hierarchy upside down required making managers accountable to front-line employees and ensuring that those in the support functions actually supported those front-line employees, instead of just insisting that they follow the hierarchy’s rigid systems.

Nayar focused his attention on two areas to ensure that the management and support functions served the front-line: reversing accountability and building transparency. Specifically, 360-degree feedback evaluations were expanded to include more front-line workers’ feedback for managers and senior level executives (that’s the accountability), and crucially those evaluations were made public so everyone who contributed to the survey could see the results (there’s your transparency). In addition, when problems occurred for front-line workers, they could create and own support tickets that their managers would have to address (usually, it’s the other way around in top to bottom organizations).

It’s important to note that HCL Technologies wasn’t a little start up in a garage or even a 50-person company. This was done at a 55,000 person, multinational organization. And, spoiler alert, it’s now grown to over 200,000 employees. Pulling off this flip was no small feat, but the results speak for themselves. Employee satisfaction soared, customer service improved, and revenues nearly tripled. By 2009, HCL was named India’s best employer.

Contrast this story with an example of what can go wrong when employee experience is overlooked. In 2001, Robert Nardelli was the newly minted CEO of Home Depot. Expectations were high given his track record at his old job at General Electric, where he had led several successful manufacturing operations.

At Home Depot, Nardelli noticed the stores were staffed with knowledgeable, full-time employees, and in his opinion, a bit too many. What do new leaders, wrongfully, do when they want to make waves and save money?

Yep, he downsized to optimize costs.

He decided to hire more part-timers, many of whom had less expertise in home improvement. The results were not what he expected. Customers quickly noticed the absence of their favorite employees and the decline in service quality. It turned out that managing a service organization like Home Depot was very different from managing a manufacturing operation.

This story underscores a critical point: leading a service organization requires a different approach — one that prioritizes employee engagement and expertise.

“Employees first, customers second” is still about serving the customer, but it’s about serving the customer through the employees whose job it is to serve the customer. Weird how that works, isn’t it? Understand that helping your employees helps your customers. These two parties are intrinsically tied together.

Research On Employees First

Nayar’s success story isn’t an isolated incidence of dumb luck. There’s research behind this. Researchers at Harvard University found a link between employee satisfaction and profitability. They took aim at a long-standing assumption in the business world that market share is the primary driver of profitability. If a company can increase market share, the thinking went, it will increase sales while taking advantage of economies of scale to lower costs and thus increase profits.

However, when they examined a variety of companies and the existing research, they found that market share is one factor in profitability. But that another factor better explains the most profitable companies: customer loyalty.

Based on their research, they estimated that a mere 5 percent increase in customer loyalty can yield a 25 to 85 percent increase in profitability.

Here’s how it works in practice: Profits are driven by customer loyalty. Customer loyalty is driven by employee satisfaction. And employee satisfaction is driven by putting employees first. They called this The Service-Profit Chain and managers who understand this can create a thriving cycle where employee and customer satisfaction drive each other, ultimately leading to greater business success.

In simple terms, if your business provides a service that your employees have front-line participation in, they are in essence an embodiment of the company, not you or the CEO. The entire brand, the experience, the service rests on those front-line employees. If they aren’t taken care of — if they aren’t satisfied — the customer tends to notice.

How Employees First Creates Customer Loyalty

Employee loyalty is a deep indicator of future performance for service organizations. It’s worth noting that there is a subtle difference between employee satisfaction and employee loyalty. Satisfaction derives from how happy employees are in their role. Loyalty comes from having a real stake in the success of the business. Without loyalty, employees leave for better opportunities, then high turnover rates drive up recruitment and training costs, disrupt productivity, and can negatively impact customer experiences. When employees stay longer, companies save on hiring costs, maintain productivity gains, and create a more positive environment for customers.

Simply put, loyal employees lead to loyal customers.

Great service leaders recognize that improving employee retention involves providing opportunities for growth and advancement. This approach keeps talented employees closer to the customer for longer periods, which directly impacts customer satisfaction and loyalty.

Take Whole Foods Market, for example. They have crafted their entire system — from their rigorous selection process to compensation methods — to encourage front-line employees to stay and thrive. Teams at Whole Foods are responsible for setting key metrics, making decisions on how to meet these targets, and even choosing what food items to buy locally. They’re rewarded with bonuses based on team performance, which often includes finding creative ways to boost sales to balance out labor costs. After three years on the job, employees receive stock options, which further incentivizes them to stay.

