AI, Outcomes, and Leadership: What Tomorrow’s Disruptive Companies Are Doing Differently | DisrupTV Ep 445
AI, Outcomes, and Leadership: What Tomorrow’s Disruptive Companies Are Doing Differently | DisrupTV Ep 445
The billable hour is dying. Homogeneity is innovation’s enemy. And the leadership trap most likely to cap your organization’s potential is one you built yourself.
Key Takeaways
- The billable hour is dying. Outcomes are the new arbitrage. Gain-share models — where vendors win only when clients make or save money — are replacing time-and-materials contracts. If you can’t tie your work to measurable results, you’re exposed.
- AI is the middle market’s great equalizer. Mid-size companies ($50M–$500M in revenue) can now achieve the leverage that once required billions in investment and massive headcount. The ability to pilot forever is gone — but so is the need to.
- The real AI efficiency gain is eliminating the coordination tax. Middle management layers, compliance handoffs, and low-value transactional overhead are where AI is actually moving the needle today — not in headline-grabbing automation.
- Governance and culture come before the model. The hard work of AI leadership isn’t choosing the right LLM — it’s setting clear purpose, establishing guardrails, creating safe sandboxes, and getting people to take more risk than they’re naturally comfortable with.
- Operational AI is just the beginning. Innovation AI is the prize. Companies with vertically specific data sets and a defensible value proposition can use AI not just to run better, but to build entirely new moats around distribution and insight.
- The services firms that win will be smaller, flatter, and outcome-obsessed. A 1,000-person AI-native services firm could disrupt a global SI. The winners will be built on results, not headcount arbitrage.
- The “why can’t they be like me” trap kills innovation. Leaders who unconsciously surround themselves with clones of their own thinking, background, and style will cap what their organizations can achieve. Diversity of thought is not a soft value — it is the engine of breakthrough.
- Your greatest strength, taken to an extreme, becomes your greatest liability. Executive coaching’s most important job is helping leaders see where overused strengths have quietly become blind spots.
- True executive coaching is still a human relationship. AI can support mid-level leaders with prompts and accountability, but senior executives need a human coach capable of real-time pattern recognition, nuance, and trust built over time.
- Schedule deliberate random collisions. Once a week or month, have a genuine conversation with someone outside your usual circle, in a part of the business you know nothing about. It’s one of the simplest ways to escape the echo chamber of your own success.
Part 1: AI, Outcomes, and the Disruption of the Services Model
Alejandro Laplana opens with a thesis that is simple and radical in equal measure: the billable hour is dying, and outcomes are the new arbitrage. At Shokworks, the AI-first digital transformation consultancy he leads — whose clients include HBO, FC Barcelona, and the United Nations — he has been building toward gain-share models since before they were fashionable.
In a gain-share arrangement, the vendor wins only when the client makes money or saves money. There are no comfortable invoices for time spent regardless of results. That alignment of incentives fundamentally changes the relationship: it rewards lean, risk-tolerant firms willing to take accountability for outcomes, and it threatens traditional time-and-materials consultancies that have coasted on complexity and hours billed.
“The notion that you would have a very comfortable and thriving business just by invoicing man-hours as opposed to tying yourself to concrete results and business outcomes — the traditional standard time-and-materials model isn’t as relevant as it formerly was.”
In an AI-first world, value is no longer in hours worked. It is in outcomes delivered. Vendors who can quantify and share in that value will displace those who cannot.
Why the Middle Market May Be AI’s Biggest Sleeper Advantage
Unlike much of the AI conversation, which focuses on Global 2000 enterprises, Laplana’s bias is explicit: he prefers the middle market. Companies in roughly the $50 million to $500 million revenue range cannot afford endless pilots and proofs of concept. They must tie AI initiatives directly to outcomes from day one, which forces a clarity of purpose that larger enterprises rarely achieve.
That constraint turns out to be an advantage. With the right strategy and data, mid-market companies can now achieve the leverage that once required billions in investment and massive headcount — competing on efficiency and intelligence rather than scale alone.
