From AI Experimentation to Business Value: GFT's AI Experience Framework
Many organisations invest heavily in AI experimentation, only to later wonder what business problem they were trying to solve.
To help bridge this gap between AI's capabilities and practical business value, GFT has developed the AI Experience Framework. This methodology aims to help companies move beyond experimentation to create AI solutions that address specific business challenges.
Common pitfalls that derail AI initiatives
Through extensive cross-industry experience, GFT has identified that many AI initiatives falter due to a fundamental disconnect between business strategy and AI technology implementation. Our analysis reveals several critical challenges that organisations consistently face:
- Unrealistic expectations about AI capabilities
- Misalignment with company vision
- Poor use case selection
- Lack of AI awareness among business teams
- Unaddressed AI bias concerns
How to build AI solutions that actually solve problems:
The GFT AI Experience Framework
The GFT AI Experience Framework offers a four-stage approach that links AI initiatives to concrete business results.
It starts with a Business Service Ecosystem Analysis, where teams map out their entire business landscape to spot valuable AI opportunities. This stage includes checking AI readiness and creating a foundation for strategic adoption, while also evaluating the existing architecture.
The second stage, AI Awareness Elevation, focuses on building understanding across the organisation. Teams learn about AI's potential while addressing common concerns and resistance. Regular updates keep stakeholders informed about AI trends and developments, helping align everyone with the organisation's AI vision.
During the third stage, AI Opportunity Identification, teams draw from a library of over 150 proven use cases. Through collaborative workshops, they develop solutions that address specific business challenges. Each opportunity is then evaluated based on data readiness and potential business impact, using insights from successful implementations across different industries.
The final Quick Pilot and Scale stage concentrates on creating experimental pilots and proofs of concept. This includes developing complete user experiences and establishing proper governance and monitoring systems. The focus stays on smooth integration with existing systems, ensuring a seamless transition to AI-enabled processes.
Making AI strategy hands-on
The framework takes an unusually hands-on approach to AI strategy. Workshop participants work with physical cards that represent over 150 real-world AI applications.
These cards become practical tools for brainstorming and discussion, letting teams physically interact with different AI possibilities.
Teams spread these cards across workshop tables, grouping similar cases, mapping connections, and planning implementation steps. This tactile approach helps transform abstract AI concepts into concrete possibilities that everyone can see and discuss. When business and technical teams work with the same physical materials, it helps create a shared language and breaks down communication barriers.
Workshop leaders guide teams through interactive exercises using the cards, encouraging people from different departments to work together, discuss options, and agree on priorities.
The physical nature of the work helps participants better understand AI's possibilities while making strategic decisions feel more concrete.
Fast Track to AI value: AI Experience Express
Organizations can get started with a one-day workshop, the AI Experience Express that covers AI strategy, hands-on experience with use cases, team collaboration, and practical next steps.
The structured process enables teams to move from ideas to action quickly, ensuring tangible outcomes.
In just six hours, participants gain a clear understanding of AI and identify specific use cases they can start exploring immediately – tailored to their level of readiness and business maturity.
The approach offers a 360-degree way for clients to become more AI-driven while staying closely aligned with business strategy, rather than treating AI as just a technological exploration. The framework encourages organisations to think strategically while starting with practical, achievable goals.
By combining strategic thinking with ready-to-use tools, teams can begin implementing AI solutions more quickly and effectively.
Real results from strategic AI implementation
GFT's framework is already helping organisations across industries put AI to work. A major financial institution used it to identify where AI could streamline their processes, leading to significant efficiency gains. In manufacturing, one company applied the approach to develop AI-powered quality control, cutting defect rates and reducing costs.
Businesses in finance, insurance, automotive, retail, healthcare, manufacturing, and telecom have applied the framework to address industry-specific challenges, proving its versatility in driving efficiency, innovation, and competitive advantage.
These successes show how combining clear strategy with practical steps helps organisations move beyond basic AI experiments to create real business value.
Looking ahead: Making AI work for your business
The numbers are clear: Many organisations struggle to make AI successful. A systematic approach can help companies avoid common pitfalls and identify opportunities where AI can make a real difference. The key is balancing quick wins with long-term strategy.
GFT's framework helps organisations do both - quickly spotting opportunities for AI while keeping focused on broader business goals. This balanced approach means AI projects can deliver immediate benefits while building toward larger strategic objectives.
