S&P Global Market Intelligence reports that 42% of companies are expected to abandon their AI initiatives in 2025, more than double the 17% failure rate from 2024.
While companies pour billions into AI, cybersecurity, and quantum technologies, they’re missing the most critical component: leaders who can actually make these investments successful. This leadership gap represents more than a doubling of failure rates in a single year, making it the most acute technology transformation crisis in recent memory.
The crisis runs deeper than most realize. Carnegie Mellon’s HCI Institute reports that nearly 90% of AI projects ultimately fail, while RAND Corporation findings show 80% of AI initiatives fail to deliver business value. Even more concerning: only 12% of enterprise leaders report being “very successful” in translating AI strategy into operational outcomes.
This occurs despite unprecedented investment levels. McKinsey’s 2025 research shows that 92% of companies plan to increase their AI investments over the next three years, yet only 1% report that their AI deployments have reached full maturity.
The disconnect between investment enthusiasm and transformation success points to a fundamental misunderstanding of what drives AI initiative success.
You’re Hiring the Wrong Capabilities
Everyone’s talking about hiring AI experts for executive roles. That’s precisely the problem.
When companies race to deploy artificial intelligence systems, they create an essential deficit in experienced leaders who understand AI’s business implications and can explain them to stakeholders across the organization.
This growing leadership gap is a significant barrier to effectively leveraging AI technologies.
2025’s most valuable executives are translators who can connect AI ethics to quarterly earnings, machine learning models to risk management frameworks, and algorithmic decision-making to investor relations. They are hybrid leaders, rarer than the AI technologists they lead.
The Skills Crisis Behind the Leadership Gap
Nash Squared’s 2025 Digital Leadership Report reveals the most dramatic technology skills shortage in over 15 years, with AI skills shortages jumping from 28% of organizations in 2023 to 51% in 2025, an 82% increase. AI has vaulted from the sixth most scarce technology skill to number one in just 18 months.
The scope of this shortage and leadership gap is unprecedented. McKinsey Global Institute projects that demand for analytics translators, professionals who bridge technical and business expertise may reach 2 to 4 million in the United States alone by 2026. Meanwhile, cybersecurity skills shortages have risen by 22% globally, driven by AI-related security complexities.
Here’s the paradox: despite 90% of technology leaders now investing in AI (up from 59% in previous reports), over two-thirds report no measurable return on investment from their AI initiatives. This disconnect between investment and returns highlights a critical leadership gap rather than technological limitations.
What Actually Drives AI Success
Most executive searches for AI leadership roles follow a predictable pattern: organizations seek candidates with advanced technical qualifications, extensive machine learning experience, and graduate degrees in computer science or data science.
The assumption seems logical: Who better to lead AI initiatives than someone who built neural networks or developed deep learning algorithms?
The reality? This approach consistently produces disappointing results.
Research from Boston Consulting Group shows that AI transformation success depends 70% on people and process transformation, 20% on technology infrastructure, and only 10% on AI algorithms and models.
Organizations that prioritize people and process see dramatic results: 50% higher revenue growth, 60% higher total shareholder return, and 40% higher return on invested capital.
McKinsey’s 2025 State of AI research confirms this pattern, finding that CEO oversight of AI governance is the element most correlated with higher self-reported bottom-line impact from organizational AI use. The research reveals that workflow redesign has the biggest effect on an organization’s ability to see EBIT impact from generative AI use.
The Technical Expertise Trap
Technical depth in AI, while valuable, fails to automatically translate to executive effectiveness. The machine learning expert who can optimize neural networks may struggle to explain AI bias implications to a board. The data scientist who publishes breakthrough research might find it difficult to navigate AI governance discussions with regulators.
Research consistently shows that 85% of AI projects fail due to poor alignment between business goals and technical execution, with miscommunication between leadership and engineering teams leading to ineffective implementations.
Gartner’s analysis reveals that costs for developing and deploying Gen AI models range from US$5m to US$20m, with unpredictable cost structures. Technical leaders, while essential for implementation, often lack the business context to navigate these complex cost-benefit decisions effectively.
RAND Corporation research shows that projects often falter because executives “misunderstand the real problem AI is supposed to solve, set unrealistic expectations, or chase the latest technology trend without a clear business case.”