“We’re not big enough to actually achieve scale under conventional models, but AI can actually help us achieve that shift where now we have the leverage that you formerly needed billions invested to achieve.”
AI is a genuine force multiplier for companies in this range. The structural disadvantages of size — less capital, smaller teams, fewer resources for experimentation — are rapidly becoming less decisive.
Killing the Coordination Tax
Where is AI actually moving the needle today, beyond the hype? Laplana points directly at middle management layers and transactional overhead — what might be called the coordination tax that most organizations pay without ever examining.
He offers a concrete example from compliance workflows: processes that once required a handful of consultants to manually review and rubber-stamp documentation can be compressed dramatically with AI and automation. Cycle times shrink, third-party involvement drops, and costs fall — all without eliminating the work, just the friction around it.
This is where Ray and Constellation Research’s own analysis converges: BPO is on a long-term structural decline, and outcome-centric, AI-powered services are replacing it. The coordination tax that sustained entire industry segments is being automated away.
Governance, Security, and Culture: The Real Work of AI Leadership
Technology, Laplana is clear, is not the hard part. Change is. When CEOs and boards come to Shokworks asking how to manage AI risk and opportunity, he doesn’t start with models. He starts with governance and acculturation.
Step one is the governance and security layer: establishing exactly how the model should be used, ensuring the model is not training on proprietary client data, and putting the right boundaries in place before anyone touches a prompt. Step two is acculturation: building a sandbox where employees can experiment, fail safely, and — paradoxically — learn to take more risk than they would in their default, risk-averse mode.
Leadership’s role in this process is to set clear purpose and end states for AI initiatives, model the right mindset even if they are not the most technical person in the room, and sponsor and legitimize new ways of working. Not every legacy leader will become an agentic AI power user. But every leader must become an active advocate for the transformation.
From Operational AI to Innovation AI: Where the Real Long-Term Value Lives
Much of the early enterprise AI story has been about operational efficiency: do things faster, cheaper, with fewer people. Laplana argues that the real long-term prize is innovation and data monetization.
Companies with vertically specific data sets and a genuinely differentiated value proposition can use AI not just to run better, but to build entirely new competitive moats. Operational AI improves today’s business. Innovation AI creates tomorrow’s.
Ray connects this to a historical pattern visible in pharma and biotech: the players who own distribution and data repeatedly acquire the innovators and extract the value. The open question for AI-powered services is who will own that distribution layer — hyperscalers, legacy vendors, or new entrants willing to embed gain-sharing into their fundamental business model.
Laplana’s answer is pragmatic: the firms that win will be those that own outcomes, embed risk-sharing, and build defensible relationships and data over time. The usual suspects may not be the ones leading that charge.
The Future of Services: Smaller, Smarter, Outcome-Obsessed
Ray offers a prediction that is hard to dismiss: a 100-person AI-native software company could displace a traditional software vendor, and a 1,000-person AI-native services firm could disrupt a global systems integrator. Laplana essentially agrees.
The services firms that win in the AI era will be smaller and flatter than today’s giants, built on outcomes rather than headcount arbitrage, deeply data- and distribution-aware, and comfortable taking risk in exchange for upside. Those willing to embed their profits into outcome-based delivery will be the challengers that earn a seat at the new table.
Part 2: The Leadership Trap That Caps Every Organization’s Potential
Claire Díaz-Ortiz joins the conversation to discuss her new book co-authored with Marshall Goldsmith: Why Can’t They Be Like Me? A Parable of Shifting Perspectives. The title captures one of the most pervasive and quietly destructive traps in leadership: the belief that because a particular way of behaving, working, and thinking made you successful, everyone around you should simply do the same.
She illustrates the cost of this trap with a vivid analogy: imagine being stuck in an escape room where every other person shares your exact background, demographic, and frame of reference. Your odds of getting out are not good. Innovation, problem-solving, and breakthrough thinking all require diversity of perspective — and leaders who unconsciously build teams of clones are slowly walling themselves off from the ideas they most need.
“If the only other people I am with are people who grew up in the exact same context I did, I would not have high levels of hope. You need diversity of thought to generate innovation.”
