Future Processing Highlights AI Adoption Gaps and Strategic Shifts
Future Processing's new case study reveals the stark contrast between the adoption of AI tools and their effective use within teams, with implications for organizations seeking to enhance AI integration.
Key Facts
- 81% of employees use AI tools, but only 34/100 for team usage shows a significant adoption gap.
- 88% use AI, yet only 5% utilize it advancedly, indicating widespread underutilization in firms.
- 42% of companies abandoned AI initiatives in 2025, highlighting vulnerability in AI strategy execution.
- Future Processing's internal AI capability development avoided hiring, leveraging existing talent for efficiency.
- Delivery 2.0 model redesign indicates strategic shift towards agile, small teams for enhanced productivity.
Summary
Summary
Future Processing, a leading software company in Poland, faced challenges in effectively utilizing AI tools across its teams. To address this, the company implemented a structured AI transformation strategy over 15 months, resulting in improved team-level AI usage and a more cohesive approach to AI integration.
Background
Future Processing, headquartered in Gliwice, Poland, has been delivering technology services for over 25 years. The company operates in various sectors, including finance, insurance, media, and energy. Prior to the deployment, Future Processing conducted its first company-wide survey, revealing that while 81% of employees used AI tools, team-level usage was rated only 34 out of 100.
Challenge
The primary challenge was the disconnect between the high usage of AI tools and the low effectiveness of team-level collaboration in utilizing these tools. Future Processing needed to enhance how teams integrated AI into their workflows to maximize its potential.
Solution
Future Processing developed the AI Boosted Delivery program as part of its broader transformation strategy. This initiative began with workshops to envision the future of delivery, followed by a backward mapping of necessary changes. The result was Delivery 2.0, which redesigned the operating model into small teams of up to three product builders. Each team possessed a diverse skill set in AI-supported software delivery, with one member specializing in a specific area as needed. The company also focused on nurturing existing talent rather than hiring new AI specialists, providing employees with protected time and clear priorities to develop their capabilities.
Results
The case study indicates that the structured approach led to significant improvements in team-level AI usage, although specific metrics on the outcomes were not provided. The transformation allowed Future Processing to better integrate AI into its operations and enhance overall productivity.
Key Insights
The case study highlights the importance of not only providing AI tools but also fostering an environment that encourages effective collaboration and skill development. Organizations should focus on internal capabilities and redesign workflows to fully leverage AI's potential.
Customer Testimonial
“Long before our AI transformation began, I came to a conclusion that hardly anyone was voicing at the time: the hard part of the journey would be human adoption and organisational inertia, more than the technology, and our transformation had to allow for that.” — Mike Zamarski, AI Strategy Advisor at Future Processing.
Entities Mentioned
Companies
Products
Technologies
People
Organizations
Key Concepts
Definitions
- AI transformation
- The process of redesigning how work gets done through the integration of AI technologies into business operations.
- organizational resilience
- The ability of an organization to adapt to changes and challenges, particularly in the context of adopting new technologies.
- agentic platform
- A platform developed by Future Processing that empowers employees to utilize AI tools effectively within the organization.
- Delivery 2.0
- A redesigned operating model at Future Processing focused on small, specialized teams for AI-supported software delivery.
- AI Boosted Delivery
- A program initiated by Future Processing as part of its transformation strategy to enhance software delivery through AI.
Use Cases
- →Improving team-level AI usage
- →Redesigning work processes with AI
- →Building AI capability without hiring
- →Enhancing software delivery through small teams
- →Utilizing employee surveys for AI strategy
- →Creating a structured approach to AI transformation
Frequently Asked Questions
What is the main focus of Future Processing's case study?
The case study focuses on the journey of AI adoption to transformation within Future Processing, highlighting the challenges and strategies employed to enhance team-level AI usage.
How does Future Processing measure AI usage among employees?
Future Processing measures AI usage through company-wide employee surveys and quarterly maturity assessments to gauge how effectively AI tools are utilized.
What is the significance of the four-point AI Organisational Resilience Framework?
The framework, developed by Mike Zamarski, emphasizes the importance of human adoption and overcoming organizational inertia as critical factors in successful AI transformation.
What does Delivery 2.0 entail?
Delivery 2.0 is a new operating model that organizes small teams of product builders, each with a mix of AI-supported skills and specialized expertise, to enhance software delivery.
How does Future Processing approach building AI capability?
Instead of hiring new talent, Future Processing identifies existing employees with potential and provides them with the necessary resources and time to develop AI capabilities.