Navigating the Paradox of AI Enthusiasm and Upskilling Inaction: Building Workforce Capability in the Era of Digital Transformation

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Abstract: Organizations face a striking disconnect between their enthusiasm for artificial intelligence (AI) and their investment in preparing employees to leverage it effectively. While 63% of organizations anticipate high impact from AI-enabled predictive analytics, only 2% have implemented these capabilities, and AI-specific upskilling efforts have declined year-over-year despite accelerating adoption. This article examines the organizational and human consequences of this readiness gap, drawing on survey data from 1,626 HR professionals and organizational research. The analysis reveals that organizations effective at technology enablement demonstrate 1.8 times higher innovation performance, yet only 38% excel at adoption practices. Evidence-based responses include strategic HR-IT collaboration frameworks, learning-in-the-flow-of-work interventions, targeted capability-building programs, distributed leadership accountability, and formal AI governance structures. Long-term organizational resilience requires embedding continuous learning cultures, developing technology-fluent leadership pipelines, and establishing human-centric AI implementation principles. Organizations that align AI strategy with workforce development transform technology enthusiasm into sustainable competitive advantage while those that neglect the human dimension of digital transformation risk failed implementations, diminished returns, and persistent capability gaps.

The promise of artificial intelligence has captured organizational imagination across industries. Executives envision predictive analytics transforming decision-making, automation streamlining operations, and machine learning unlocking unprecedented insights from data. Yet beneath this technological optimism lies a troubling reality: organizations are implementing AI faster than they are preparing their people to use it effectively.

Recent data reveals a stark capability gap. While two-thirds of organizations report implementing AI beyond exploratory phases, only 14% have established formal AI strategies (McLean & Company, 2025). More concerning, investments in AI-specific workforce upskilling declined from 42% to 36% between 2025 and 2026, even as AI adoption accelerated (McLean & Company, 2025). This disconnect between technological deployment and human capability development represents more than an implementation oversight—it signals a fundamental misalignment that threatens to undermine the very benefits organizations seek from their AI investments.

The stakes extend beyond failed projects. When MIT researchers examined custom AI implementations, they found that only 5% reach full deployment (Challapally et al., 2025). The primary barriers? Not technical limitations, but human factors: change resistance, skills gaps, unclear roles, and misaligned incentives. Organizations that neglect the people dimension of digital transformation risk not only wasted technology investments but also employee disengagement, productivity disruptions, and competitive disadvantage.

This article examines why organizations struggle to match AI enthusiasm with workforce development, explores the organizational and individual consequences of this readiness gap, and provides evidence-based interventions to build sustainable AI capability. As innovation surges to become HR's second-highest priority—jumping from tenth place in just one year—the imperative for human-centric technology implementation has never been more urgent (McLean & Company, 2025).

The Digital Transformation Landscape

Defining AI Maturity and Organizational Readiness

AI maturity represents an organization's capability to leverage artificial intelligence technologies strategically and effectively. Info-Tech Research Group's AI Maturity Curve delineates five progressive stages: Exploration (initial awareness with no implementation experience), Incorporation (employee skill development and piloting), Proliferation (multi-departmental adoption with emerging governance), Optimization (automated processes with comprehensive controls), and Transformation (AI embedded in organizational culture with proactive risk management) (Wong & Sezen, 2025).

The distribution of organizations across these stages reveals significant progress yet persistent gaps. Between 2025 and 2026, organizations in the Exploration phase declined from 29% to 22%, while those in Proliferation increased from 34% to 39%, and Optimization surged from 12% to 22% (McLean & Company, 2025). This acceleration signals that organizations are moving beyond tentative experimentation toward more systematic deployment.

However, maturity stage alone inadequately captures readiness. Organizations may advance technologically while lagging in workforce capability, creating a dangerous misalignment. True organizational readiness requires parallel development of both technical infrastructure and human capacity—what researchers term "sociotechnical systems thinking" (Bostrom & Heinen, 1977). When technical capabilities outpace human readiness, organizations experience implementation failures, change resistance, and unrealized value.

State of Practice: Current AI Adoption Patterns

AI adoption has accelerated dramatically. Sixty-eight percent of organizations now implement AI beyond exploratory phases (McLean & Company, 2025). Yet this implementation velocity has outstripped strategic planning: while the percentage of organizations with formal AI strategies nearly doubled from 7% to 14% between 2025 and 2026, this growth remains disproportionately low relative to the 68% actively deploying AI technologies (McLean & Company, 2025).

