Polish software company Future Processing has published a case study examining how it moved from employee-level AI adoption toward broader organizational transformation over 15 months. The report draws on two company-wide employee surveys and quarterly maturity assessments, highlighting a gap between individual AI use and team-wide capability. In its first survey, 81% of respondents said they used AI tools, but rated their team’s AI use at just 34 out of 100. The case study explores how the company approached organizational change, AI governance, internal capability development and the redesign of software delivery workflows.
Future Processing Examines the Gap Between AI Adoption and Transformation
Future Processing is sharing details of its internal AI transformation in a new case study aimed at technology leaders seeking to move beyond simply providing employees with AI tools. Titled Our path from AI adoption to AI transformation, the report documents a 15-month initiative built around employee feedback, organizational maturity assessments and changes to the company’s software delivery model.
The findings illustrate a challenge facing enterprises adopting generative AI: widespread individual use does not necessarily mean teams have integrated the technology into how work gets done.
In Future Processing’s first company-wide survey, 81% of respondents reported using AI tools. Yet the same respondents rated their team’s AI use at 34 out of 100. The company uses this gap to frame its transformation story, focusing on the organizational practices needed to turn personal experimentation into coordinated business capability.
The figures come from Future Processing’s own research and should be understood as company-specific findings rather than a representative measure of the wider technology industry.
From access to organizational capability
The case study addresses four practical questions for enterprise technology leaders: whether employee use of AI constitutes meaningful adoption, how organizations can control the information entered into AI tools, how to develop internal AI capabilities when specialist talent is difficult to hire, and when AI begins to change the way work is performed.
These questions reflect the difference between deploying a technology and redesigning an operating model around it. Employees may use AI assistants for writing, coding, research or summarization while teams continue to follow the same processes, responsibilities and delivery practices they used before.
Future Processing’s approach centers on the sequence of organizational changes, the metrics used to evaluate progress and the lessons it would repeat. The company says its account draws on two company-wide surveys and quarterly maturity assessments, although the announcement does not provide the complete survey methodology or detailed results from every assessment.
Mike Zamarski, Future Processing’s AI Strategy Advisor and the author of the four-point AI Organisational Resilience Framework, emphasized the human side of the transition. He said the company anticipated that employee adoption and organizational inertia would be more difficult to address than the technology itself.
That perspective places leadership, workforce readiness and organizational design alongside model selection and technical implementation. For enterprises, it also suggests that measuring adoption solely through tool access or usage frequency may miss whether AI is improving collaboration or changing business outcomes.
Delivery 2.0 reshapes software development
Future Processing’s wider transformation strategy includes AI Boosted Delivery, a programme intended to align AI adoption with business objectives. According to the company’s enterprise AI transformation guide, the initiative began with workshops exploring what future software delivery could look like. Teams then worked backward to identify the changes required and determine the order in which to implement them.
The resulting Delivery 2.0 model redesigns the company’s operating approach around small teams of up to three product builders. Team members share a broad base of AI-supported software delivery skills, while each person brings a deeper specialization that can be applied when a project requires it.
This model represents a shift away from treating software development as a collection of narrowly separated tasks. AI tools can assist with activities such as code generation, testing, documentation and analysis, but organizations still need people who can validate outputs, make technical decisions and understand product requirements.
Small, broadly skilled teams could make it easier to coordinate these activities, although the results will depend on project complexity, engineering standards, team experience and the reliability of the tools being used. Future Processing has not supplied independent performance benchmarks in the announcement that quantify the effect of Delivery 2.0 on delivery speed, software quality or cost.
Building AI expertise from within
Future Processing also describes an internal capability-building strategy that focused on developing existing employees rather than relying primarily on external recruitment.
According to the company’s guide, it identified people who could contribute to its AI ambitions and provided them with protected time, suitable working conditions and a clear organizational priority. The team subsequently built the company’s own agentic platform.
The approach addresses a common enterprise challenge: demand for AI engineering and implementation expertise can outpace the supply of experienced specialists. Internal development can help organizations apply existing domain knowledge to new tools and workflows, while reducing dependence on external hiring alone.
However, building an agentic platform also introduces requirements for access controls, monitoring, testing and accountability. The announcement does not detail the platform’s architecture, supported models, security controls or specific production use cases, so its technical capabilities cannot yet be independently assessed from the available information.
External research points to a wider implementation challenge
Future Processing’s enterprise AI transformation guide references research from EY and S&P Global to illustrate the distance between AI use and organizational impact.
According to the figures cited in the guide, EY’s 2025 Work Reimagined survey found that 88% of employees used AI at work, while 5% used it in advanced ways. The guide also cites S&P Global findings that 42% of companies abandoned most of their AI initiatives in 2025, compared with 17% in 2024.
These figures point to different aspects of the implementation challenge: employee adoption does not necessarily indicate advanced use, and experimentation does not guarantee that projects will progress. The precise definitions and survey populations behind each statistic should be reviewed before using the figures to draw direct comparisons across organizations.
Future Processing’s case study is available in English and Polish through the company’s AI transformation case study page. The accompanying guide, authored by Head of Delivery Daria Polończyk and published in September 2026, provides additional context for the company’s operating-model changes.
A practical test for enterprise AI strategies
Future Processing’s experience highlights a shift in how enterprises can evaluate AI programmes. Tool availability and individual usage are useful starting points, but they do not show whether teams have redesigned workflows, established appropriate controls or achieved measurable business improvements.
For technology leaders, the more consequential questions concern the work itself: which processes should change, what skills teams need, how risks will be governed and which measures will demonstrate progress.
The company’s 15-month account offers one example of how an organization approached those questions. Its broader lesson is that enterprise AI transformation requires coordinated changes to people, processes and technology, rather than a tools-first rollout alone.
Market Landscape
Enterprise AI adoption is moving beyond software procurement and employee experimentation toward workflow redesign, governance and organizational capability building. Companies increasingly need to understand not just whether employees use AI, but whether teams can integrate it into repeatable, controlled processes.
Future Processing’s approach combines employee surveys, maturity assessments, internal capability development and a redesigned software delivery model. Its Delivery 2.0 structure emphasizes small, broadly skilled teams supported by AI tools.
The wider challenge is measuring the business impact of these changes. Usage rates and maturity scores can help track adoption, but enterprises also need evidence tied to software quality, delivery outcomes, cost, security and employee effectiveness.
Top Insights
- Adoption is not transformation: Future Processing reported 81% employee AI usage but a team-level AI-use score of 34 out of 100 in its first company-wide survey.
- A 15-month transformation: The case study draws on two employee surveys and quarterly maturity assessments.
- Software delivery redesign: Delivery 2.0 uses teams of up to three product builders with broad AI-supported skills and individual specializations.
- Internal capability development: Future Processing says it gave existing employees protected time and organizational support to build its own agentic platform.
- Measurement matters: The company frames transformation around changes to workflows and organizational capability, not just access to AI tools.
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