Nowhere to Hide: Lessons from 2 Years of Hype and Hope

Despite the initial excitement, the reality of artificial intelligence implementation presents a sobering picture. AI projects are still failing at a rate of more than 80%, double that of regular IT projects without AI. Productivity gains in organizations remain unproven, and the economic impact of AI is still largely invisible. Companies grapple with uncertainty about future job impacts (whether to hire, fire, or retrain workers) continue to distrust leadership—especially around AI. 

Adding to this complex landscape, Wall Street is cooling on AI. Jim Covello, head of stock research at Goldman Sachs, has emerged as Wall Street’s leading AI skeptic. He’s challenging the notion that businesses will see sufficient returns on an estimated $1 trillion in AI spending. Covello’s skepticism isn’t unfounded; it stems from his experience with past tech booms and busts, as well as his firsthand evaluation of AI services at Goldman Sachs. He found many AI services to be costly, cumbersome, and not “smart enough to make employees smarter.” 

Yet one thing remains certain: AI is coming, and like electricity, its societal impact will be enormous and inevitable. As Jamie Dimon, CEO of JPMorgan Chase, stated in his letter to shareholders earlier this year, AI will be “possibly as transformational as some of the major technological inventions of the past several hundred years: Think the printing press, the steam engine, electricity, computing, and the Internet ... Over time, we anticipate that our use of AI has the potential to augment virtually every job, as well as impact our workforce composition.”

The question is: Are we approaching AI in a way that will deliver transformative solutions to real problems?   

The Slow March of Progress

At a recent conference I spoke at, an attendee observed, “It seems like AI is slowing down because CHROs are talking about it less now than last year.” But the truth is, successful AI implementation is just a challenging process that takes time. Experimentation is key, but it must focus on the right problems, which take time to identify. Data and tech are essential starting points but are not enough by themselves for real transformation.

AI analyst Daniel Faggella, the founder of Emerj, puts it bluntly: “It genuinely will take an existential crisis to force enterprise leadership to consider AI’s strategic implications and to see it as more than a Band-Aid for individual problems.” In his view, there are two versions of the enterprise AI future we could be walking toward:

1. Transformation: Enterprises overhaul their data infrastructure and strategy to remold core operations, moving toward an AI-enabled vision of what their company will become in the next three to five years.
2. Hobbling: Enterprises ineptly stumble to make even the slightest efforts toward AI transformation. Leadership remains uninspired, and AI adoption stays at the level of “Band-Aid” fixes.

Like it or not, Faggella says, “We’re currently on the fast track for the second vision.”

After talking with dozens of companies and their consultants, I’ve identified a consistent characteristic of AI implementation failures. The core issue is a widespread misunderstanding: Many organizations fail to grasp that success with AI is more than just a technological upgrade—it’s a social, cultural, and behavioral transformation. This blind spot often becomes the stumbling block.

When tech issues do cause failures, deeper analysis often shows that resolving them requires a new data-driven culture. This culture, as expected, relies on people, alignment, and leadership guidance.

This blind spot is likely the fundamental reason why many AI investments aren’t yielding the expected ROI, projects are failing, and companies aren’t fundamentally redesigning work. Many organizations have yet to fully comprehend that AI implementation is, at its core, a human-centric, social, and cultural shift. They continue to operate under an outdated paradigm, unaware that the playing field has changed.

The Human Element Is Key to AI Success

After learning from an Upwork study that 96% of C-suite leaders expect AI to boost productivity, while 77% of employees report it actually decreased productivity and increased their workload, I interviewed Kelly Monahan, the managing director of the Upwork Research Institute who conducted the study, to gain deeper insights into the AI implementation challenge. 

Our podcast episode highlighted critical insights about the human factor in AI adoption. Below are three key points (tune in to the full episode for more):

1. AI implementation is not just a technology problem.

Technology is important, but people are crucial for AI success. Monahan said, “The technology wasn’t the problem. It was our human behavior and the human side.” AI isn’t plug-and-play; it needs human guidance.

