Category Archives: Leadership

Self-Acceptance Will Supercharge Your Life

GUEST POST from Tullio Siragusa

For a long time, society has demanded that we show up as good people. Do the right things and practice Godliness. The facts are that this has turned out to be an impossible expectation to fulfill. Not because we can’t be good people, and do the right things, it’s because the edict doesn’t give license to vulnerably reveal the darkness in the way of achieving the goal of being a good person.

“When we accept ourselves as a gift in the world, we begin to recognize the same in others. Whatever is external of ourselves becomes a mirror of who we are within.”

That means that if you don’t like what is external of you, simply shift what is within you.

“ALL PROBLEMS SOLVED”. Simple right? Not exactly.

There is a step that most people avoid, and that is to reveal the darkness within first. At the heart of becoming the best version of ourselves, is acceptance. While historically we’ve had the pressure to always show up as if we have it all together, for fear of retribution of judgement from others, we can’ keep avoiding or masking our darkness.

It’s important to go deeper in the darkness we are in as individuals to discover the source of it, but we have to stop judging and shaming each other for being human. We are imperfect. We discover ourselves through failures., just as science discovers things through failure.

“Failure is built into the success formula of scientific discovery, it’s no different in how we discover ourselves as human beings.”

If you have darkness within you, instead of feeling shame, or guild you could shift your context and realize that our collective consciousness has chosen you to play out the darkness so you could overcome it and create the frequency for others to do the same. This is because you are the best person among all of us, to overcome it and become a beacon of Light for the rest of us.

Let me repeat that in case it hasn’t sunken in yet. YOU HAVE BEEN CHOSEN TO OVERCOME THE DARKNESS YOU ARE IN.

“The way out of hell in life… is on the other side of it. The door is just past the point of no return… only those trusting that the door is within reach, can walk through fire and gain control over everything.”

There are two ways to overcome challenges in life.

1) You work really hard to transform yourself, and to overcome the “not so good” traits; most of us end up simply suppressing who we are, but few do actually transform “some” aspects of themselves.

2) You accept yourself as you are, and you focus on becoming a being who bestows goodness in the world. When you are feeling bad about yourself, you are not good to you or anyone.

The first route will have you chasing your tail for years, and when you do fall (which happens in this imperfect reality) you’ll feel so bad, that you can’t focus on anything else. This has been the cause of depression, anger, resentment and all the chaos in the world for thousands of years. It all stems from lack of self-respect, self-love, self-dignity, self-honor, and lack of self-acceptance.

It’s impossible to accept others as they are when we still have traits, we don’t accept about ourselves. How can you accept other people’s traits, if you don’t accept yourself completely?

The second route shifts you into a parallel universe instantly, where you begin to accept others by allowing them to not be perfect, just like you.

“When you accept yourself for all of who you are, you can do the same for others, and you begin to experience life’s beauty and perfection in the imperfections.”

Acceptance shifts you into a parallel Universe where bliss is the normal mode of existence… Acceptance is being present without judgment. Having trouble with self-acceptance?

Try this simple exercise and mantra. Give yourself a hung and say:

“I am great just as I am, and I love me just as I am; I extend the same to everyone around me, and allow them to accept me as I am. I can now focus my energy on emanating the love I have for myself to the entire world and allow the world to do the same in return”.

For millenniums we’ve been going in circles feeling bad about our “character flaws”, which in some ways has kept us from achieving our greatest potential as humanity.

It’s important to get in touch with our own inner ugliness, yes… this is very important, but for no other reason than to recognize it, accept it, and find love for ourselves anyway.

“How we choose to perceive ourselves, is how we experience the entire Universe.”

Our thoughts and actions generate energy; this energy multiplies and creates a frequency for others. The more we generate the energy of compassion, love, and we shed a tear for those who suffer, the more a sense of urgency will take place worldwide to do the same.

Self-acceptance isn’t just the first step to practicing emotional intelligence, it is the way to living free of shame, and free to be our imperfect selves. My recent Rant & Grow guest, Rocky Rosen is the world’s #1 smoking cessation coach (aka the cigarette whisperer) as he turns 67 he is finally embracing self-acceptance.

Check out the coaching session with Rocky and see what commitments he makes to practice self-acceptance and supercharge his life. Maybe you’ll discover some wisdom for your own life. You can listen to the podcast right here.

Originally published at tulliosiragusa.com on September 9, 2019.

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Managing Across Cultures

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Managing Across Cultures

GUEST POST from David Burkus

If you’re leading multicultural teams, you already know that the hard part isn’t managing projects—it’s managing people. People who see work, time, leadership, and even your well-intentioned Zoom calls very differently. when your team isn’t just spread across departments or cities, but countries and cultures, those small issues can quickly snowball into trust breakdowns, missed deadlines, and a whole lot of stress.

The good news? That’s exactly where cultural intelligence comes in.

Why Most Leadership Advice Doesn’t Cut It Globally

Most leadership best practices are built on Western ideals: autonomy, authenticity, egalitarianism. And for many teams in the U.S., Canada, or Northern Europe, those principles work fine. But here’s the disconnect: over 70% of the global workforce doesn’t come from those cultures. Instead, they come from collectivist, hierarchical contexts where values like harmony, deference, and indirect communication are more important than speaking up or standing out. So, when leaders apply those Western norms across a multicultural team, problems arise. Trust breaks down. Communication stalls. Performance lags. And it’s not because the team isn’t capable—it’s because the leadership approach isn’t compatible.

That’s why cultural intelligence (CQ) is essential. According to social scientist David Livermore, cultural intelligence is a leader’s ability to recognize different cultural norms, expand their own understanding, and adapt their behavior to work effectively across those differences.

In other words, it’s not about memorizing facts like what holidays people celebrate or who bows versus shakes hands. It’s about learning to lead with flexibility, humility, and a willingness to adjust.

The Common Pitfalls of Leading Multicultural Teams

When leaders first encounter cultural differences, they often default to one of two flawed approaches: overcorrecting or oversimplifying.

Some leaders think, “Let’s celebrate every culture! Let’s learn fun facts! Let’s avoid conflict and just let people be people.” While well-intentioned, this can lead to a surface-level focus that ignores deeper dynamics.

Others take a hands-off approach: “We hired great people. Let’s let them figure it out.” But that abdication often results in misunderstandings festering until they explode—or worse, quietly eroding trust.

Then there’s the psychological safety trap. In Western teams, psychological safety often looks like open debate and direct feedback. But in many cultures, especially those where saving face is critical, this approach can feel aggressive or disrespectful.

Take Google, for example. They were early champions of psychological safety, encouraging teams to challenge ideas openly. But when they rolled out that concept globally, it backfired. Some teams became overly cautious, avoiding honesty to protect harmony. Others interpreted directness as disrespect.

The lesson? Psychological safety isn’t a universal behavior. It’s a universal need expressed in culturally different behaviors.

