The pace of corporate transformation is accelerating, yet many initiatives fail to deliver sustained value. The DOTS model—Direction, Operating model, Team and adoption, and Systems—provides a framework to diagnose where transformations break down and what to fix first. By aligning decisions, capabilities, adoption, and scalable execution, organizations can achieve lasting performance shifts.
In today’s business environment, artificial intelligence digital tools, evolving customer expectations, and new regulations are compelling companies to reconfigure their business models. However, research indicates that about 70 percent of transformations fail to deliver real value, with only about 30 percent meeting their target value and creating sustainable advantages.
Common Pitfalls in Transformation Initiatives
Many organizations roll out changes but fail to create the conditions for people to perform differently. Often, technology is in place, but the organization does not adapt accordingly. This misalignment creates a gap between ambition and capability, leading to burnout, rework, and stalled outcomes.
Transformations do not fail at rollout; they fail weeks later when old incentives, ownership, and habits revert to the primary basis for actions and decisions. To succeed, organizations must connect operational dots across people, technology, and mindset.
Operational Clutter: Five Key Issues
Inadequate operational linkages result from several critical issues:
- Underestimating people readiness Teams often lack the skills, time, or support needed to deliver new ways of working. This fragility leads to delays, quality issues, and missed revenue goals.
- Unclear ownership When no one takes end-to-end ownership, meetings multiply, decisions drift, and priorities shift, leading to stalled transformations.
- Treating adoption as training Delivering a solution without changing workflows results in employees reverting to old habits, undermining the transformation.
- Weak foundations Messy data, unconnected systems, and manual workarounds hinder automation and AI implementation, leading to slow delivery and poor quality.
- Late compliance and governance Adding requirements near the end creates rework, delays, and risk, undermining confidence in the transformation.
The real problem lies in operational disconnect and ineffective performance. Most transformations fail because organizations continue to measure and reward old behaviors while asking employees to operate in new ways. When incentives, ownership, and decision-making shift, adoption becomes natural, and results show up in performance.
The Role of AI in Structural Transformation
Artificial intelligence represents a structural transformation that impacts all sectors. While AI offers significant benefits, such as increased productivity, improved process quality, and enhanced safety, it also presents challenges. Some job roles may change or reduce, but the demand for new, more qualified, creative, and relational skills will grow.
To harness the opportunities of AI, organizations must focus on formation and participation. Every worker should have access to annual training to update digital skills, and this time should be recognized as part of their work. Involving all stakeholders, especially the most vulnerable, is crucial for governing change effectively.
The sindacato (labor union) plays a central role in organizing work and ensuring that AI is regulated and contracted within ethical values. Redistributing the productivity gains from innovation through reduced working hours, new welfare tools, and productivity agreements is essential. Policies must support this transition by strengthening labor rights, promoting education, and fostering a development model centered on the individual.
The fundamental question is: What kind of society do we want to build? One that uses innovation to improve collective well-being, expand rights, and promote social cohesion, or one that accepts new forms of exclusion? The answer depends on the choices we make today, with the sindacato playing a pivotal role in this transformation.
AI and Return on Investment
While AI is increasingly part of corporate strategies, only a small percentage of companies generate new revenue from it. This gap puts pressure on CEOs to rethink organizational structures. In Italy, AI adoption is still slow, particularly among small and medium-sized enterprises (SMEs), but there is growing awareness of its potential to drive new revenue.
As we move into 2026, the focus will shift to certifying AI adoption through measurable returns on investment. Despite concerns about a potential AI bubble, there are no signs of the promises associated with AI diminishing. Companies with AI-first strategies are more agile and effective, as demonstrated by IBM’s own experience in redesigning processes with AI.
By addressing operational clutter and embracing AI responsibly, organizations can navigate the complexities of change and realize sustainable benefits.
