Companies are investing significant capital in artificial intelligence–based solutions to extract concrete value from their vast amounts of data. The numbers speak for themselves: in 2025, Gartner forecasts that total investments will exceed USD 1.5 trillion and reach USD 2 trillion in 2026. In Italy, the landscape is equally dynamic: according to the Assintel 2025 report, spending on AI solutions is expected to grow by around 35% this year.
The value of AI and the challenge of scaling
If we move beyond the sheer volume of investments and try to quantify the value companies derive from them, the picture changes. While individual projects can deliver very attractive ROI, BCG states that, despite experimentation, “only 5% is generating value at scale — and nearly 60% report having achieved minimal or no impact so far.” This statement must, of course, be interpreted carefully: AI has enormous potential, but only a few companies manage to scale its benefits beyond specific use cases, even though the same source notes that 35% of companies have now entered the scaling phase and are therefore beginning to generate systemic value.
So why do companies struggle to make AI transversal across their processes and an integral part of corporate culture? Depending on the perspective, the causes may vary, but one of them is certainly the lack of data literacy.
Data literacy: the current state and why investment is needed
To develop a coherent discussion around data literacy, it is useful to start from the context: what are we talking about, how many companies are aware of its central role, and how widespread data literacy actually is within organizations.
What is data literacy?
Analysts agree in defining data literacy as the ability to read, interpret, and communicate data in a business context, transforming it into useful information to support strategic and operational decision-making.
Data literacy is not simply the ability to read basic charts; rather, it is the ability to work with relevant data and extract meaningful insights to solve problems or make better decisions. It is not a skill reserved for data scientists, because all roles can and should make data-driven decisions.
According to Gartner, poor data literacy is one of the main obstacles to the success of Data & Analytics initiatives; in fact, the second biggest barrier according to Chief Data Officers at leading global companies. The reason is simple: an organization may invest in advanced and innovative technologies, but without widespread skills to use data, decisions continue to be based more on intuition than on evidence. In this sense, the topic is closely linked to AI adoption: if people do not develop the skills and capabilities to interpret and use data, they will also tend to make less use of the advanced solutions made available by the company.
Fortunately, awareness among management is growing and is beginning to translate into concrete initiatives: 83% of organizations have already launched or are planning structured data literacy programs, a clear sign that the topic is now seen as a strategic lever rather than a secondary activity. The urgency is further highlighted by other figures: for example, according to a Harvard Business Review study, only 25% of employees feel confident in their data skills.
Data literacy, investment, and effectiveness are to be strengthened
Once the context is clear, and above all, the fact that only one employee in four claims to have adequate data skills, it is worth going deeper and understanding why data literacy is still so open to improvement.
Limited resources and budgets
Companies do invest in training, but given the revolution brought about by data, they should certainly focus more on data literacy. Analysts estimate that companies spend between 2% and 5% of payroll on training and skills development, a percentage that is insufficient to support an AI transformation as pervasive as the one we are witnessing. This results in short courses, non-continuous initiatives, and an inability to cover the entire workforce.
Resistance to change and fear of becoming irrelevant
Low data literacy also stems from resistance to change. Many people experience digital transformation with fear: fear of making mistakes, of being evaluated, and of having to put in more effort to achieve the same results. This is the natural comfort zone reaction that emerges whenever we perceive a change in required skills or daily processes.
AI greatly amplifies this phenomenon because, beyond natural resistance, the arrival of artificial intelligence is interpreted by many as a risk to their role. This perception generates passive resistance and reduces the adoption of any data-related tools.
Limited sponsorship
There is also a cultural issue that is not always addressed with sufficient clarity: many leaders claim to want a data-driven company but fail, through their daily behaviors, to convey the message that data should guide all decisions, not just strategic ones. The result is that data analysis remains confined to top management, while operational activities continue to be decided as they always have been, effectively undermining any attempt at widespread adoption.
Ineffective training
Some data literacy programs fail because they overlook how people work today. Assuming that employees — especially in the era of hybrid work — can devote hours and hours to classroom-based training sessions is unrealistic. The result is sporadic training, disconnected from daily work, often theoretical and not very engaging.
How to truly elevate data literacy in the company: 4 key steps
Given these challenges, how should a data literacy journey be approached? Available best practices converge on four key points.
Assess the current level and set clear objectives
More mature organizations conduct formal assessments to understand their level of data literacy and identify the skills that are missing to reach satisfactory levels. As evidence of the existing urgency, 87% of companies have already assessed their current state.
This phase also serves to define measurable objectives: not just “using data to decide,” but setting concrete indicators to evaluate employees’ progress toward data usage and, therefore, the company’s transformation into a data-driven organization.
Secure maximum leadership support
We have already highlighted how many initiatives fail due to insufficient support from top management. A formal endorsement is not enough: daily commitment is required, in behaviors and internal communication.
Companies that succeed are those where the CEO, CIO, or Chief Data Officer demonstrates how they make data-driven decisions, share achieved results, and make the use of analytical tools visible. This creates a powerful domino effect: if leadership uses data every day, teams will do the same.
Modern training
With regard to training as well, we have emphasized how traditional approaches no longer fit modern work rhythms and mindsets. Fortunately, the market offers many solutions:
- micro-learning platforms based on short videos and content lasting just a few minutes;
- exercises using real company data, not abstract examples;
- periodic tests to consolidate skills;
- gamification to increase engagement and retention;
- role-based learning paths (managers, analysts, operational teams);
- on-demand learning accessible “when needed,” not only in the classroom.
Measure results and continue to evolve
The world of data never stands still. After the first wave of training, structured monitoring is required: tool usage rates, number of data-driven decisions, skill improvements measured through periodic tests, and changes in established habits.
The role of Kirey: making AI a value-driven investment, end to end
At Kirey, we are aware that the value of data and AI does not depend solely on the technologies adopted, but on a company’s ability to use them consciously, daily, and with a results-oriented mindset. For this reason, we support organizations in end-to-end AI transformation journeys, starting from concrete needs and targeted solutions, and then evolving toward a systemic transformation that involves processes, skills, and culture.
Our expertise combines high-level technological capabilities with a strongly consultative approach. We support organizations on their path toward a data-driven model that makes data usage not an episodic initiative or a solution to a specific problem, but a daily habit.
Contact us to discover how we can help you turn technology into tangible value for your organization.