What Really Gets in the Way: The Hidden Barriers

To lead multicultural teams effectively, you need to recognize the specific barriers that can derail collaboration:

Direct vs. Indirect Communication: In some cultures, clarity means saying exactly what you mean. In others, it means saying just enough for someone to infer your meaning. That “yes” from a team member might just mean “I hear you,” not “I agree.”
Language and Fluency Gaps: When some team members aren’t fluent in the working language, it creates power imbalances. They might hold back, not because they lack ideas, but because they’re unsure how to express them. Others may interpret that silence as disengagement.
Different Views of Hierarchy: In flat organizations, people are expected to challenge ideas regardless of seniority. But for team members from hierarchical cultures, speaking up—especially in front of a boss—can feel deeply uncomfortable.
Conflicting Norms Around Decision-Making: Some cultures value fast, intuitive decisions. Others prefer slow, consensus-driven processes. Without clarity, this mismatch breeds frustration.

Build Cultural Intelligence with the SPLIT Framework

One of the most practical tools for building cultural intelligence comes from Harvard professor Tsedal Neeley: the SPLIT framework. It’s designed to address the core challenges of global teams—Structure, Process, Language, Identity, and Technology—and it’s especially helpful for leaders looking to lead with intention.

Structure

Structure isn’t just about org charts. It’s about perceived power. If your headquarters is in New York but your designers are in São Paulo and your engineers in Bangalore, there’s already an unspoken hierarchy. Leaders need to be intentional about flattening that perception. Reinforce that everyone’s on the same mission—different roles, same goals.

Process

Process is how you create empathy. Build in small, deliberate moments for connection. Five minutes of personal talk at the start of a Zoom call. Spontaneous Slack check-ins. And in meetings, draw out quieter voices first. Start with junior team members or those from deferential cultures. When they speak up early, it sets the tone for inclusion.

Language

Language isn’t just about translation—it’s about clarity. If some team members struggle with fluency, that’s a structural disadvantage. Set ground rules. Encourage fluent speakers to slow down and drop the idioms. Encourage non-native speakers to ask for clarification without fear. Normalize that everyone is responsible for making the conversation work.

Identity

Identity is where curiosity matters most. Don’t assume you understand what a behavior means. Ask. Learn. Invite your team to teach you about their norms—and be open about your own. The moment you switch from “leader as expert” to “leader as learner,” you earn credibility and foster mutual respect.

Technology

Technology is your connection toolkit. Use it intentionally. For trust-building, choose live video. For info-sharing, stick to well-crafted emails. And model the behavior you expect. If you ask for cameras on, turn yours on first. If you want prompt responses, respond promptly.

Cultural Intelligence Is a Leadership Discipline

Let’s be clear: cultural intelligence isn’t a checklist. You don’t become “certified” after watching one video or reading one book. It’s a leadership discipline. It’s about staying curious, adjusting your approach, and building connection—even across borders and bandwidth issues.

You’ll make mistakes. That’s inevitable. But the goal isn’t perfection—it’s progress. It’s about learning what candor means in one culture and how respect is shown in another. It’s about tweaking your leadership style not to appease, but to align.

And the result? Multicultural teams that don’t just function—but flourish. Teams where diversity isn’t a liability but a strategic advantage. Teams where trust isn’t accidental—it’s intentional.

So, if you’re leading multicultural teams and feeling a little overwhelmed, take a breath. Start small. Ask better questions. Listen a little longer. And lead a little differently.

Because cultural intelligence isn’t just the key to global collaboration. It’s the new core competency for leadership.

Image credit: Pexels

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The Anatomy of Agentic Trust

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A Mechanistic Interpretability Framework for Change Leaders

LAST UPDATED: June 5, 2026 at 3:13 PM

The Anatomy of Agentic Trust - A Mechanistic Interpretability Framework for Change Leaders

GUEST POST from Art Inteligencia

The Impasse of the Black Box: Why Agentic AI Demands a New Trust Paradigm

Digital transformation has reached an inflection point. Organizations are moving away from traditional, deterministic software and basic copilots toward Agentic AI—autonomous systems capable of executing complex, multi-step operational workflows with minimal human oversight. While this shift promises unprecedented efficiency, it introduces a severe psychological and operational barrier: The Wall of Trust.

The Shift to Autonomy

Unlike previous iterations of artificial intelligence that relied on simple pattern-matching or isolated text generation, agentic systems possess agency. They can formulate plans, interact with external software ecosystems, and make consequential business decisions independently. However, because these systems are built on top of massive deep learning architectures, their reasoning remains entirely opaque.

The Psychological Friction of Current AI Explanations

Traditional approaches to Explainable AI (XAI)—such as post-hoc approximations, saliency maps, or text-based self-justifications—are no longer sufficient for enterprise governance. These methods merely show what data correlated with an output; they do not reveal the actual underlying computational logic. When an autonomous agent makes a flawed decision, a post-hoc explanation acts as a guess rather than an audit trail. For a workforce tasked with collaborating alongside these machines, this lack of transparency breeds deep-seated skepticism.

The Change Management Mandate

Successful innovation and experience design depend entirely on psychological safety. Change leaders cannot integrate autonomous agents into hybrid human-machine teams if the machine’s logic remains inscrutable. To transition employees from defensive resistance to confident collaboration, organizations must establish absolute legibility. Mechanistic interpretability provides the exact verifiable transparency required to align AI agents with human ethics, compliance mandates, and organizational values.

Demystifying Mechanistic Interpretability: From “Black Box” to Open Circuit

To dismantle the black box, innovation and change leaders must embrace a paradigm shift in how we audit artificial intelligence. Mechanistic Interpretability (MI) moves away from treating neural networks as abstract, unknowable minds. Instead, it approaches them like complex, physical objects—akin to an intricate mechanical watch or an integrated circuit board—that can be systematically disassembled and reverse-engineered.

The “Neuro-Industrial” Approach

Rather than merely observing what goes into a model and what comes out, MI focuses on internal computational mechanics. By treating deep learning structures as physical systems waiting to be mapped, researchers and engineers can trace the exact pathway information takes as it moves through the network. This shifts the conversation from passive observation to rigorous, empirical auditing.

Deconstructing the Neural Architecture

Understanding this open-circuit paradigm requires looking at three core components of modern model architecture:

The Communication Channel (The Residual Stream): Think of the residual stream as the primary information highway of a Large Language Model. As data passes from layer to layer, each computational mechanism reads from and writes to this central highway, iteratively refining the concepts the model is processing.
The Challenge of Superposition: Deep learning models are incredibly efficient compactors. Through a phenomenon known as superposition, a network can compress thousands of overlapping concepts into a relatively small number of neurons. This results in “polysemanticity”—where a single neuron might fire for a medical diagnosis, an ancient historical event, and a specific lines of code, making raw network readouts look like total gibberish to humans.
The Solution (Sparse Autoencoders): To untangle this mess, researchers use an auxiliary tool called a Sparse Autoencoder (SAE). The SAE acts as an analytical lens, expanding the compressed neural activity back out into an uncompressed, highly specific map of distinct business concepts and features. Polysemantic neurons are separated into clean, human-readable concepts.