This strategy-implementation gap manifests in specific capability areas. Only 2% of organizations currently use AI for predictive analytics in HR functions, despite 63% anticipating high organizational impact from such applications (McLean & Company, 2025). Similarly, while 54% of organizations expect high impact from AI-specific workforce upskilling, implementation rates remain negligible and planning rates declined year-over-year (McLean & Company, 2025).

Skills-based hiring practices offer a contrasting pattern. Twenty percent of organizations have implemented skills-based hiring approaches, making it the most adopted emerging practice for two consecutive years, with an additional 49% currently implementing or planning adoption within 18 months (McLean & Company, 2025). This success demonstrates that when organizations commit strategically to capability transformation, meaningful progress becomes achievable.

The disparity between AI technology adoption and workforce development investments creates organizational vulnerability. Organizations essentially deploy sophisticated tools without ensuring employees possess the competencies to leverage them effectively, akin to distributing advanced equipment without training.

Organizational and Individual Consequences of the AI Readiness Gap

Organizational Performance Impacts

The failure to align AI implementation with workforce development produces measurable organizational consequences. Organizations where HR excels at technology enablement demonstrate 1.8 times higher performance in innovation compared to those with weak enablement capabilities (McLean & Company, 2025). This correlation underscores that technology value realization depends fundamentally on human capability to apply it effectively.

Failed AI implementations carry substantial financial costs. The 95% failure rate for custom AI projects reaching full deployment represents not only sunk development costs but also opportunity costs from unrealized productivity gains, competitive advantages foregone, and organizational attention diverted from other strategic priorities (Challapally et al., 2025). When organizations in the Proliferation or Optimization maturity stages fail to invest in corresponding workforce capabilities, they risk regression to earlier stages as implementations stall.

Change fatigue emerges as an additional performance barrier. Organizations experiencing rapid AI-driven transformation without adequate change management support report that change fatigue negatively impacts employee effectiveness (McLean & Company, 2025). When leaders make decisions aligned with organizational values, employees demonstrate 1.5 times greater resilience against change fatigue (McLean & Company, 2025). However, the pace of AI change often outstrips leaders' capacity to maintain this alignment, particularly when leaders themselves lack AI fluency.

Innovation capacity suffers when AI readiness gaps persist. Organizations with leaders highly effective at talent development demonstrate 2.1 times higher innovation performance (McLean & Company, 2025). Yet developing talent for AI-enabled innovation requires leaders to possess sufficient AI literacy to identify relevant learning opportunities, coach employees on AI application, and support AI-related development goals—capabilities many leaders currently lack.

Strategic execution becomes compromised. Only 50% of organizations report that HR partners in planning and executing strategy, a figure that has plateaued despite HR's expanding strategic importance (McLean & Company, 2025). Without strong HR involvement in AI strategy development, organizations risk technology-first approaches that neglect workforce implications until implementation problems emerge.

Individual Wellbeing and Employee Experience Impacts

The individual-level consequences of inadequate AI readiness extend beyond skills gaps to fundamental wellbeing concerns. Employees facing AI implementation without sufficient preparation experience heightened uncertainty about job security, role evolution, and their ability to remain valuable contributors. Research on technology-induced change consistently identifies perceived threat and uncertainty as primary drivers of resistance and stress (Laumer et al., 2016).

Change fatigue at the individual level manifests as emotional exhaustion, cynicism toward organizational initiatives, and reduced commitment (Bernerth et al., 2011). When organizations fail to adequately support employees through AI transitions, only 12% of respondents report not experiencing change fatigue (McLean & Company, 2025). This widespread fatigue directly undermines the employee experience, with organizations effective at managing change demonstrating 1.8 times higher performance in providing great employee experiences (McLean & Company, 2025).

Competency threats prove particularly damaging to employee wellbeing. When employees perceive their current skills becoming obsolete without clear pathways to develop AI-relevant capabilities, they experience diminished self-efficacy and professional identity threats (Lyons, 2020). Organizations implementing AI without parallel upskilling investments essentially communicate to employees that their development is not an organizational priority—a message fundamentally incompatible with employee engagement and retention.