Additionally, Whole Foods allows employees to vote every three years on various aspects of the benefits package, from community service pay to health insurance provisions. All these factors contribute to Whole Foods’ remarkably low turnover rate of less than 10 percent for full-time employees after the probationary period — far below the industry average.

The results speak for themselves: Whole Foods is regularly rated as one of the best places to work, known for excellent customer service, and boasts some of the highest profits per square foot in the grocery retail industry.

This success is a testament to the power of employee loyalty in driving exceptional service. Great service leadership isn’t just about managing day-to-day operations — it’s about creating an environment where employees feel valued, empowered, and committed. By focusing on employee loyalty, service leaders can build stronger customer relationships and achieve sustainable success.

Employees First For All Leaders

You may not have the power in your organization to completely flip the hierarchy. But there’s still an important lesson for leaders at all levels: Flip the accountability. This can look like bringing in more feedback from front-line employees or just seeing the structure of your team differently. You work for your team. Don’t squeeze your team; foster them to do well.

In addition, give your employees real stakes and invest in them. Prioritize training and growth opportunities for your employees so they know you’re committed to not just their output, but their career. Parties, gift certificates, awards, summer Fridays, bonuses — all of these are great. Do those things. But those are more employee appreciation, not real development. Development looks like sending your rising stars to conferences, workshops, night school even, if you have the budget. Things you think will help them grow as employees, spark innovation, and create future leaders.

Conclusion

If I could put a message on a billboard in front of every Fortune 500 company, it would be this:

People don’t work for you.

Smart leaders know that employees work with them, and ultimately, leaders work for their people. Embracing the “employees first, customers second” philosophy means prioritizing the well-being and growth of employees, enabling them to deliver outstanding service. Happy, engaged employees create satisfied customers. When leaders invest in their teams’ success and happiness, they cultivate a culture where customers feel valued, leading to long-term loyalty and a thriving business.

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Image credit: David Burkus

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Bridging the Gap Between Strategic Ambition and Innovation Delivery

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Why Long-Range Planning and Product Development Rarely Align — And What Companies Can Do About It

Bridging the Gap Between Strategic Ambition and Innovation Delivery

GUEST POST from Noel Sobelman

Across industries, executive teams craft long-range plans (LRPs) with confident projections for revenue growth, market expansion, and innovation impact. But when it comes time to deliver, product development pipelines often tell a different story. This misalignment, between the top-down assumptions embedded in strategic plans and the bottom-up reality of new product development (NPD), is one of the most persistent and under-addressed risks in corporate planning.

The consequences are serious: growth targets are missed, credibility erodes, and shareholder confidence wanes. And yet, many organizations continue to treat this disconnect as inevitable, rather than solvable.

The Illusion of Alignment

On paper, LRPs typically assign a portion of future revenue to innovation — new products, new markets, new business models. This makes sense. In competitive, fast-moving sectors, sustaining growth depends on a constant stream of successful launches.

But few companies take the next step: validating whether their actual innovation pipeline supports those ambitions. The top-down LRP rarely connects meaningfully with the bottom-up details of project timelines, product margins, development risks, or resource constraints.

Leadership may assume, for instance, that new product contributions will ramp up in years three through five of the plan. Yet the NPD pipeline might only be populated with early-phase projects, with no clear line of sight to commercialization in that time frame. Or worse, it might be filled with low upside sustaining efforts that do little to drive long-term growth.

This isn’t just a data problem — it’s an accountability problem.

A Blind Spot in Strategic Execution

Unlike sales or operations, which are frequently forced to reconcile their contributions to the LRP through tangible metrics and quarterly reviews, product development is often allowed to operate in a parallel universe. Project business cases get approved on a rolling basis, disconnected from aggregate targets. Teams work diligently, but no one steps back to ask: Do the numbers add up?

In many organizations, this analysis is simply never done. When questioned about how the pipeline contributes to the LRP, the answers range from vague optimism (“We’ll figure it out”) to manual workarounds (“We added 5% to last year’s numbers to cover new product upside”).

Such informal planning approaches might have been acceptable in a slower, less competitive world. But in today’s environment, where innovation cycles are compressed, capital is scrutinized, and every function is expected to deliver ROI, they fall short.