Mapping the Circuits

Once the concepts are isolated by Sparse Autoencoders, change and safety leaders can trace how individual components connect to form causal, end-to-end pathways—or circuits. These circuits execute specific pieces of logic, such as a circuit that detects tax compliance rules or a circuit that handles data privacy boundaries. Mapping these circuits turns an opaque mathematical matrix into a transparent, visual map of organizational logic.

The Commercial Frontier: Leading Organizations and Startups Shifting MI from Theory to Tooling

What began as an academic and safety-centric pursuit has quickly evolved into a critical layer of the enterprise AI value chain. As organizations demand verifiable trust before deploying agentic workflows, a robust commercial ecosystem has emerged. Today, the development of Mechanistic Interpretability tools is divided among frontier research labs, open-source consortia, and specialized AI safety startups.

Frontier Research Labs: Setting the Scale

The foundational model developers themselves are treating internal architectural translucency as both a primary safety barrier and a competitive advantage.

Anthropic: Widely recognized as a pioneer in dictionary learning, Anthropic demonstrated commercial-scale concept mapping by isolating millions of abstract, safety-critical, and real-world features inside its Claude models. Their pioneering work in circuit tracing maps not just which features are active, but how they causally influence each other in sequential processing chains.
OpenAI: Operating at massive computational scale, OpenAI has focused on automating the interpretability pipeline itself. By utilizing advanced Large Language Models as automated “feature explainers,” they systematically analyze, score, and catalog millions of dense neuron activations simultaneously across models like GPT-4, laying the groundwork for algorithmic “lie detectors” built directly into model internals.
Google DeepMind: DeepMind significantly accelerated industry-wide adoption with the release of Gemma Scope, a massive, comprehensive open-source interpretability toolkit mapping across the entirety of its Gemma model families. This initiative effectively democratizes MI, giving enterprise change and innovation leaders the open tools needed to audit fine-tuned models independently.

Open-Source Consortia

Bridging the gap between frontier research and accessible development is EleutherAI. Through specialized open-source libraries like sparsify, EleutherAI provides researchers and enterprise engineers with the standard blueprints required to train Sparse Autoencoders (SAEs) and transcoders directly on HuggingFace transformers, allowing organizations to extract custom, localized operational feature dictionaries without relying on proprietary third-party APIs.

The Emerging AI Governance & Steering Startup Ecosystem

As the market shifts from post-hoc model analysis to real-time behavioral intervention, a specialized group of AI safety, security, and compliance startups has emerged. These early-stage innovators are building platforms that operationalize MI principles for the enterprise:

Algorithmic Auditing & Protection Platforms: Emerging vendors—including teams like Protect AI, Turing, Holistic AI, and Enkrypt AI—are actively developing continuous monitoring guardrails, neural audit logs, and PII containment shields.
From Observation to Intervention: Rather than just notifying a business that an autonomous agent has hallucinated, the vanguard of this ecosystem is building enterprise toolsets focused on feature steering. By giving compliance officers and change managers the ability to programmatically clamp down or amplify specific feature vectors, these platforms provide an exact knob to safely steer agent behavior in production environments without requiring costly model retraining cycles.

The Collaborative Interface: Designing the Human-Machine Audit Trail

For change and innovation leaders, a technical map of a neural network is only useful if it can be translated into operational reality. To turn Mechanistic Interpretability from an engineering luxury into a practical governance mechanism, organizations must implement a standard action loop. This practical paradigm is defined by three continuous operational steps: Locate, Steer, and Improve.

1. Locate (The Diagnostic Phase)

When an autonomous AI agent produces an unexpected anomaly, drifts from compliance, or triggers a customer experience failure, traditional troubleshooting is useless. Under the MI framework, operations teams initiate the Locate phase. By utilizing Sparse Autoencoders, corporate compliance teams can systematically look under the hood to isolate the exact subgraphs and internal feature nodes that dictated the agent’s flawed decision path. Instead of guessing why an error occurred, leaders can pinpoint the specific computational circuit responsible for the behavior.

2. Steer (The Real-Time Intervention Phase)

Once a problematic circuit or feature node is located, the organization does not need to undergo a weeks-long, financially draining model-retraining process. Instead, leaders use feature steering to intervene directly. By programmatically adjusting, clamping, or dampening specific feature activations within the live system, operations teams can instantly align the agent’s behavior. For example, if an insurance agent begins using unapproved geographic criteria to assess risk, a compliance manager can safely dial down that specific feature vector without degrading the agent’s overall processing capabilities.

3. Improve (The Continuous Alignment Phase)

The final phase transitions the organization from reactive intervention to proactive refinement. Over time, data engineers, risk managers, and business unit leaders iteratively review the agent’s global modular vocabulary. By continuously updating and refining these feature dictionaries, the enterprise can permanently align autonomous workflows with changing regulatory landscapes, ethical guidelines, and internal corporate values. This creates a living, transparent human-machine audit trail that ensures autonomous systems remain accountable to human intent.

The Human-Centered Angle: Using Circuit Translucency to Drive Adoption

The ultimate success of any digital transformation initiative hinges on the psychology of the people expected to drive it. Technology alone does not yield ROI; adoption does. By turning the “black box” into a translucent, auditable map of circuits, Mechanistic Interpretability addresses the deepest root cause of workforce resistance: the fear of the invisible, unaccountable driver.

Abolishing the “Us vs. Them” Dynamic

When autonomous agents are introduced as inscrutable forces that magically output decisions, an adversarial dynamic inevitably forms between employees and technology. Teams view the AI as an opaque competitor designed to replace or undermine their judgment. Providing an interactive, auditable look “under the hood” radically reframes this relationship. When employees can visually trace the model’s logic pathways, the AI shifts from a mysterious threat to a legible, controllable tool. Demystification actively dissolves defensive skepticism and replaces it with shared ownership.

Designing the Experience of AI Auditing

Innovation and experience design leaders must proactively design the workflows that connect humans to these neural circuits. This requires upskilling traditional Subject Matter Experts (SMEs)—such as underwriters, clinicians, or compliance officers—from passive users into active “circuit overseers.” Instead of forcing SMEs to learn complex linear algebra, organizations must build intuitive, human-centered dashboard experiences. These interfaces translate complex Sparse Autoencoder feature dictionaries into plain language, empowering business leaders to confidently monitor, validate, and sign off on automated reasoning.

The Safety-Trust Horizon

Psychological safety cannot coexist with unpredictability. True confidence is built on empirical predictability—knowing exactly where the guardrails are and how to enforce them. By establishing a verifiable baseline for risk mitigation, circuit translucency gives operations teams the concrete evidence they need to trust autonomous systems. When a team knows they can structurally audit a workflow, catch compliance drift before it impacts a customer, and pinpoint exactly why an anomaly occurred, they can deploy agentic workforces at scale with absolute confidence.

Operationalizing the Framework: A Roadmap for Innovation Leaders

Transitioning an organization from opaque, unverified AI deployments to a translucent, mechanistically interpretable architecture requires an intentional, staged approach. Innovation and change leaders cannot implement this infrastructure overnight. Instead, they must systematically align technical capabilities with human experience design. This roadmap provides a practical three-phase deployment strategy to operationalize agentic trust across the enterprise.