Trust erosion represents another individual consequence. When leaders implement AI-driven changes without adequate explanation or employee involvement, employees question decision-making rationale. Only 53% of employees report understanding the rationale behind senior leader decisions (McLean & Company, 2024), and AI implementations without transparent communication exacerbate this clarity gap.

Performance anxiety increases when employees must use AI tools without adequate training. The pressure to maintain productivity while learning new technologies in real-time creates stress that diminishes both wellbeing and actual performance. Organizations reporting highly effective leaders at change management demonstrate significantly lower change fatigue, illustrating how leadership capability directly protects employee wellbeing during technological transitions (McLean & Company, 2025).

Evidence-Based Organizational Responses

Table 1: Case Studies of AI Readiness and Implementation Strategies

Organization Name	Key Initiative or Program	AI Maturity Focus Area	Collaboration Type	Primary Strategy Implemented	Outcome or Performance Impact	Human-Centric Principle Applied
Meritage Homes	AI learning hub within communication platform	Proliferation / Adoption	Manager-led / Self-service	Learning-in-the-flow-of-work	Increased AI feature adoption by 60% with 90% less instructional time than workshops	Reducing change fatigue and performance anxiety through microlearning
Accumin	AI skills academy (Bronze, Silver, Gold certifications)	Optimization	Enterprise-wide / Structured	Targeted Capability Building Programs	70% of employees achieved certification; productivity improvements exceeded projections by 40%	Clear pathways for internal mobility and skill recognition
Royal Canadian Mint	Leadership competency model integration	Optimization / Transformation	Leadership-driven	Performance management integration	Leader participation in AI training increased from 40% to 95% within two quarters	Leadership modeling and accountability for technology fluency
AGC Biologics	Integrated project teams for talent analytics	Optimization / Proliferation	HR-IT Collaboration	HR-IT Collaboration Frameworks	Reduced implementation time by approximately 40%	Human-in-the-loop (combining talent drivers with data infrastructure)
The Citco Group	AI rapid-prototyping program (30-day sprints)	Transformation / Proliferation	Cross-functional Innovation	Targeted Capability Building Programs	Generated 47 viable AI applications in the first year	Empowering employees to solve real-world organizational challenges
Centennial College	LMS transformation joint HR-IT working group	Incorporation / Piloting	HR-IT Collaboration	HR-IT Collaboration Frameworks	Prevented costly misalignments between system capabilities and faculty development requirements	Inclusive design and involvement of faculty in technological selection
Pigment	Embedded learning prompts in analytics tools	Optimization	User-centric / Automated	Learning-in-the-flow-of-work	Significantly reduced user confusion and support ticket volume; accelerated employee confidence	Transparency in AI algorithm logic and outputs
Healthcare Outcomes Performance Company (HOPCo)	AI learning buddies	Incorporation / Proliferation	Peer Learning / Cross-departmental	Peer learning networks	Accelerated capability building and strengthened cross-departmental relationships	Employee involvement and social support during transition
Wacker Chemical Corporation	AI lunch and learns / Employee advisory panels	Transformation	Peer-led / Advisory	Continuous Learning Culture	Surfaced creative uses leading to measurable cost savings scaled enterprise-wide	Inclusive design through employee advisory panels
Anchor Point Management Group	10% dedicated AI learning and experimentation time	Transformation	Individual / Structural	Resource allocation frameworks	Adoption rates significantly exceeded industry benchmarks; generated multiple process improvements	Psychological safety for experimentation by providing protected time
CMS Info Systems	AI Innovation Fund	Proliferation	Entrepreneurial / Competitive	Financial and Organizational Support Systems	Generated significant operational improvements and identified high-potential talent	Rewarding innovation and providing resources for experimentation
MetroPlusHealth	Temporary performance goal rebalancing	Incorporation / Proliferation	Leadership-supported	Workload adjustment / Support systems	Enabled higher AI adoption rates without increasing employee stress or burnout	Proactive displacement and stress mitigation during transformation

Strategic HR-IT Collaboration Frameworks

Effective AI transformation requires purposeful, structured partnership between HR and IT functions. Organizations where HR and IT collaborate effectively demonstrate 2.0 times greater likelihood of having formal AI strategies and 1.8 times higher innovation performance (McLean & Company, 2025). Yet only 55% of HR organizations report highly effective IT collaboration (McLean & Company, 2025), indicating substantial opportunity for improvement.