Interestingly, other parts of the business, particularly operations, already have a model for how to approach this. Manufacturing teams routinely perform network strategy exercises to determine whether they have the physical capacity to meet future demand. They map projected sales to factory utilization, labor capacity, CapEx, and throughput. If there’s a gap, they create an actionable plan.

Yet in most organizations, this rigor stops at the walls of the plant. There is no equivalent exercise on the R&D side to ask: Do we have the innovation pipeline, product plans, and resources required to meet our revenue commitments? Working with our clients, we’ve seen how powerful it is when this same network strategy logic is applied to product development. The exercise shifts the conversation from hope to confidence, from general intent to measurable plans.

The Case for a Unified Growth Strategy

The path forward requires a more integrated, data-driven approach, a growth strategy that spans both the strategic and executional layers of the business.

At the core is a disciplined feedback loop: reconciling the LRP’s innovation-driven revenue expectations with the actual new product roadmap, resource plan, and market assumptions. This means:

  • Bottom-up modeling of product-level forecasts (volumes, ASPs, margins, launch dates) that aggregate to a portfolio view of expected revenue. Our benchmarks show that without this discipline, overstatements of new product contributions can widen to 20–40% or more in the outer years of the LRP. Modeling helps identify these gaps early, enabling timely course corrections.
  • Scenario analysis that tests different mixes of existing and in-development products to identify gaps and prioritize high-leverage opportunities.
  • Risk adjustment grounded in performance benchmarks and realistic probabilities of technical and commercial success, not wishful thinking. Companies that formalize these assumptions often uncover significant overstatements in expected revenue from early-stage projects.
  • Cross-functional transparency between R&D, finance, operations, and commercial teams to ensure the entire organization is planning from a shared reality.

Working with our clients, we’ve helped build models that mirror this approach, combining innovation pipeline data, financial assumptions, and market insights into a unified view of expected contribution to growth. The result? Greater visibility into how future revenue will be earned and higher confidence in investment decisions. For some organizations, this alignment has helped redirect 10–15% of R&D spend toward higher-value opportunities without increasing total investment.

In nearly every case, the analysis reveals significant gaps between what leadership believes the innovation engine will deliver and what’s realistically in flight. But once exposed, those gaps become manageable. They become actionable.

This isn’t about punishing innovation teams for uncertainty. It’s about giving them, and the organization, an honest view of what’s likely to be delivered and where targeted adjustments are needed.

Building the Capability (Not Just the Model)

Organizations that do this well don’t just build a single model — they build the capability. They embed portfolio management processes that continually evaluate whether innovation plans are aligned with strategic goals. They invest in tools and talent that can translate project business cases into forward-looking financial impact. And critically, they elevate the conversation from “project selection” to “portfolio impact.”

This approach can also shift the internal conversation away from politics and gut feel, and toward clarity and confidence. CFOs, for example, are increasingly demanding to know what they’re getting for the annual increases in R&D spend. A connected, data-rich view of how new product drives future cash flows goes a long way in strengthening that case. We’ve seen how quickly these conversations mature when companies adopt a planning discipline that brings product development onto the same strategic playing field as operations and sales.

The Strategic Imperative

Ultimately, reconciling innovation with the LRP isn’t a nice-to-have. It’s a fiduciary responsibility. Companies make commitments to their boards and investors based on the assumption that R&D investment will deliver a meaningful share of future growth. When that assumption is built on loosely connected plans and unvalidated forecasts, the entire strategy is at risk.

Bridging that gap can unlock substantial value. In our experience, we see organizations with tightly aligned portfolio and strategy processes outperform their peers by as much as 40% in terms of new product ROI and time-to-market.

The good news? The gap is measurable. The tools, models, and methods to close it exist. What’s often missing is the mandate.

Organizations that seize this opportunity will be better equipped to make confident trade-offs, accelerate high-potential initiatives, and pivot early when plans drift off course. They’ll be able to tell a coherent story, not just about where they want to go, but how they plan to get there.

And that story, told with numbers and backed by action, is what distinguishes companies that plan for growth from those that actually deliver it.

If you’re interested in exploring how to better align your product development plans with long-range strategic goals or want to assess the credibility of your innovation pipeline, we’d be happy to share what we’ve learned from working with companies in similar situations.

HALLOWEEN BONUS: Save 30% on the eBook, hardcover or softcover of Braden Kelley’s latest book Charting Change (now in its second edition) — FREE SHIPPING WORLDWIDE — using code HAL30 until midnight October 31, 2025

Image credits: Pexels

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