Phase 1: Diagnostic Readiness and Risk Mapping

The first step is identifying high-stakes operational workflows where opaque agent logic presents an unacceptable risk to compliance, organizational stability, or brand trust. Leaders must audit their current AI roadmap and pinpoint “red zone” processes—such as autonomous financial underwriting, automated contract enforcement, or clinical triage routing. By scoring these workflows based on regulatory exposure and the psychological impact on the employees overseeing them, organizations can prioritize exactly where mechanistic transparency is required to maintain operational stability.

Phase 2: Architectural Translucency and Feature Extraction

Once high-risk workflows are mapped, innovation leaders must partner directly with AI engineering and data science teams to build out the technological transparency layer. This phase involves integrating open-source frameworks or commercial governance platforms directly into fine-tuned enterprise models. Engineers deploy Sparse Autoencoders (SAEs) and transcoders across the model’s layers to untangle polysemantic neurons, systematically extracting a structured, human-readable dictionary of the specific business concepts, compliance rules, and operational parameters the agent uses during execution.

Phase 3: Cultural Integration and Co-Creation Loops

The final phase embeds this structural transparency directly into the company’s operating model and culture. Change leaders must design and establish cross-functional governance loops where compliance officers, risk managers, change management practitioners, and front-line business leaders systematically review and steer agent behavior. By designing intuitive dashboards that translate extracted features into plain language, organizations empower non-technical personnel to participate in feature-steering exercises, transforming AI alignment from a back-office engineering chore into a collaborative corporate discipline.

Conclusion: The Future of Co-Elevation

As organizations stand on the precipice of widespread Agentic AI deployment, a critical truth becomes apparent: the ultimate bottleneck to scaling artificial intelligence is not computational power, data density, or algorithmic sophistication—it is human trust. Businesses cannot capture the exponential ROI of autonomous workflows if their own teams pull back in skepticism, or if compliance frameworks reject the inscrutable nature of the systems driving them.

The Core Philosophy

Mechanistic Interpretability represents far more than a technical patch for AI safety. It is a fundamental philosophical shift that treats neural networks with the same empirical rigor we apply to physical engineering. By transforming the “black box” into a legible blueprint of interconnected circuits, we strip away the unhelpful mystique surrounding deep learning. This structured transparency provides the absolute bedrock for psychological safety, transforming autonomous agents from opaque wildcards into predictable, reliable partners.

The Innovation Call to Action

Forward-thinking innovation and change leaders must stop viewing AI safety and interpretability as a narrow, back-office technical function left solely to data scientists. True, sustainable digital transformation requires a holistic approach. It is the responsibility of culture builders, experience designers, and corporate strategists to champion architectural translucency. By operationalizing Mechanistic Interpretability, enterprises can successfully bridge the cognitive divide, mitigate systemic operational risk, and unlock the true potential of a highly confident, collaborative, and co-elevated human-machine workforce.

Frequently Asked Questions

To help both your human teams and automated search crawlers understand the intersection of AI safety and organizational change, this section includes a standard human-readable FAQ alongside a structured JSON-LD Schema block optimized for modern answer engines.

1. How does Mechanistic Interpretability differ from standard Explainable AI (XAI)?

Traditional Explainable AI (XAI) usually generates post-hoc guesses or approximations—like text descriptions or heat maps—of why a model arrived at an output. It tells you what inputs correlated with the result, but not the actual path taken. Mechanistic Interpretability (MI) reverse-engineers the network itself, unpacking compressed neural activity to reveal the literal computational “circuits” and logical workflows inside the model. It moves from correlation to true mechanical causation.

2. Why is structural transparency critical for human-centered change management?

Successful digital transformation requires psychological safety. When organizations deploy fully autonomous “Agentic AI” workflows without visibility, employees experience defensive skepticism because they cannot audit, predict, or trust the system’s logic. By making the model’s internal reasoning translucent, change leaders can transition human teams from resistant onlookers to confident collaborators who can proactively steer and manage their AI partners.

3. What is “feature steering” and how does it protect an organization?

Feature steering is the ability to programmatically amplify, clamp, or dampen specific concept vectors isolated inside a model using Sparse Autoencoders (SAEs). Instead of undergoing a long, expensive retraining or fine-tuning process when an AI agent drifts out of compliance or experiences a workflow anomaly, compliance and innovation managers can adjust the model’s specific internal logic dials in real time to ensure safe, ethical execution.

Disclaimer: This article speculates on the potential future applications of cutting-edge scientific research. While based on current scientific understanding, the practical realization of these concepts may vary in timeline and feasibility and are subject to ongoing research and development.

Image credits: Gemini

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Markets Don’t Build Themselves, You Must Engineer Them

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Exclusive Interview with Bruce Cleveland

In a business landscape increasingly cluttered by “feature wars” and fleeting viral trends, true market leadership isn’t just about who builds the best product — it’s about who defines the problem. In his groundbreaking work, Market Engineering, Bruce Cleveland argues that successful companies don’t just enter markets; they architect them. By blending rigorous systems thinking with the art of category design, Cleveland provides a blueprint for moving beyond commodity status to become a dominant force that sets the rules of the game.

In this insightful Q&A, Cleveland breaks down why “Market Engineering” must be foundational from day one rather than a secondary thought for the marketing department. From the evolution of Chief Storytellers to the strategic distinction between a market and a category, he explores how leaders can steer through the noise — especially in the age of AI — to create a resonant narrative that sticks.

Today we dive deep into the characteristics and necessities of market engineering with our special guest.

Markets Don’t Build Themselves

Bruce Cleveland is a former venture capitalist and engineering and product executive at Apple, C3 AI, Oracle, and Siebel Systems. As founder of Traction Gap Partners, he has helped hundreds of startups, scale-ups, and enterprises to transform innovation into impact. His previous book, Traversing the Traction Gap, is taught in universities and used by investors and founders worldwide. Cleveland’s frameworks blend analytical discipline with creative storytelling — empowering leaders in companies of all sizes and industries to transform technology into traction and markets into movements. He lives in Bend, Oregon.

Below is the text of my interview with Bruce and a preview of the kinds of insights you’ll find in Market Engineering presented in a Q&A format:

1. When does it make sense for a company to engage in Market Engineering?

Market Engineering isn’t something you save for later: it’s foundational from the moment you decide to bring a new product or company to life. The earlier you start intentionally defining or redefining your category, shaping positioning, and setting the narrative, the more leverage you have. If you wait until after a product launch or when you’re trying to scale, you’re forced to play by definitions set by incumbents or competitors, which makes differentiation and leadership much harder.

2. Why is it so important for a company to shape the market reality?

If you don’t shape your market’s reality, someone else will, often in a way that disadvantages you. Shaping market reality means you control how problems are defined, which features or metrics matter, and what the buying criteria look like. Market leadership is rarely awarded to the objectively “best” product; it’s achieved by those who frame the market in terms they can win.

3. Why must all leaders intimately understand the difference between a category and a market?

A market is the overarching territory: the set of buyers, sellers, and needs. A category is a specific frame or context you create and own within that market. If you only compete in the market, you become a commodity; if you define and then dominate a category, you set the standards and leave competitors playing catch-up. Leaders must understand this distinction so they can move from playing the existing game to rewriting the rules.