Collaborative governance structures provide the foundation for effective HR-IT partnership. Research on digital transformation suggests that successful organizations establish joint steering committees with shared accountability for technology implementation outcomes (Westerman et al., 2014). These structures ensure technology decisions incorporate both technical feasibility (IT's expertise) and human impact considerations (HR's expertise) from initial planning through deployment and sustainment.

Several organizations exemplify effective HR-IT collaboration in AI implementation:

Centennial College established a joint HR-IT working group when planning their learning management system transformation. The partnership ensured the selected platform aligned with pedagogical needs while remaining technically supportable. Miriam Ibrahim, Associate Vice President of Talent & HR Business Partnering, noted that involving HR early prevented costly misalignments between system capabilities and faculty development requirements that would have emerged with a purely IT-led approach.

AGC Biologics created integrated project teams for their talent analytics initiative. Greg Shelton, EVP People & Culture and General Counsel, emphasized that combining HR's understanding of talent drivers with IT's data infrastructure expertise accelerated their ability to generate actionable workforce insights. The collaboration reduced implementation time by approximately 40% compared to sequential handoffs between functions.

Bruce Power instituted quarterly strategic planning sessions between HR and IT leadership to align technology roadmaps with workforce priorities. Karen Smith, SVP & Chief Human Resources Officer, explained that this cadence enables proactive identification of AI opportunities that serve both operational efficiency (IT's typical focus) and employee development (HR's priority), creating dual-benefit initiatives that secure stronger executive support.

Evidence-based practices for strengthening HR-IT collaboration include:

Shared metrics and objectives

• Establish joint KPIs for AI initiatives that measure both technical performance and human outcomes

• Create accountability for technology adoption rates, user satisfaction, and capability development

• Tie executive compensation to collaboration effectiveness metrics

Regular partnership rituals

• Institute standing meetings between HR and IT leaders to review technology pipeline and workforce implications

• Conduct joint AI opportunity assessments that evaluate both technical feasibility and people readiness

• Establish escalation protocols for rapid resolution of cross-functional barriers

Mutual capability building

• Provide AI literacy training to HR professionals to enhance technical conversations

• Offer human-centric design training to IT professionals to strengthen user empathy

• Create rotation programs where HR and IT staff temporarily work in partner function

Integrated planning processes

• Require HR review and approval for AI implementations affecting workforce

• Include IT representation in talent strategy development

• Conduct joint scenario planning for technology-driven workforce disruptions

Learning-in-the-Flow-of-Work Interventions

Traditional training approaches—pulling employees away from responsibilities for multi-day workshops—prove increasingly inadequate for AI upskilling. Leaders already stretched thin report limited capacity for formal development, with people managers 1.4 times more likely than individual contributors to experience elevated job stress (McLean & Company, 2025). Learning-in-the-flow-of-work offers a sustainable alternative by embedding development into daily activities.

Only 8% of organizations currently implement learning-in-the-flow-of-work approaches, despite 53% anticipating high organizational impact (McLean & Company, 2025). This gap represents significant unrealized potential. Organizations that successfully integrate continuous learning into workflows report both higher capability development and reduced productivity disruption (Bersin, 2019).

Evidence supports multiple effective approaches:

Microlearning modules

• Develop 3-5 minute AI skill videos accessible within productivity tools

• Create searchable libraries of AI use case examples employees can reference when encountering similar situations

• Design mobile-accessible content employees can consume during transition periods

AI-powered learning assistants

• Implement chatbots within collaboration platforms that provide just-in-time guidance on AI tool usage

• Deploy contextual help systems that offer suggestions when employees perform AI-relevant tasks

• Use recommendation engines to surface relevant learning content based on employee activities

Embedded coaching moments

• Train managers to identify natural coaching opportunities when employees use AI tools

• Provide leaders with conversation guides for debriefing AI application experiences

• Establish "learning sprints" where teams briefly reflect on AI usage patterns

Peer learning networks

• Facilitate communities of practice where employees share AI application insights

• Create internal AI champion programs recognizing employees who develop innovative applications

• Establish cross-functional AI user groups that accelerate knowledge transfer

Meritage Homes implemented an AI learning hub within their communication platform where employees access brief tutorials when adopting new AI-enabled project management features. Javier Feliciano, Executive Vice President & Chief People Officer, noted this approach increased AI feature adoption by 60% compared to prior training approaches while consuming less than 10% of the instructional time required for traditional workshops.

