AI is redefining the operational blueprint for enterprises worldwide, but the pace at which technology evolves doesn’t always match the readiness of companies eager to implement it. This disconnect is the core concern spotlighted in Kyndryl’s latest “People Readiness 2025” research, which pulls back the curtain on the stark realities facing organizations as they integrate AI technologies like Microsoft Copilot and intelligent agents into their workflows. Drawing from the perspectives of 1,100 C-suite business leaders, Kyndryl’s report does not sugarcoat: a wide gap exists between AI’s potential and the human and organizational readiness to harness it.
Notably, 71% of surveyed leaders admit their workforce is not yet prepared to successfully leverage AI in the workplace. This figure is more than a number—it’s a signal flare for organizations looking to race ahead with AI initiatives. The reluctance and lack of preparedness extend beyond generic discomfort; it reflects lingering skill gaps, resistance to change, inadequate training, and a still-evolving understanding of what AI means for employee roles and company culture.
These findings are doubly significant given the context of AI’s meteoric progress. In the last few years, the advent of platforms like Microsoft Copilot has promised to revolutionize productivity by weaving AI directly into everyday enterprise tools like Microsoft 365, Dynamics, and Teams. Cloud-driven AI agents offer automation and insight at a scale never before experienced. Yet, as IT leaders are learning, delivering on these promises requires more than flipping the switch on new software.
What accounts for such hesitation in sectors that often lead on digital transformation? The reasons are both operational and cultural:
On the adoption continuum, 60% of organizations are only at the earliest stages—either just embarking on AI initiatives or still struggling with integration. A notable 5% admit their company has yet to engage with AI at all. This broad spectrum of preparedness is further corroborated by outside analyses—ServiceNow’s recent survey, for example, found that overall AI maturity within enterprises actually declined year-over-year, underscoring stalled progress and diffused efforts.
Best practices from across the Microsoft ecosystem reveal that successful deployments almost always take a phased approach:
The real-world impact is tangible: companies charging ahead without a comprehensive readiness plan risk underwhelming AI results, employee retrenchment, and even compliance missteps. Conversely, organizations that focus on incremental wins—scaling pilots, sharing success stories, and building AI literacy—unlock possibilities, gradually weaving new technologies into the fabric of daily work.
If there’s a single takeaway from Kyndryl’s “People Readiness 2025” analysis, it’s that technology alone will never drive sustainable transformation. Success with Microsoft Copilot, AI agents, and broader cloud AI frameworks will follow where organizations invest as heavily in people as they do in technology. Forward-thinking leadership, robust change management, and substantial investments in training are not just “nice-to-haves”—they are non-negotiables for closing the gap between AI’s promise and organizational performance.
Companies that recognize and act on this reality—by embedding AI-readiness into the spotlight of their digital roadmaps—will be best positioned to benefit from the next wave of intelligent business transformation. Those who underestimate the challenges ahead may find that the steep hill Kyndryl identifies only gets higher. The most successful companies in the Copilot and AI agent era will be those that ensure their people are as ready as their technology stack—and make room for ongoing learning and adaptation as the field evolves.
Source: Cloud Wars AI Agent & Copilot Podcast: Kyndryl AI Readiness Report Finds People, Orgs Have a Steep Hill to Climb
Enterprise AI Adoption: Between Promise and Practicality
Notably, 71% of surveyed leaders admit their workforce is not yet prepared to successfully leverage AI in the workplace. This figure is more than a number—it’s a signal flare for organizations looking to race ahead with AI initiatives. The reluctance and lack of preparedness extend beyond generic discomfort; it reflects lingering skill gaps, resistance to change, inadequate training, and a still-evolving understanding of what AI means for employee roles and company culture.These findings are doubly significant given the context of AI’s meteoric progress. In the last few years, the advent of platforms like Microsoft Copilot has promised to revolutionize productivity by weaving AI directly into everyday enterprise tools like Microsoft 365, Dynamics, and Teams. Cloud-driven AI agents offer automation and insight at a scale never before experienced. Yet, as IT leaders are learning, delivering on these promises requires more than flipping the switch on new software.