4. What do you think about the Chief Storyteller roles we see appearing in companies?

It’s a positive development; as long as the role goes beyond polished campaign stories and becomes architect and keeper of the full-market narrative. The best Chief Storytellers aren’t just marketers; they’re narrative engineers who unite product, category vision, customer proof, and internal culture into a coherent, resonant story that attracts and aligns stakeholders. Think Steve Jobs: one of the best storytellers ever.

5. Many see Thought Leadership as a combination of messaging and storytelling, what makes it a standalone tenet?

Thought Leadership stands alone because it’s about setting the agenda (leading the conversation) rather than just communicating your point of view. It requires original insight, provocation, and the courage to propose new models, not just synthesize existing ones. When done well, it changes the direction of the market; others start to echo your terminology and frameworks.

6. Why is it so hard for most new products to get traction?

Most new products fail to get traction not because of weak tech, but because of unclear value, undifferentiated positioning, or market confusion. Teams overfocus on features and under-invest in the story, category, and proof. Without clear market engineering, no one knows why the product matters or how they should think about it compared to everything else.

7. Where do companies go wrong with category design?

The most common mistake is either not designing a category at all (just trying to out-feature incumbents) or making it a “naming exercise” disconnected from authentic customer need and business reality. Category design isn’t branding; it’s systems thinking. it should be rooted in a real problem, codified with relentless clarity, and validated with influential customers and analysts.

8. How does the leadership team recognize they got the positioning wrong and how do they fix it?

You’ll know you have a positioning problem if deals stall in the pipeline, you get slotted into the wrong RFP bucket, or media/analysts lump you with solutions you don’t respect. Fixing it starts with honest investigation: talking directly to customers/prospects, auditing every touchpoint, and rigorously re-testing your Messaging Matrix. It’s usually about clarity, not cleverness.

9. What are the biggest pitfalls of message ownership and management and how can leaders avoid them?

The biggest pitfalls are lack of internal discipline and message drift: where every functional group tells the story a bit differently, or the narrative morphs with each campaign. Leaders must treat the messaging as a living, central artifact (like the Messaging Matrix), ensure frequent training, and make every update explicitly cross-functional. Messaging must be owned at the top.

10. What are some of the keys to great storytelling that every leader should master?

Great storytelling starts with empathy: a deep understanding of customer pain and aspiration. Then, it follows with clarity (no jargon), specificity (real data, real outcomes), and tension (what’s at stake in the market). Too often, stories become “laundry lists”. The key is to focus on a single arc: What’s broken in the world, what new future you’re inviting them into, and social proof that it’s real.

11. What are the keys to creating effective thought leadership?

You must have a strong point of view and the willingness to challenge conventional wisdom. Effective thought leadership is not just more content; it’s original, actionable ideas presented consistently across channels and validated with real-world outcomes, not just theory. Authenticity and a learning mindset are critical: the market rewards those who teach, not just those who promote.

12. Does AI make Market Engineering easier or more difficult and why?

AI makes Market Engineering both easier and much harder. Easier, because it democratizes access to research, market signals, and rapid content generation. Harder, because it amplifies noise and makes it much more difficult to stand out unless your positioning, messaging, and insight are precise and differentiated. The bar for clarity and originality rises: those who do Market Engineering well will thrive; those who don’t will be commoditized instantly.

13. Is there anything you wish I had asked so that you could speak to it?

I wish more people asked, “How do you maintain momentum and discipline in Market Engineering after the initial category launch?” Winning the first lap is one thing; evolving category leadership into true market leadership and dominance over the years is another. It’s not a one-time event: it’s ongoing narrative, data, partner ecosystem, and customer proof work. The companies that endure are those that outlearn, outevolve, and outlast, not just outlaunch their competition.

Conclusion

Thank you for the great conversation Bruce!

I hope everyone has enjoyed this peek into the mind of the man behind the insightful new title Market Engineering!

Image credits: Bruce Cleveland, Google Gemini

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We Need More Innovators and Scientists in Leadership Roles

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We Need More Innovators and Scientists in Leadership Roles

GUEST POST from Pete Foley

Our world is changing at an unprecedented rate. We are in an innovation driven economy. AI, genetic manipulation, energy innovation, climate, and virtually anything driving change are all highly technical and complex. And all come with high stakes pros and cons.

Scientists and innovators navigating this requires strategic leadership that understands technical complexity, uncertainty and that collectively has some knowledge of basic science and engineering.

Politics Lacks Scientists: Today, while more than half of US Senators have a law background, only one has a science PhD. I believe this creates a serious gap in fundamental knowledge between our strategic leaders and the innovators that are driving change.

Experts or Oracles? Of course, our leaders have access to ‘experts’ to help them with complex topics. But when the fundamental knowledge gap between leaders and experts becomes too big, experts become oracles. They pronounce rather than persuade. When this happens we risk the determining factor in strategy becoming superior communication skills, instead of knowledge or superior ideas. The ideas (and regulations) that win are not the necessarily best ones, but the ones championed by good communicators, salesmen scientists or smooth talking lobbyists. It’s dangerous to follow the science blindly, and even riskier to regulate what we don’t understand. That invites dangerous unintended consequences. But increasingly, that is the path we are on.

Why We Need More Innovators and Scientists in Leadership Roles

Of course, our leaders don’t need to all be 160 IQ polymaths with PhD’s in quantum mechanics. But to make good decisions they do need to at least be able to understand and apply critical thinking to the inevitably conflicting opinions of experts.

Communicating Science and Technology: Now of course, much of the onus for promoting understanding of complex technology lies with us in the broader innovation and science community. If we cannot communicate knowledge to people who own resources and executive power, then we risk that knowledge becoming redundant.

But communication is always a two way street. Bridging between leaders and experts requires some common ground. It’s really hard to have a useful discussion with someone who does even have a basic vocabulary for a topic. As technology and innovation become increasingly important, without more technically savvy leaders we risk a disconnect between strategy, regulation and knowledge. As our leaders get older, and more disconnected from the science driving change they rely less on quality of ideas, and more on appealing framing of ideas, or perhaps familiarity with equally disconnected experts. That is a dangerous path.

Non Scientific Mindsets Facing Technical Challenges. One key danger is the tendency to view choices as binary, another is sunk cost. Binary choices are superficially easy, but in the real world most innovation is not black and white, but instead involves some form of trade off. Whether it is AI, energy strategy, pharmaceutical development or one of the other ever growing list of emerging technologies, there are benefits, but also costs. With AI for example, the benefits of gaining and holding global leadership of the technology are likely as economically huge as the opportunity cost of not doing so. But with big opportunity also comes big risks, including the environmental costs of data centers, risks to societal structure, and even existential risk to humanity itself. The stakes don’t get much higher.

The Uncertainty Principle: And this is multiplied by the sunk cost fallacy. Over commitment to an incorrect binary choice can be really risky. While we know there are going to be pros and cons to any new technology, we rarely understand them very well in advance. Innovation is by definition a dive into the unknown, and that makes accurately predicting both upsides and downsides really difficult. This requires flexible, agile thinking, openness to new data, and a willingness to adjust mid-flight, skills inherent to science and technology .