Pigment embedded learning prompts directly into their AI-enhanced analytics tools. When users perform certain actions, brief tips appear explaining how AI algorithms generate specific outputs. Missy Strong, Senior Lead for People Experience, reported this contextual learning significantly reduced user confusion and support ticket volume while accelerating employee confidence in using advanced AI features.

Healthcare Outcomes Performance Company (HOPCo) established "AI learning buddies" where early adopters partnered with colleagues less experienced in specific AI applications. Ryan Martin, Chief People Officer, emphasized that this peer approach not only accelerated capability building but also strengthened cross-departmental relationships and created organic innovation as employees shared creative applications they discovered.

Targeted Capability Building Programs

While learning-in-the-flow-of-work provides continuous reinforcement, foundational AI literacy requires more structured development. Effective programs balance accessibility with rigor, ensuring broad workforce participation while building genuine capability rather than superficial familiarity.

Evidence-based program components include:

Differentiated learning paths

• Executive track: Focus on AI governance, strategic opportunity identification, and ethical considerations

• Manager track: Emphasize AI-enabled talent development, change leadership, and team productivity optimization

• Professional track: Build hands-on skills in AI tool usage, prompt engineering, and outcome evaluation

• Technical track: Develop deeper capabilities in AI customization, integration, and performance monitoring

Practical application requirements

• Design programs requiring participants to complete real-world AI projects addressing actual organizational challenges

• Establish "AI innovation challenges" where teams compete to develop most valuable AI applications

• Create showcase events where employees demonstrate AI solutions to leadership

Certification and recognition systems

• Implement tiered AI proficiency certifications employees can pursue

• Recognize AI capability development in performance reviews and advancement decisions

• Establish AI expertise as a criteria for high-potential identification

Measurement and accountability

• Assess learning effectiveness through pre/post competency assessments

• Track AI adoption rates and productivity impacts following training

• Monitor employee confidence and self-efficacy measures

Accumin developed a comprehensive AI skills academy offering bronze, silver, and gold certification levels. Teresa Coelho, Global Chief People & Sustainability Officer, explained that creating visible proficiency levels motivated employees to invest in development while providing managers concrete criteria for delegation decisions. Within one year, 70% of employees achieved at least bronze certification, and AI-enabled productivity improvements exceeded initial projections by 40%.

The Citco Group launched an AI rapid-prototyping program where cross-functional teams received intensive 3-day training followed by 30-day supported innovation sprints to develop AI solutions. Chris Collins, Head of Human Resources, noted this approach generated 47 viable AI applications in the first year, many addressing pain points that pure IT-led initiatives had overlooked. The program's success stemmed from combining concentrated skill building with immediate application opportunities.

Wacker Chemical Corporation instituted monthly "AI lunch and learns" where employees shared AI applications they discovered or developed. Ato Taylor, VP of Human Resources, emphasized this peer-led approach created continuous learning culture while surfacing creative uses leadership had not anticipated. Several lunch-and-learn innovations subsequently scaled enterprise-wide, delivering measurable cost savings.

Operating Model and Governance Mechanisms

Sustainable AI capability requires more than training—it demands organizational structures and governance that reinforce continuous development. Formal mechanisms ensure AI readiness remains a strategic priority rather than competing with other demands.

Effective governance approaches include:

AI steering committees

• Establish executive-level oversight bodies reviewing AI strategy, investments, and workforce implications

• Ensure HR representation with equal standing to IT and business function leaders

• Conduct quarterly workforce readiness assessments examining skill gaps and development progress

Change management protocols

• Require human impact assessments for all AI implementations exceeding specified thresholds

• Mandate stakeholder engagement and communication plans addressing employee concerns

• Establish escalation procedures for addressing adoption barriers

Performance management integration

• Include AI capability development as component of individual performance objectives

• Evaluate leaders on their effectiveness enabling team AI adoption

• Reward innovation in AI application and knowledge sharing

Resource allocation frameworks

• Establish dedicated budgets for AI upskilling separate from general training funds

• Create innovation time allowances for employees to experiment with AI applications

• Provide computing resources and tool access to support hands-on learning

Ochsner Health established an AI Adoption Council including HR, IT, clinical, and operational leaders with explicit accountability for workforce readiness. Kelly Murphy Almerico, Assistant Vice President of Employee Experience, noted the council's quarterly capability assessments enabled proactive intervention when skill gaps emerged, preventing implementation delays and ensuring clinical staff felt supported rather than overwhelmed by new AI diagnostic tools.