The Transformation Reality
Modern AI deployment represents organizational transformation disguised as technology implementation. When companies deploy AI systems, they discover that the technology itself accounts for only 20 to 30% of the challenge. The remaining 70 to 80% involves business process reengineering, role redefinition, workflow optimization, and cultural adaptation.
Consider what happens when an organization implements AI-driven customer service systems:
The technology enables automated response capabilities, yet success demands reimagining customer interaction workflows, retraining people for complex escalations, restructuring quality metrics, implementing bias monitoring protocols, and adapting management approaches while protecting brand reputation from potential AI failures.
Every function experiences impact: operations must redesign processes around AI governance frameworks, sales teams adjust to AI-augmented lead qualification while ensuring fair treatment across customer segments, and HR must rightsize responsibilities across the organization while managing workforce anxiety about role evolution.
This transformation extends beyond process redesign to foundational elements: data quality, infrastructure readiness, and most critically, employee trust and adoption. Research shows that while employees look to leaders for AI guidance, many feel their leaders aren’t prepared to support them through this change.
This transformation dynamic explains why technical leaders often struggle in AI implementation roles. They focus on optimizing algorithms while the real challenge lies in orchestrating organizational change across multiple functions simultaneously.
Enter the Translator Executive
The executives succeeding in AI leadership share an entirely different profile. They possess what I call “translator capabilities”: the ability to move fluidly between technical and business contexts, converting complex technological concepts into strategic insights that drive decision-making.
McKinsey’s research on analytics translators describes professionals who “play a critical role in bridging the technical expertise of data engineers and data scientists with the operational expertise of marketing, supply chain, manufacturing, risk, and other frontline managers.”
These leaders differ fundamentally from technical specialists. McKinsey research clarifies that translators “are neither data architects nor data engineers. They’re not even necessarily dedicated analytics professionals, and they don’t possess deep technical expertise in programming or modeling.” Instead, they ensure that “deep insights generated through sophisticated analytics translate into impact at scale in an organization.”
These leaders:
- Rarely write machine learning code, yet understand the implications of algorithmic limitations
- May lack the ability to design neural network architectures, yet grasp what AI implementation means for organizational workflows and customer experience
- Seldom build AI models from scratch, yet recognize bias patterns and can articulate AI governance requirements to regulators and stakeholders
Similar translation challenges exist in cybersecurity convergence and quantum computing deployment, which we will explore in future articles in this series.
Why Traditional Hiring Backfires
The translation crisis stems from a fundamental misunderstanding of what AI leadership actually requires. Organizations continue to prioritize technical credentials over business acumen, domain depth over stakeholder navigation skills, and individual expertise over team orchestration capabilities.
Here’s why this approach backfires in three critical ways:
Technical expertise becomes obsolete quickly. The half-life of specific AI technical skills is measured in months, rather than years. Betting an executive hire on current machine learning knowledge resembles hiring a race car driver based on expertise with one specific track – valuable for one race, limiting for a career spanning multiple circuits.
Business context determines technology success. The most sophisticated AI implementation fails without proper change management, stakeholder alignment, and organizational readiness. Technical leaders often lack the business context to navigate these challenges effectively.
Traditional hiring ignores enterprise-wide leadership needs. Organizations hire for individual technical expertise rather than leaders who can enable organization-wide decision-making about AI investments, risk management, and strategic positioning across ethical and legal considerations.
The Full Scope of AI Transformation
Understanding why translator executives are essential requires recognizing what AI transformation actually demands across three critical dimensions:
People: Role redefinition as AI automates routine tasks, skills evolution to work alongside AI systems, building employee trust and confidence in AI-driven change, and leadership development to manage AI-augmented teams effectively.
Process: Workflow redesign around AI capabilities and limitations, governance frameworks for AI decision-making, bias monitoring and mitigation protocols, data privacy and security controls, quality assurance systems that account for algorithmic unpredictability, and the foundational data infrastructure that enables AI effectiveness.
Technology: The AI implementation itself – often representing only 20–30% of the total transformation challenge, yet traditionally receiving 80% of leadership attention during hiring decisions.
Translator executives excel because they understand this full scope and can orchestrate change across all three dimensions simultaneously.
The Competitive Advantage Hiding in Plain Sight
Organizations that recognize this leadership gap possess a significant competitive advantage. While competitors chase scarce technical talent and focus primarily on the technology dimension, they can access a broader pool of candidates who understand the full people-process-technology scope of AI transformation.
