Artificial intelligence has moved faster than any previous enterprise technology. But while investment in AI continues to surge, a growing number of corporate leaders are confronting a paradox: technology adoption is outpacing workforce readiness, and this gap is increasingly cited as the main bottleneck in AI transformation efforts.
Recent global research highlights this divide starkly. According to a 2026 report by Absorb Software, although a majority of organizations have begun integrating AI into learning and development strategies, only 11 % of HR and L&D leaders feel confident in their future skills-building plans, signaling that capability development is not keeping pace with technological adoption. Similar trends appear worldwide: surveys show that fewer than half of workers feel their current workplace is prepared to use AI tools effectively, and a broad skills gap continues to hinder meaningful uptake across industries.
The disconnect between theory and practice has reached a stage where companies now implement AI systems while their employees lack necessary skills and required workforce mentality and essential leadership skills for proper system usage. Katerina Andreeva, who founded The Brained Inc, develops her own unique approach to solving problems through practical methods. She has spent over ten years establishing herself as an expert in AI and data architecture because she has made critical AI decisions at IBM and other organizations. Andreeva, who speaks at Web Summit Lisbon 2025 and other international technology conferences, has provided consulting services to startups and growth-stage companies in music, tourism, fashion, and SaaS industries. She has observed AI success and failure across multiple sectors which enables her to identify consistent patterns that explain results because they do not rely on technological factors. The author demonstrates how AI projects fail by providing her expertise which shows organizations need to change their methods for achieving successful results.
During her time at IBM, Katerina Andreeva worked as a Customer Success Manager and Data & AI Architect, partnering primarily with large insurance organizations, as well as leading companies in telecommunications and other highly regulated, data-intensive industries. This position placed her at the center of large-scale AI initiatives, where she observed how identical technologies produced radically different outcomes depending on workforce readiness and leadership engagement.
“Companies are making a critical mistake by trying to save on people and education while investing heavily in AI tools,” Andreeva explains. “Technology alone does not create value. The real difference between a successful AI project and a failed one is the people behind it, their engagement, understanding, and alignment with leadership.”
Her observations highlight a systemic problem: AI failures are not isolated incidents, but repeat across industries and companies that underestimate the human dimension. Without investing in skills, motivation, and executive understanding, even the most advanced AI stack cannot deliver on its promise.
Organizations frequently overlook essential elements when they treat AI training as a single occurrence instead of an ongoing, structured process. Nevertheless, leading research demonstrates that this method contains fundamental design errors. McKinsey & Company argues that AI upskilling must be treated as a change imperative rather than a standalone training initiative, which requires new training methods to alter their operational procedures, evaluation standards, and executive conduct, as well as their reward systems.
The process of developing AI skills within a workforce requires multiple training sessions instead of a single training session. Organizations need to establish AI systems as core elements of their operational processes, including their evaluation systems and decision-making procedures. Organizations need to implement this system because training without proper integration will remain academic, while organizations will experience delays in adopting AI technology, which will result in their AI investments not generating financial benefits.
This diagnosis closely mirrors what Katerina Andreeva has observed in large enterprise environments. During her work with major insurance and telecommunications organizations, she repeatedly saw companies invest in technically sound AI solutions while underestimating the organizational effort required to make them stick. Employees attended workshops, learned the basics of new tools, and then returned to unchanged processes, conflicting priorities, and leadership teams that were not aligned on how AI should be used.
“The problem isn’t that people don’t want to learn,” Andreeva notes. “It’s that learning is disconnected from daily work. If AI skills aren’t reinforced through real use cases, leadership support, and clear incentives, they fade quickly.”
In practice, effective AI upskilling looks less like a course and more like a journey: continuous learning tied to real business problems, supported by leadership, and reinforced through measurement and accountability. Without this foundation, even the most advanced AI technologies remain underleveraged.
Much of today’s AI conversation focuses on platforms, models, and vendors. Yet organizations that treat AI primarily as a technology investment often overlook a critical factor: organizational readiness goes far beyond tools. Even the most advanced AI initiatives can stall if foundational elements (especially data and workflows) are not prepared for scale.
“Another major challenge we see,” Andreeva explains, “is that many organizations aren’t truly ready for AI. Their data is poorly organized, incomplete, or structured incorrectly for AI applications. On top of that, companies often treat AI as a pilot project rather than a full-scale capability.”
Analysts confirm this pattern. According to Gartner, 63 % of organizations either lack or are unsure they have the right data management practices to support AI, in the coming year, those without AI-ready data are likely to abandon around 60 % of their AI initiatives. This highlights that issues with data readiness and infrastructure (not the technology itself) remain the largest barrier to scaling AI.
Andreeva’s experience working with large insurance, telecom, and enterprise clients mirrors these findings. She was a data engineer preparing massive datasets for analytics and AI teams. Across multiple organizations, she observed the same pattern: companies rushed to implement AI tools while underinvesting in the data engineering and organizational foundations that make AI truly usable.
According to Andreeva, organizations need to approach AI as a capability that grows over time rather than a one-off project. This means investing in data readiness, embedding AI into everyday workflows, and providing employees and leaders with the skills and support to use these tools confidently. When these elements are addressed, pilots have a real chance to scale and deliver tangible results, setting the stage for broader transformation across the business.
Katerina Andreeva has applied her approach to AI adoption across multiple industries, helping companies move from stalled pilots to measurable impact. One example comes from a mid-sized logistics company she worked with while running The Brained Inc.
The company had invested heavily in AI-powered demand forecasting but struggled to see results. Pilots were inconsistent, employees were hesitant to trust the models, and leadership lacked a clear strategy for scaling adoption,” says Andreeva.
Andreeva started by mapping business objectives to practical AI use cases, focusing on areas where quick wins were possible. She then ran role-specific training sessions, involving both operational staff and management, and established a mentorship and feedback loop to support employees as they applied AI tools in daily workflows.
Within six months, the company reported a 20% improvement in forecast accuracy, which led to reduced inventory costs, along with 30% faster planning cycles as employees became confident in AI-driven recommendations. Engagement and adoption also increased significantly, with over 85% of staff regularly using the AI platform, compared to less than 40% before the program.
As Katerina notes: “The technology was already there. What was missing was the people, their confidence, and a structured adoption plan. Once we addressed that, results followed quickly.”
The experience of AI leaders makes one thing clear: technology alone doesn’t drive transformation. Successful AI adoption requires a systematic approach that combines data readiness, workforce skills, and governance structures. Without attention to these dimensions, even the most advanced AI stacks remain underutilized.
“Companies often focus on acquiring the latest AI tools but neglect the foundations that make them effective,” says Katerina Andreeva. “Data must be prepared, processes redesigned, and employees trained and supported continuously. When these elements are in place, AI moves from a pilot experiment to a measurable business capability.”
IBM’s research confirms this: organizations that upskill and reskill employees for AI in a structured, ongoing way consistently outperform those that treat AI training as optional or one-off. Harvard Business Review also notes that technology alone rarely transforms incumbents, highlighting the need to align people, processes, and governance for real impact.
Real-world results confirm these insights. Companies like Bank of America and Walmart paired AI adoption with comprehensive employee training, achieving measurable business outcomes: higher adoption rates, faster planning cycles, and cost reductions. These examples show that investment in people and process is not a side project; it is the key driver of AI value.
For executives navigating AI transformation, the lesson is clear: invest in people, organize your data, and embed AI into governance and workflows. Only by treating AI as a holistic capability, rather than a technology purchase, can organizations translate tools into tangible business results.