This problem is especially pronounced in environments where speed to trust and pattern recognition drive decisions — like Silicon Valley, where investors and leaders often gravitate toward people who look and think like them. The result is execution efficiency in the short term, and innovation poverty in the long run.
When Strengths Become Liabilities
One of the more subtle and important insights Díaz-Ortiz surfaces is the way that the qualities driving individual success can become the very liabilities that limit leadership effectiveness. Incredible focus becomes myopic leadership. High standards become an inability to delegate. Speed becomes impatience with the people and processes needed to scale.
“Anything taken to an extreme can be a challenge in leadership.”
Executive coaching, in this framing, is partly about helping leaders see where their overused strengths have quietly become blind spots — and creating the self-awareness to decide whether they genuinely want to change. That last point matters: you can shift a leader’s trajectory even in crisis, but only if they truly want to change. Coaching cannot manufacture that desire.
What AI Can and Cannot Do in Executive Coaching
Díaz-Ortiz draws a careful line around AI’s role in leadership development. For mid-level leaders and managers, AI tools are increasingly capable of providing useful prompts, supporting accountability, and acting as an always-on nudge coach — accessible, patient, and consistent in ways a human coach cannot always be.
But for senior executives, she draws a hard line: true executive coaching still requires a human relationship built on deep trust. The value in high-end coaching is not generic best practices or behavioral frameworks. It is the real-time pattern recognition, lived experience, and ability to read nuance and non-verbal signals in the moment that no algorithm can yet fully replicate.
The distinction matters because conflating the two — treating AI-assisted coaching and human executive coaching as equivalent — risks leaving senior leaders without the kind of honest, trusted relationship that actually shifts behavior at the highest levels.
Practical Advice: Escaping the “Like Me” Trap
Díaz-Ortiz ends with advice that is simple enough to dismiss and powerful enough to change an organization’s trajectory: once a week, or at minimum once a month, have a genuine conversation with someone who is not in your boardroom, not in your department, and not in your area of expertise. Listen to how another part of the business works, what it is trying to solve, and where it sees the world differently.
These deliberate random collisions — scheduled, intentional, and genuinely curious — are one of the most effective ways to challenge the assumptions that success quietly accumulates around a leader. They expose you to the signals your echo chamber filters out, and they build the diversity of perspective that every escape room — and every organization — actually needs.
Final Thoughts
DisrupTV Episode 445 holds together two conversations that might seem separate but are ultimately about the same thing: the gap between what made organizations successful yesterday and what will make them competitive tomorrow.
Alejandro Laplana’s argument is that the business model itself is being disrupted. Charging for time is being replaced by sharing in outcomes. Headcount arbitrage is being replaced by AI-powered leverage. Endless pilots are being replaced by the discipline to define purpose, set clear end states, and tie every initiative to a measurable result. The firms that internalize this shift early will define the next generation of services. The ones that don’t will find themselves displaced by smaller, faster, and far more accountable competitors.
Claire Díaz-Ortiz’s argument is that the leader themselves is often the constraint. The very patterns of thinking, hiring, and deciding that built a successful career can cap an organization’s potential if they go unexamined. Building diverse teams, seeking out deliberate random collisions, and working with a trusted human coach to surface blind spots are not soft leadership practices — they are the structural requirements for sustained innovation in a world that is changing faster than any single person’s experience can track.
“The organizations that win in the AI era will be outcome-obsessed and perspective-rich. The leaders who get there will be humble enough to know what they don’t know — and intentional enough to go find it.”
Related Episodes
If you found Episode 445 valuable, here are a few others that align in theme or extend similar conversations:
Your Hosts
R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg. Short Bio R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...
Read moreElle is the Director of Marketing at Constellation Research and producer of the weekly enterprise tech show, DisrupTV. She leads marketing and communications efforts across all avenues while also managing an array of programs including the SuperNova Awards, Business Transformation 150, AX100, AI150, S50 and ShortLists. She has previous experience in public relations and has a strong passion for the marketing world. Contact Elle at [email protected] ...
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