But as a society, if anything we seem to be moving away from flexible thinking, and towards more rigid viewpoints that are often heavily pre-primed by affiliations, preconceptions and bizarrely, politics. People are often passionately for or against AI, but all too often without really knowing why. ‘Green’ energy is polarizing, climate change is divisive. But while passion and ownership have their place, often the best answer is not cheerleading for a team. Instead it’s beneficial to find a flexible balance that acknowledges the pros and cons, and that ideally identifies non zero sum answers for those contradictions. But that again typically requires nuance, and some level of technical understanding.

Finding Non Zero Sum Answers: The good news is that once we step away from polarized and binary thinking, non zero sum solutions are sometimes not as hard to find as we think. Just as an example, with AI, there is potential to have our cake and eat it. If we cut out digital slop, it’s conceivable that could we achieve and maintain technology leadership, but with much lower environmental cost. For example, using AI to solve complex medical problems may be a net benefit that is worth some damage to our wilderness, or use of our scarce resources. But action figures, generic illustrations, mediocre music and often pointless copies of master artists not so much! I’m sure all of the latter help advance our knowledge to some degree, and help to justify AI investment, but by being more selective, could we achieve the same or similar ends with a superior benefit/cost ratio?

The Human Advantage: But making smart trade-off decisions like this requires flexible and creative thinking. Ironically that is one of the things humans still do better than AI. We just need to embrace our human strengths, but also make sure our leaders also reflect those strengths.

Innovators in Leadership Roles: This means we need a more balanced and scientific approach to leadership if we are navigate the increasingly technology driven future. Having lawyers making laws is not bad per se, but I passionately believe we need a more diverse set of skills at our upper leadership levels if we are to effectively navigate the coming years. That means the innovation and scientific community needs to step up. We also need to get much better, and mea culpa, at communicating complex issues. It’s critical to be clear and simple but not simplistic.

The Tyranny of Simplicity: Simplistic answers, memes, and binary choices have a great deal of superficial appeal. And politicians and the media exploit this very effectively. In our information overloaded, time constrained world, everybody’s cognitive bandwidth is stretched. We often seek answers rather than understanding because that’s all we have time for. But from a leadership perspective, we need to understand that limited cognitive bandwidth is not the same as limited intelligence. People may grasp for simplistic answers, but because they have no commitment to them based on their own knowledge or critical thinking, that grasp is tenuous. This means that being simplistic can be self defeating in the long run. For example, take the much quoted, ‘globally agreed’ climate target; to not exceed a 1.5 degrees Celsius increase since pre-industrial times. For sure, some people will accept this without question. But other enquiring minds will ask if 1.49C OK? Is this a tipping point? Do we fall of a cliff at 1.51C. Conversely, what happens if we exceed that limit and nothing dramatic happens? Do we discard that boundary, or move it? Then there are obvious questions around how we address that boundary. What will it take to prevent crossing it? What are the trade offs? Who has the sphere of influence to actually make a difference? It’s OK to have a simplistic position, but it needs to be supported by layered reasoning.

Cry Wolf: I’m not suggesting that climate scientists who promote 1.5C don’t grasp this complexity. But somewhere in the path from science to politicians and media the real world complexity it often gets lost in translation. And thats not trivial, as it creates the risk of ‘cry wolf’ effects, and of leaders being perceived as manipulative. If we overstate the importance of 1.5 C, and it proves to be wrong, or at least a softer limit than previously advertised, we risk people perceiving that they have been mislead or manipulated. That then feeds skepticism, and even gives support to some of the wilder ‘conspiracy theories’. Once a source has become discredited on one vector, it is typically discredited on everything.

No easy answers to this. But I believe innovators and scientists really need to take a bigger leadership role in a world where innovation is increasingly the driving force. Politicians generally don’t get elected because they deeply understand complex issues, but because they understand how to motivate, communicate, simplify and manipulate. They often rely on peoples limited cognitive bandwidth, as this helps them to craft simple slogans, concepts, and sometimes trigger fear and division. Remember that we dislike losing something about twice as much as we like gaining it, which makes fear a very powerful manipulative tool. That brings power, but not necessarily wisdom. But limited cognitive bandwidth is not the same as limited intelligence. And simplistic concepts are vulnerable to challenge, or evolving data.

Of course, we don’t want to make every issue a PhD thesis. But we do need to acknowledge increasing complexity and uncertainty, and at the very least develop authentic, layered narratives that acknowledge complexity and the inevitable uncertainty of an innovation driven world. Without that, our strategies become extremely fragile, and easily shattered the first time we are proved wrong. Even if we may start from a position of intense conviction, we must also change paths in the face of compelling evidence. Scientists and innovators tend to be good at this. It’s a skill that maybe needs to be used more broadly

Image credits: Google Gemini

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You Have to Be Right in the Right Way

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You Have to Be Right in the Right Way

GUEST POST from Mike Shipulski

When something doesn’t feel right, respect your intuition. Even when you don’t know why it doesn’t feel right, respect your gut. When something doesn’t make sense, don’t judge yourself negatively. Rather, make the commitment to dig deeply until you hit the fundamentals. When a proposed approach violates something inside, don’t be afraid to say what you think is right. Or, be afraid and say it anyway. But right doesn’t mean your predictions will come true. Right means you thought about it, you understand things differently and you have a coherent rationale for thinking as you do. And right also means you don’t understand, but you want to. And right means something does not sit well with you and you don’t know why. And it means the right view is important to you.

Right doesn’t mean correct. And right doesn’t mean something else is wrong. When you have right view, it doesn’t mean you see things exactly right. It means you’re going about things in a way that’s right for the situation. It means your approach feels right to the people involved. It means you’re going about things with the right intention.

Now, like with any new idea, you’re obligated to formalize what you think is right and explain it to your peers. But, to be clear, you’re not looking for permission, you’re writing it down to help you understand what you think. When you try to present your thoughts, you’ll learn what you know and what you don’t. You’ll learn which words work and which don’t. You’ll learn right speech.

And you’ll find the potholes. And that’s why you present to your peers. They’ll be critical of the idea and respectful of you. They’ll tell you the truth because they know it’s better to iron out the details early and often. As a group, you’ll support each other. As a group, you’ll take the right action.

When ideas are introduced that are different, the organization will feel stress. Everyone wants to do a good job, yet there’s no agreement on the right way. Even though there’s stress, no one wants to create harm and everyone wants to behave ethically. It’s important to demonstrate compassion to yourself and others. The stress is natural, but it’s also natural to go about your livelihood in the right way.

But when the stakes are high and there’s no consensus on how to move forward, it’s not easy to hold onto the right mental state. The stress can cause us to delude ourselves into thinking things aren’t going well. But, letting the disagreement go unaddressed is unskillful, as it will only fester. It’s far more skillful to respectfully debate and discuss the disagreement. In that way, everyone makes the right effort to work things out.