Royal Canadian Mint incorporated AI fluency into leadership competency models and performance expectations. Michel Boucher, Vice President of Human Resources, explained that formally recognizing AI capability as a leadership requirement transformed AI development from optional to essential. Leader participation in AI training increased from 40% to 95% within two quarters of incorporating AI into performance criteria.

Anchor Point Management Group dedicated 10% of each employee's work time to AI learning and experimentation. David Hawthorne, Chief People Officer, emphasized this structural commitment signaled organizational seriousness about capability building while providing practical space for hands-on development. Employee AI adoption rates significantly exceeded industry benchmarks, and the experimentation time generated multiple process improvements that more than offset the productivity time invested.

Financial and Organizational Support Systems

Organizations effective at managing change allocate resources demonstrating that employee development represents strategic investment rather than discretionary expense. When HR demonstrates high effectiveness at measuring program ROI, organizations show 1.9 times greater likelihood of being high-performing at leadership development and 1.9 times higher innovation performance (McLean & Company, 2025).

Evidence-based support mechanisms include:

Dedicated development budgets

• Establish per-employee AI learning budgets separate from general training allocations

• Create innovation funds employees can access to pilot AI applications

• Provide technology subscriptions and tool access supporting experimentation

Time allocation policies

• Institute minimum development time requirements (e.g., 5% of work time)

• Establish "learning Fridays" or similar protected development periods

• Reduce other requirements to create genuine capacity for skill building

Career pathway clarity

• Define AI-fluent roles and progression opportunities

• Articulate how AI capabilities enhance internal mobility

• Provide visibility into AI-related advancement opportunities

Recognition and incentive systems

• Offer spot awards for innovative AI applications

• Include AI capability development in promotion criteria

• Celebrate and share AI success stories organization-wide

CMS Info Systems created an "AI Innovation Fund" where employees could apply for resources to develop AI solutions. Sanjay Singh, CHRO, noted the fund both resourced development and identified employees with entrepreneurial mindsets suited for emerging roles. Several funded projects generated significant operational improvements, and the application process itself became a development experience as employees articulated business cases and impact projections.

ESAB instituted quarterly AI showcases where teams demonstrated AI applications they developed, with executive leadership providing both recognition and feedback. Michele Campion, CHRO, emphasized these events created positive motivation while enabling knowledge transfer across the organization. Several showcased innovations subsequently scaled enterprise-wide, and employee engagement scores related to development opportunities increased significantly.

MetroPlusHealth adjusted performance goals during AI implementation periods, explicitly reducing other deliverable expectations to create genuine capacity for learning. Michael Kushner, Chief People Officer, explained that without this adjustment, AI development would inevitably lose priority to seemingly more urgent operational demands. The temporary workload rebalancing enabled higher AI adoption rates without increasing employee stress or burnout.

Building Long-Term AI Fluency and Organizational Learning Capability

Continuous Learning Culture as Strategic Foundation

Sustainable AI capability requires more than individual training events—it demands organizational cultures where learning constitutes an ongoing expectation and practice. Organizations with continuous learning cultures demonstrate significantly higher leadership effectiveness across all foundational people management areas, including talent development (34% vs. 7%), change management (27% vs. 6%), and employee engagement (47% vs. 16%) compared to organizations lacking such cultures (McLean & Company, 2025).