Sector-Specific Struggles: Healthcare, Telecom, and Beyond
Diving deeper, the survey results expose heightened anxiety in healthcare, where 81% of leaders concede they’re not completely ready for AI adoption. Telecom—a sector viewed as technologically advanced—shows 78% agreeing with this sentiment. Even in banking and finance, typically regarded as more mature in digital transformation, a sobering 64% report they’re not fully prepared.What accounts for such hesitation in sectors that often lead on digital transformation? The reasons are both operational and cultural:
- Skill Shortages: Even “tech-forward” industries lack specialized AI expertise, from model development and data analytics to cybersecurity tailored to AI systems.
- Cultural Resistance: Legacy processes die hard. Employees in regulated environments (like healthcare and finance) are especially wary of disruptive change, in part over worries about compliance and data privacy.
- Regulatory Concerns: The ambiguity of AI-specific regulatory frameworks amplifies uncertainty, slowing adoption in risk-averse industries.
Measuring AI Maturity: The Slope is Steeper Than Expected
What truly stands out in Kyndryl’s data is the distinction between AI hype and operational reality. A telling 45% of CEOs say the majority of their employees are resistant—if not openly hostile—to AI. This resistance is not confined to rank-and-file workers; it cuts across all levels, making organizational change more complicated. Such pushback is particularly perilous for companies investing heavily in AI, as employee buy-in is often a determinant of transformation success.On the adoption continuum, 60% of organizations are only at the earliest stages—either just embarking on AI initiatives or still struggling with integration. A notable 5% admit their company has yet to engage with AI at all. This broad spectrum of preparedness is further corroborated by outside analyses—ServiceNow’s recent survey, for example, found that overall AI maturity within enterprises actually declined year-over-year, underscoring stalled progress and diffused efforts.
Table: AI Readiness Survey Highlights
| Industry | % Not Ready for AI | % Using AI for Customer Products/Services | Resistance to AI |
|---|---|---|---|
| Healthcare | 81% | 22% | High |
| Telecom | 78% | n/a | High |
| Banking/Finance | 64% | n/a | High |
| Insurance | n/a | 35% | Moderate |
| Cross-industry | 71% | 21% | 45% CEO report |
Realistic Opportunities for Copilot, AI Agents, and the Cloud
Despite these sobering statistics, cloud-powered AI agents—exemplified by Microsoft Copilot—remain a bright spot for long-term productivity and innovation. The AI Agent & Copilot Podcast regularly explores customer use cases that illustrate how businesses can gradually integrate advanced AI without overwhelming employees or operations.Best practices from across the Microsoft ecosystem reveal that successful deployments almost always take a phased approach:
- Pilot Programs: Early wins often come when companies focus Copilot pilots on well-scoped business processes—such as automating meeting summaries in Teams or accelerating financial analysis in Excel.
- Iterative Change Management: Organizations report more success when communication, training, and transparent leadership set employee expectations that AI augments (rather than replaces) human judgement.
- Center of Excellence Model: Leading enterprises establish internal groups charged with vetting, scaling, and advocating for Copilot and AI agent adoption. This centralized expertise helps prevent fragmented deployments and ensures best practices propagate throughout the organization.
Notable Strengths: Where AI Is Driving Value
Amid the widespread caution, Kyndryl’s research does highlight green shoots. Insurance is outperforming other sectors in deploying AI for customer-facing products and services, with 35% of surveyed insurance firms leveraging AI in this way—well above the overall cross-industry percent of 21%. This aligns with broader market trends, as insurers look to AI to accelerate claims processing, improve risk scoring, and enhance customer support via intelligent chatbots.- Personalization at Scale: AI agents embedded in cloud platforms can draw on vast data reserves to tailor customer experiences in real time.
- Operational Efficiency: Automation of repetitive and compliance-heavy tasks in insurance and finance is already delivering measurable productivity gains.