Over time, the pattern of behavior can transition to a natural openness where ideas are shared freely. This becomes easier when we drop the mental habit of categorizing things into buckets we like and buckets we don’t. And it helps to maintain awareness of how things really are so we can strip away our subjective options. In this case, mindfulness is the right way to go.

None of this is easy. Our minds are constantly distracted by competing demands, growing to-do lists and organizational complexities of the work. Without dedicated practice, our minds can get lost in a flurry of thoughts of our own creation. To make it work, we’ve got to maintain a heightened alertness to our mental state and that takes the right concentration.

There’s nothing new here, but this well-worn path has merit.

Image credit: Unsplash

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Leadership Nightmares That Drive Employees Away

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Bosses Emailing at Midnight and Other Tales of Woe

Leadership Nightmares That Drive Employees Away

GUEST POST from Shep Hyken

“People don’t leave jobs. They leave bad bosses.”

There is truth to this unattributable quote. (I searched Google and ChatGPT, and neither could give me the definitive origin of this quote.) Validation comes from numerous articles and studies that claim a large percentage of employees quit their jobs because of bad managers.

A Harvard Business Review article, Quiet Quitting Is About Bad Bosses, Not Bad Employees by Jack Zenger and Joseph Folman, explains that employees don’t have to outright leave their jobs to “quietly quit,” or do only the bare minimum needed to keep their jobs. According to Gallup’s 2024 State of Global Workplace Report, only 23% of employees are engaged, 62% are “not engaged” and 15% are “actively disengaged.” And 70% of the variance in team engagement is due to the manager.

When you look at the best companies to buy from, you often find they are also listed on Glassdoor.com as the best companies to work for. That direct correlation isn’t a coincidence. In my customer service and customer experience (CX) work, I recognized decades ago that what’s happening on the inside of an organization is felt by customers on the outside. The employee experience is as important, if not more so, than the customer experience, and the boss can “make or break” that experience.

Meet Mita Mallick, who once had a boss who would only communicate with her via email between 10 p.m. and 2 a.m. That seemed to be the only time the boss was available. Trying to meet with her boss during normal business hours was an exercise in futility. The message was clear: “I don’t have time for you.” And as a junior employee, Mallick thought she had to respond in real time to keep her job.

That experience, along with others, is why Mallick, who is now an author and speaker on a mission to “fix what’s broken in the workplace,” wrote the book, The Devil Emails at Midnight: What Good Leaders Can Learn from Bad Bosses. This book shares the details of 13 bosses, herself included, who demonstrate what not to do.

I interviewed Mallick for an episode of Amazing Business Radio to learn about some of these bad bosses, in hopes that anyone who falls into that category of leadership might learn a lesson and make their employees’ experience better. Here are descriptions of just a few of the bad bosses Mallick talked about in our interview, along with some of my commentary:

The Boss Who Never Had Time for Employees—Except at Midnight

As mentioned, this is where the book begins, with a boss who didn’t respect employees’ time or explain that just because she worked at midnight, she didn’t expect her employees to do the same. A simple explanation that immediate responses to her late-night emails weren’t necessary would have been easy, but unfortunately for Mallick, that was not the case. Everything seemed urgent, and Mallick emphasized this by saying, “When we treat everything as urgent, nothing is urgent.”

The Lesson: Leadership means making time for your team. Respect employees’ time and boundaries.

The Boss Who Wouldn’t Call an Employee by Name

Mallick shared that a boss didn’t want to call her by her full first name, Madhumita. Because he struggled to pronounce her full name, he renamed her Mohammed. One day, she worked up the courage to say, “You can call me Mita,” but the insensitive boss smiled and said, “Oh, Mohammed is funny. Everyone loves it. Don’t be so sensitive!” No doubt an HR issue by today’s standards, this boss showed a lack of respect for a good employee.

The Lesson: Calling people by their correct names is a basic courtesy and sign of respect. But there’s more to this. It’s not just about a name. Recognizing something sensitive and/or important to an employee should be acknowledged and accepted. Teasing about it will, at a minimum, put distance between the boss and employee.

The Boss Who Was Filled with Toxic Positivity

An upbeat and energetic boss is great, but ignoring real problems and acting like everything is fine is known as toxic positivity. If the facts indicate that something isn’t possible, then pretending it is can set a team up for failure and disappointment. Cheerleading only helps so much. If the boss hypes everyone up to believe something impossible can be done, and then the team fails, it can be demoralizing to the team.

The Lesson: Leaders should inspire, but not at the cost of reality.

Final Words

The worst behaviors in any workplace become part of its culture if they are allowed to continue. Whether it’s disrespect, slacking off or bullying, what leaders let slide becomes the norm. Look at yourself in the mirror and ask, “Am I one of these people causing the problem?” Creating a positive environment means taking action when problems arise, not ignoring them. A healthy workplace looks out for everyone, not just the loudest or most powerful voices.

The Final Lesson: Culture is defined by what is tolerated and demonstrated by the boss.

This article was originally published on Forbes.com.

Image Credit: Unsplash, Shep Hyken

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Understanding Polarization

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Understanding Polarization

GUEST POST from Geoffrey A. Moore

One might be forgiven for thinking that our world is undergoing an unprecedented crisis of polarization, but to help put things in perspective, here are some lyrics from a song sung by the Kingston Trio in 1959 to a tuneful minuet:

The whole world is festering
With unhappy souls
The French hate the Germans
The Germans hate the Poles

Italians hate Yugoslavs
South Africans hate the Dutch
And I don’t like
Anybody very much.

Polarization has been with us throughout recorded history. What is bringing it to crisis proportions in our era is a digitally connected world population being fed a stream of narratives that are constructed specifically and intentionally to exacerbate the problem. If we are going to navigate our way through this challenge, we need to get a better understanding of how polarization works and what it takes to depolarize.

How Polarization Works

Polarization begins when we embrace an opinion so deeply we incorporate it into our personal identity. It becomes part of the narrative we use to make sense of the world and our lives, and in this way becomes inseparable from our sense of self. An attack on such an opinion strikes at the very foundations of our personhood, something we hold inviolate, something we will defend to the death. This results in a “no-fly zone” of non-negotiability, a ring-fence that we will not allow to be breached.

Clearly, this is dangerous stuff, and we would all do well to avoid it altogether. Indeed, one way to think of spiritual enlightenment is to have grounded one’s identity in a state of being outside the realm of opinions. One still has opinions, but one controls them instead of having them control you. Unfortunately, but for a few saints and enlightened Buddhas, there are precious few of us who can claim that state. Most of us hold (or are held by) positions on one or more issues of contention that we simply refuse to entertain abandoning. That, let us say, is normal. But we need to understand, these are not positions of strength. They are not assets. They are liabilities. They make us vulnerable in all sorts of ways, some of which we might not appreciate or even detect.

Why do we do this? Our identities are anchored in narratives, stories we tell about ourselves and that others tell about us. They tell everyone including ourselves who we are. These narratives are organized around protagonists and antagonists. We seek to emulate the protagonists and defeat the antagonists. Now, the antagonists don’t have to be people. They can be challenges like crime or poverty or sickness or climate change. More often, however, they do end up being people, people we don’t know in all likelihood but who stand for the very things that we are so clearly against. The weird part about this is that they feel exactly the same way about us! But, how can that be? We are in the right, they are in the wrong, why don’t they see that? Instead, bizarrely, they are saying the same thing.