Building continuous learning cultures involves several interconnected elements:

Senior leader commitment and modeling

• Executives publicly discuss their own AI learning journeys, normalizing that expertise develops over time

• Senior leaders allocate visible time to AI skill building, demonstrating learning priority

• Leadership team establishes AI fluency as organizational value embedded in decision-making

Psychological safety for experimentation

• Organizations explicitly communicate that AI learning involves trial, error, and refinement

• Managers create space for employees to test AI applications without penalty for failed experiments

• Celebration of "intelligent failures" that generate useful insights even when specific applications prove unviable

Systems and structure reinforcement

• Performance management criteria include learning agility and AI capability development

• Succession planning processes evaluate AI fluency when identifying high-potential talent

• Compensation decisions consider learning investment and capability growth

Accessible learning infrastructure

• Organizations provide diverse learning modalities (microlearning, workshops, peer learning, coaching) accommodating different preferences

• Technology platforms enable easy discovery and access to relevant content

• Learning analytics help employees identify skill gaps and relevant development resources

HomeFirst embedded "learning as a value" explicitly into their organizational framework, with CEO René Ramirez consistently emphasizing capability building in all-hands communications. This executive commitment cascaded throughout the organization, with managers evaluated partly on team development outcomes. The culture shift resulted in significantly higher voluntary participation in AI training compared to mandate-based approaches at similar organizations.

General Canadian Mint created "learning champions" distributed across departments who modeled continuous development and helped colleagues navigate learning resources. Michel Boucher emphasized this distributed leadership approach prevented learning from being perceived as solely HR's responsibility, instead embedding it throughout the organization's fabric.

Bruce Power instituted quarterly "learning dialogues" where employees discussed development activities and applications with managers, separate from performance reviews. Karen Smith noted this dedicated conversation space reinforced that learning merited focused attention rather than being rushed through at the end of performance discussions, significantly increasing the depth and quality of development planning.

Leadership Pipeline Development for Technology Fluency

Today's leaders must master the human dimensions of AI-enabled change, yet only 36% of organizations report leaders highly effective at change management (McLean & Company, 2025). As AI reshapes roles and potentially flattens organizational structures—with 41% of organizations planning to reimagine roles through AI-driven automation (McLean & Company, 2025)—leadership pipeline development becomes critical.

Effective approaches include:

Early-career AI leadership exposure

• Incorporate AI fluency into emerging leader programs

• Provide firsthand experience leading small-scale AI initiatives

• Develop change management capabilities in context of technological transformation

Manager-of-managers programs

• Build capability to coach managers on AI-enabled team development

• Strengthen strategic thinking about AI's workforce implications

• Develop skills in navigating AI-related ethical dilemmas

Executive leadership development

• Focus on AI governance, strategic opportunity identification, and organizational transformation

• Build capability to lead enterprise-wide AI initiatives

• Strengthen ability to balance innovation with workforce stability

Delegation and distributed leadership

• Train leaders to effectively delegate AI-related responsibilities, building team capabilities

• Develop skills in identifying high-value delegation opportunities that strengthen both operational delivery and employee development

• Only 30% of organizations report leaders effectively delegating tasks, yet effective delegation correlates with 1.5 times higher likelihood of continuous learning culture (McLean & Company, 2025)

Meritage Homes redesigned their leadership development curriculum to integrate AI case studies and simulations throughout all modules rather than treating AI as separate topic. Javier Feliciano explained this integration ensured leaders viewed AI fluency as core leadership competency rather than optional technical knowledge, fundamentally shifting how emerging leaders approached technology in their roles.

AGC Biologics established an "AI Leadership Circle" where senior leaders collaboratively explored AI governance challenges through structured case discussions. Greg Shelton noted this peer learning approach accelerated executive capability building while creating alignment on organizational AI principles, significantly easing subsequent policy development and decision-making.

Centennial College created AI project leadership opportunities for high-potential employees, providing coaching support and allowing controlled risk-taking. Miriam Ibrahim emphasized these developmental assignments built both AI fluency and change leadership capabilities simultaneously while identifying future leaders capable of driving technological transformation.

Human-Centric AI Implementation Principles

As organizations accelerate AI adoption, maintaining human-centric approaches prevents technology from undermining the employee experience it should enhance. Organizations where leaders make decisions aligned with values demonstrate 2.4 times higher workforce productivity and 2.0 times greater strategic goal achievement (McLean & Company, 2025).