Many companies are falling into the trap of providing access to AI tools without proper training or a clear vision. This leads to burnout and underutilization of AI’s potential. As Monahan pointed out, “Forty-seven percent of workers in our study told us that they actually have no clue how to gain more productivity with the use of these new tools.”

This statistic reflects a broader issue in AI adoption: the tendency to focus on the technology itself rather than on how it integrates with human workflows and organizational processes. As Monahan explained, “Many organizations are trying to jump on the AI hype ... but the reality is, all they’re doing is signing up for licenses and giving access to a large language model, with very few trainings and skilling in place.”

2. There’s a need for essential leadership in the age of AI.

Essential leadership, as defined by Monahan, is a human-centered approach that prioritizes personal growth, curiosity, and fulfillment of human needs. It involves leaders transcending their egos and self-interest, cultivating relentless curiosity about the changing world, and creating cultures of excellence by addressing fundamental human needs in the workplace. 

This leadership style emphasizes the importance of aligning a leader’s personal values with their organizational behavior, recognizing that effective leadership in today’s rapidly evolving business environment starts with the leader’s own mindset and extends to how they engage with and develop their teams.

Essential leaders need to move beyond traditional metrics such as hours worked and output, focusing instead on qualitative aspects such as adaptability, creativity, and strategic contributions. As Monahan emphasized, “Workers were asking, ‘I want to be measured, in terms of productivity, [by] my adaptability to change and my contributions to company strategy, creativity, and innovation.”

This shift in focus requires a fundamental change in how we think about productivity and value creation in the workplace. It’s no longer enough to measure success by the number of tasks completed or hours logged. Instead, leaders need to cultivate an environment that encourages experimentation, learning, and strategic thinking.

3. Rethink productivity and ROI in the context of AI.

Traditional productivity metrics are no longer sufficient. The focus should shift toward measuring adaptability, innovation, and strategic contributions. Monahan emphasized this point: “Many organizations today are still thinking about productivity in terms of hours worked, output ... but we need to get better at qualitative metrics like creativity, innovation, and adaptability.”

One of the key benefits of AI is its ability to automate routine tasks, freeing up human workers for more strategic, creative work. However, this benefit can only be realized if organizations actively encourage and enable this shift. As Monahan noted, “When AI saves you time, the real question is: What are you going to do with that extra time? The smart leaders are using it to unlock higher-level thinking and creativity.”

These insights from Monahan underscore the critical role of human factors in successful AI implementation. Organizations that prioritize these elements—from comprehensive training programs to leadership development and redefined productivity metrics—will be better positioned to harness the full potential of AI technology.

Looking Ahead: Nowhere to Hide

By early next year, I predict the conversation will shift again. As organizations move from early experimentation to the nitty-gritty of implementation, they’ll begin to face this inconvenient truth: People are as important as technology in making AI transformations successful.

We’ll likely see more articles about AI and culture because we’ll realize that culture still eats strategy for breakfast—even AI strategy. Old ideas about change management will have to evolve, as many of the best GenAI use cases will bubble up from the bottom. Top-down “command-and-control” change will miss the best GenAI use cases unless it’s agile, iterative, and empowers “citizen” process redesign by employees.

As AI takes on more lower-level tasks, leaders and managers will need to step up their game. The true job will be to unlock team members’ human potential, not just check off weekly summaries now done by AI. Anyone who wants to stay relevant will need to focus on strategic value-add and become experts in areas that require judgment, discernment, and taste—not just knowledge and data.

In conclusion, while I’ve previously highlighted numerous AI success stories, this article underscores a critical insight: Even the most promising AI implementations falter without proper attention to human factors. Organizations that truly excel will be those that not only adopt cutting-edge AI but also embrace how human development must evolve alongside it. By fostering collaboration, enhancing team dynamics, and continuously adapting to AI-driven changes, we can create AI-augmented workplaces that drive productivity, creativity, and personal growth. 

The future of AI in the workplace isn’t just about smarter machines—it’s about cultivating smarter, more adaptable, and more empowered humans across entire organizations. This holistic approach, combining technological advancement with human-centric strategies, will ultimately separate the leaders from the laggards in the AI-driven future.