OK, this is pretty obviously a trap of our own making, and as adults, it is incumbent upon us to resist its effects as best we can. It is also clear that we come up short more often than one would like. So, for the time being, let us assume that some amount of polarization is a fact of life, and in that context, take stock of what that entails.

On a personal level, polarized beliefs make us susceptible to righteousness. We are deeply certain we are right and, when put under sufficient pressure, entitled to take whatever action we feel is necessary, even when that involves breaking the law. We have no interest in understanding our opponents or negotiating with them. We are in our very own “no-fly zone,” and we carry it with us wherever we go. This takes a toll on us but perhaps more importantly on our friends and family as well. They either have to capitulate and participate in our vision, or they have to skirt the issue altogether. Direct honest communication would require a level of vulnerability we are unwilling to entertain.

As citizens, polarized beliefs make us susceptible to political manipulation. Demagogues can engage our psyches by demonizing our antagonists, inflaming our righteousness with calls to action that speak to our very souls. We will bond with these leaders regardless of their histories because we are not interested in evidence, only validation. We unite with them around what is wrong and then allow them to define what is right as the destruction of what is wrong. It is a playbook that has been used throughout history, sad to say, because it is very, very effective. We see this in other people all the time. We need to see it in ourselves as well.

Next up: On Depolarization

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

Image Credit: Pixabay

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Have You Ever Encountered the Slow No?

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GUEST POST from Mike Shipulski

When there’s too much to do and too few to do it, the natural state of the system is fuller than full. And in today’s world we run all our systems this way, including our people systems.

A funny thing happens when people’s plates are full – when a new task is added an existing one hits the floor. This isn’t negligence, it’s not the result of a bad attitude and it’s not about being a team player. This is an inherent property of full plates – they cannot support a new task without another sliding off. And drinking glasses have this same interesting property – when full, adding more water just gets the floor wet.

But for some reason we think people are different. We think we can add tasks without asking about free capacity and still expect the tasks to get done. What’s even more strange – when our people tell us they cannot get the work done because they already have too much, we don’t behave like we believe them. We say things like “Can you do more things in parallel?” and “Projects have natural slow phases, maybe you can do this new project during the slow times.” Let’s be clear with each other – we’re all overloaded, there are no slow times.

For a long time now, we’ve told people we don’t want to hear no. And now, they no longer tell us. They still know they can’t get the work done, but they know not to use the word “no.” And that’s why the Slow No was invented.

The Slow No is when we put a new project on the three year road map knowing full-well we’ll never get to it. It’s not a no right now, it’s a no three years from now. It’s elegant in its simplicity. We’ll put it on the list; we’ll put it in the queue; we’ll put it on the road map. The trick is to follow normal practices to avoid raising concerns or drawing attention. The key to the Slow No is to use our existing planning mechanisms in perfectly acceptable ways.

There’s a big downside to the Slow No – it helps us think we’ve got things under control when we don’t. We see a full hopper of ideas and think our future products will have sizzle. We see a full road map and think we’re going to have a huge competitive advantage over our competitors. In both situations, we feel good and in both situations, we shouldn’t. And that’s the problem. The Slow No helps us see things as we want them and blocks us from seeing them as they are.

The Slow No is bad for business, and we should do everything we can to get rid of it. But, it’s engrained behavior and will be with us for the near future. We need some tools to battle the dark art of the Slow No.

The Slow No gives too much value to projects that are on the list but inactive. We’ve got to elevate the importance of active, fully-staffed projects and devalue all inactive projects. Think – no partial credit. If a project is active and fully-staffed, it gets full credit. If it’s inactive (on a list, in the queue, or on the road map) it gets zero credit. None. As a project, it does not exist.

To see things as they are, make a list of the active, fully-staffed projects. Look at the list and feel what you feel, but these are the only projects that matter. And for the road map, don’t bother with it. Instead, think about how to finish the projects you have. And when you finish one, start a new one.

The most difficult element of the approach is the valuation of active but partially-staffed projects. To break the vice grip of the Slow No, think no partial credit. The project is either fully-staffed or it isn’t And if it’s not fully-staffed, give the project zero value. None. I know this sounds outlandish, but the partially-staffed project is the slippery slope that gives the Slow No its power.

For every fully-staffed project on your list, define the next project you’ll start once the current one is finished. Three active projects, three next projects. That’s it. If you feel the need to create a road map, go for it. Then, for each active project, use the road map to choose the next projects. Again, three active projects, three next projects. And, once the next projects are selected, there’s no need to look at the road map until the next projects are almost complete.

The only projects that truly matter are the ones you are working on.

Image credit: Pexels

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Customers Don’t Care About Your Profit

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They Care About Your Service

GUEST POST from Shep Hyken

Recently, I heard from one of our subscribers, a sales and finance consultant at a luxury automobile dealership. He shared a story about how a customer was almost mistreated.

In the world of auto sales, some salespeople are 100% commission-based, and when they sell a vehicle at a discounted price, there is little to no profit, resulting in a very small commission. This is important, as sometimes these low-commission sales cause employees to treat customers differently than they would for a high-commission sale.

Customers expect to be treated the same regardless of how much or little they pay for their vehicle. Furthermore, they don’t realize, nor do they care, how much of a sales commission is paid to the employee.

Shep Hyken Customer Service vs Profit Cartoon

That brings us to the customer who bought a two-year-old luxury sports car. The first time it rained, she realized the windshield wipers needed to be replaced. The customer called her salesperson, who explained that he was happy to replace the blades. He went to his sales manager to ask how to handle the replacement and was told to charge her the cost of the blades or to tell her to buy them at Walmart for less than the dealership’s cost and bring them in to have them replaced.

The salesperson was shocked and reminded his sales manager that they were selling a premium brand. Eventually, the manager agreed, but the experience reminded him that profit, or the lack thereof, dictated the level of service the dealership would offer.

Three Customer-First Lessons

With that in mind, let’s use the story as a learning experience for all businesses. Here are three lessons from the story:

The Customer Doesn’t Care about Your Profit: Every customer deserves respect and a consistent experience, whether it’s $20 transaction or a $200,000 one. Profit per interaction shouldn’t determine the level of care.
Know the Lifetime Value of the Customer: The wiper blades may have been a $20 problem, but how the customer was treated for the problem could determine the future sale of a high-end luxury automobile worth thousands of times more. Knowing the average value of a customer will help employees make more informed, customer-focused decisions. Small gestures today can protect long-term loyalty and repeat business.
Consistency Builds Trust: Luxury brands thrive on consistent treatment, but the principle applies to all types of businesses. Today’s customers demand a good customer experience. Train and empower employees to deliver a consistent standard of service, every time, for every customer.

In the end, customers remember the experience, not your profit margins. Get the small things right, and the money follows as you earn their trust, confidence, and loyalty.

Image Credit: Unsplash, Shep Hyken

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