Core principles include:

Transparency in AI decision-making

• Communicate how AI systems inform decisions affecting employees

• Provide visibility into AI algorithm logic and limitations

• Establish processes for employees to question or appeal AI-influenced decisions

Meaningful human oversight

• Maintain human decision-making authority for high-stakes talent decisions

• Ensure AI augments rather than replaces human judgment

• Design "human-in-the-loop" processes for sensitive applications

Privacy and data ethics

• Implement robust data governance protecting employee information

• Obtain informed consent for AI applications using employee data

• Conduct regular audits of AI systems for bias and fairness

Inclusive design and accessibility

• Involve diverse employee groups in AI tool design and testing

• Ensure AI applications accommodate different abilities and preferences

• Provide alternative processes for employees uncomfortable with specific AI applications

Proactive displacement mitigation

• Conduct workforce planning identifying roles most vulnerable to AI disruption

• Provide early reskilling opportunities for employees in at-risk positions

• Establish transition support for displaced workers

Ochsner Health implemented comprehensive transparency protocols for AI-enabled clinical decision support, ensuring all users understood how algorithms generated recommendations and what evidence informed them. Kelly Murphy Almerico noted this transparency reduced resistance while strengthening appropriate reliance on AI tools, as clinicians understood both capabilities and limitations.

Wacker Chemical Corporation established employee advisory panels that reviewed proposed AI implementations, providing feedback on human impact and design improvements. Ato Taylor emphasized this inclusive approach identified potential problems early while building employee trust in organizational AI governance, significantly reducing resistance during deployment.

Pigment conducted algorithmic audits of their AI-enabled talent systems, examining outcomes across demographic groups to identify potential bias. Missy Strong explained these proactive assessments demonstrated organizational commitment to fairness while identifying and correcting issues before they affected employee experiences or created legal liability.

Conclusion

The gap between organizational AI enthusiasm and workforce upskilling investment represents one of the most significant strategic risks facing organizations in 2026. While 68% of organizations implement AI beyond exploratory phases, workforce development efforts lag dramatically, with AI-specific upskilling declining year-over-year despite accelerating adoption (McLean & Company, 2025). This misalignment threatens to transform AI's promise into disappointment, as the 95% failure rate for custom AI projects reaching full deployment demonstrates (Challapally et al., 2025).

The evidence presented reveals clear paths forward. Organizations must strengthen HR-IT collaboration, with data showing that effective partnerships yield 2.0 times greater likelihood of formal AI strategies and 1.8 times higher innovation performance (McLean & Company, 2025). Learning-in-the-flow-of-work approaches offer sustainable development models compatible with constrained leader capacity. Targeted capability building programs, when designed with differentiated paths and practical applications, accelerate competency development. Operating model changes and governance mechanisms ensure AI readiness remains strategic priority rather than optional initiative. Financial and organizational support systems demonstrate genuine commitment to capability investment.

Long-term success requires embedding continuous learning into organizational culture, developing leadership pipelines capable of navigating AI-driven transformation, and maintaining human-centric principles as technology adoption accelerates. Organizations effective at managing change demonstrate 2.3 times higher innovation performance (McLean & Company, 2025), illustrating how capability-building investments compound over time.

The imperative is clear: organizations must match their AI implementation pace with equivalent workforce development intensity. Technology alone delivers no competitive advantage; only when paired with human capability to leverage it effectively does AI fulfill its transformative potential. Organizations that recognize this fundamental truth and act accordingly will differentiate themselves significantly, while those that continue deploying technology faster than they develop people will join the 95% whose AI projects fail to reach meaningful impact.

The choice facing organizational leaders is not whether to invest in AI capability building, but whether to do so proactively and strategically, or reactively after implementation failures accumulate. The evidence demonstrates conclusively that the former approach delivers superior outcomes across innovation, employee experience, strategic execution, and financial performance. In an era where innovation has surged to become HR's second-highest priority, the organizations that build genuine AI fluency throughout their workforce will be the ones positioned to lead.

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Jonathan H. Westover, PhD is Chief Academic & Learning Officer (HCI Academy); Associate Dean and Director of HR Programs (WGU); Professor, Organizational Leadership (UVU); OD/HR/Leadership Consultant (Human Capital Innovations). Read Jonathan Westover's executive profile here.

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Suggested Citation: Westover, J. H. (2026). Navigating the Paradox of AI Enthusiasm and Upskilling Inaction: Building Workforce Capability in the Era of Digital Transformation. Human Capital Leadership Review, 30(2). doi.org/10.70175/hclreview.2020.30.2.5