- AI in Healthcare: While the sector struggles with readiness, it presents enormous potential—AI copilots can help clinicians summarize medical records, flag drug interactions, and even optimize scheduling workflows. These value drivers, however, are only realized when organizational readiness is addressed.
Potential Risks: Resistance, Regulation, and Resource Gaps
The Kyndryl report doesn’t shy away from risks and shortcomings that could imperil the AI revolution at the enterprise level. The most prominent hazards include:1. Workforce Resistance and Training Deficits
- Cultural Fears: The perception that AI could threaten jobs, or make roles obsolete, feeds employee resistance. Transparent communication about AI’s intended role is often missing.
- Skills Gap: Upskilling is a massive, ongoing task. Microsoft, Google, and AWS all offer extensive AI certification programs, yet companies consistently underestimate the scale and speed required to retrain staff.
2. Data Governance and Security
- Privacy Risks: Especially in regulated industries, uncertainty over how AI systems process, store, and share sensitive information slows down adoption.
- Bias and Explainability: Pressure increases for AI vendors to provide explainable AI (XAI) tools, especially when algorithms influence critical decisions in finance or healthcare.
3. Changing Compliance Requirements
- The Regulatory Wild West: Laws governing AI use are still evolving, especially around data sovereignty, algorithmic transparency, and ethical guidelines. Multinational organizations in particular are forced to navigate a patchwork of regional compliance expectations.
Critical Analysis: Why the Readiness Gap Matters—and How to Bridge It
The disconnect between technology and organizational transformation is not unique to the AI era, but the stakes—and speed—are higher than ever. Unlike past IT revolutions, AI promises exponential gains for first movers but threatens to leave the rest far behind. Kyndryl’s research shows that most enterprises now find themselves facing a “steep hill”—with success depending less on access to the technology and more on the collective ability to adapt corporate culture, reskill workforces, and instill trust in intelligent systems.The real-world impact is tangible: companies charging ahead without a comprehensive readiness plan risk underwhelming AI results, employee retrenchment, and even compliance missteps. Conversely, organizations that focus on incremental wins—scaling pilots, sharing success stories, and building AI literacy—unlock possibilities, gradually weaving new technologies into the fabric of daily work.
Looking Ahead: From Steep Hill to Level Ground
Industry events like the AI Agent & Copilot Summit offer a useful barometer for where business and tech leaders are directing their efforts. Scheduled for March 17-19, 2026, in San Diego, the conference aims to further define the strategies, opportunities, and outcomes surrounding Microsoft Copilot, AI agents, and the cloud’s central role in transformation. As with its 2025 predecessor, the summit will likely serve as both a networking hub and a stage for setting priorities for AI adoption in the year ahead.If there’s a single takeaway from Kyndryl’s “People Readiness 2025” analysis, it’s that technology alone will never drive sustainable transformation. Success with Microsoft Copilot, AI agents, and broader cloud AI frameworks will follow where organizations invest as heavily in people as they do in technology. Forward-thinking leadership, robust change management, and substantial investments in training are not just “nice-to-haves”—they are non-negotiables for closing the gap between AI’s promise and organizational performance.
Conclusion: Seizing the AI Advantage Requires Realism
Kyndryl’s survey ultimately demystifies much of the hype around enterprise AI adoption. Transforming skepticism and hesitation into enthusiasm and innovation is a marathon, not a sprint. For IT leaders, line-of-business managers, and front-line employees alike, the message is clear: AI readiness is not a given, but a discipline developed over time with intentional investment and leadership.Companies that recognize and act on this reality—by embedding AI-readiness into the spotlight of their digital roadmaps—will be best positioned to benefit from the next wave of intelligent business transformation. Those who underestimate the challenges ahead may find that the steep hill Kyndryl identifies only gets higher. The most successful companies in the Copilot and AI agent era will be those that ensure their people are as ready as their technology stack—and make room for ongoing learning and adaptation as the field evolves.
Source: Cloud Wars AI Agent & Copilot Podcast: Kyndryl AI Readiness Report Finds People, Orgs Have a Steep Hill to Climb