These translator executives who, in essence close the leadership gap often emerge from unexpected backgrounds: AI consultants who have explained machine learning implications to business clients, product managers who have bridged data science and business teams, former engineers who transitioned into AI strategy or venture capital roles focused on AI investments.
The key lies in recognizing that AI translation capabilities, while rare, can be identified and developed more readily than deep machine learning expertise. As McKinsey’s research indicates, an experienced business leader can acquire sufficient AI literacy to excel in transformation leadership, while teaching a machine learning expert to navigate complex stakeholder relationships and business dynamics proves far more challenging.
The Market Reality
The compensation data tells the story. Riviera Partners’ 2025 Executive Compensation Report confirms that AI leadership roles are commanding premium compensation packages and are often compensated at higher total compensation levels than traditional roles like CTOs or CPOs.
However, only 8% of S&P 500 companies have publicly disclosed a senior-level position with a direct AI focus, and just 4% have established C-suite titles that explicitly reference artificial intelligence. This gap between demand and supply explains the compensation premiums and competitive dynamics in the AI executive market.
Companies achieving AI transformation success demonstrate distinct patterns. McKinsey research on successful implementations shows that organizations with advanced AI capabilities achieve 1.5 times higher revenue growth, 1.6 times greater shareholder returns, and 1.4 times higher returns on invested capital.
What This Means for Your Organization
Organizations continuing to hire for AI technical depth alone will struggle to find qualified candidates and may end up with leaders who struggle to bridge the technical-business divide. Those who adapt their hiring approach to prioritize AI translator capabilities will access a broader talent pool, achieve better technology-business alignment, and build leadership teams capable of navigating the complex intersection of AI technology and business strategy.
With failure rates between 42-90% across multiple studies, AI skills shortages at 15-year highs, and only 12% of leaders achieving successful transformation outcomes, the current approach is demonstrably inadequate.
Organizations that understand the translator executive concept will access broader talent pools, achieve better technology-business alignment, and build leadership teams capable of turning emerging technology investments into sustainable competitive advantages.
The question is no longer whether you have AI technology: it’s whether you have leaders who can translate AI capabilities into strategic advantages.
AI’s New Power Brokers: The Translator Executive Series
Over the next eight weeks, I’ll introduce you to a new category of executive that forward-thinking organizations are building competitive advantages around: Translator Executives. These leaders understand technology and translate its implications into business strategy, stakeholder communications, and organizational transformation.
Part 1: Beyond Technical Expertise – Why the executive profile that drives AI transformation looks different from what most organizations are hiring for
Part 2: The Three Translator Archetypes – Strategic Bridge Builders, Risk Navigators, and Innovation Orchestrators: which one does your organization need?
Part 3: The Compensation Premium – Why translator executives command higher pay than traditional technical leaders plus how to structure offers that win
Part 4: The Remote Advantage – Data showing why geography lacks relevance for translator executives
Part 5: The Diversity Dividend – How non-traditional backgrounds create better translator executives while driving higher performance
Part 6: Interview Strategies – Tactical frameworks for assessing translation capabilities without requiring deep technical knowledge
Part 7: Building Internal Capability – How to develop translator skills within your current team using proven development programs
Part 8: The Future of Executive Search – How leading search firms are adapting plus what the translator executive market will look like through 2030
Who This Series Is For:
CEOs and Board Members navigating AI transformation and emerging tech investments
Executive Search Professionals seeking competitive advantage in tech leadership placements
CHROs and Talent Leaders responsible for building next-generation leadership capabilities
Current Executives looking to position themselves for translator leadership roles
In the coming weeks, we’ll explore the three distinct translator archetypes, why they command premium compensation, assessment strategies that go beyond technical interviews, and how to build these capabilities within your current team. We’ll also examine why diverse, non-traditional backgrounds often produce the strongest translator executives and how executive search is evolving to identify these hybrid leaders.
Is your organization prepared for the AI translation gap? Understanding the specific translator profile your company needs represents the first step toward building effective AI transformation leadership.
The author, Ken Persel, is founder of Persel Group, an executive search firm specializing in AI and technology leadership. This analysis combines independent research with professional insights to examine and closing the leadership gap facing AI transformation initiatives.