Why Companies Should Consider Establishing A Generative AI Lab Within Their Organisation

At Vajra Global and XITE Create, we’ve been on a learning journey exploring how AI can meaningfully support revenue operations and customer experience. What we’ve seen is that even small, well-placed AI interventions can make a big difference: helping teams respond faster, personalise better, or reduce manual effort.

Our focus is on applying AI where it truly adds value and not using it for the sake of novelty. We work closely with clients to identify high-impact opportunities where GenAI can support business goals, while moving with clarity, speed, and purpose through bold experimentation.

In this context, establishing a dedicated Generative AI lab is becoming an increasingly strategic consideration. These labs provide structured environments for testing ideas, building proprietary capabilities, and creating solutions tailored to specific business needs. At the same time, organisations can benefit from collaborating with external experts to accelerate pilots and bring in specialised AI skillsets, combining the best of internal ownership and external insight.

Strategic Importance of Internal Generative AI Labs

GeneAI represents a fundamental shift in how businesses can approach innovation, productivity, and customer engagement. According to Deloitte¹, over 60% of chief intelligence officers now report directly to CEOs, reflecting the increased importance of tech leaders in setting and overseeing AI strategy. This statistic signals the growing importance of having structured approaches to AI development and implementation.

Internal GenAI labs provide focused spaces for AI experimentation and solution development tailored to business needs. Unlike simply adopting third-party AI tools, a lab allows companies to build proprietary capabilities that directly address their unique challenges and create sustainable competitive advantages.

Encouraging innovation and experimentation

One of the principal benefits of establishing a Gen AI lab is creating a dedicated space for innovation. These labs serve as incubators where teams can explore AI applications without the immediate pressure of production deadlines. Deloitte's approach², as evident in their Gen AI Lab Programme, emphasises collaborative hackathons, labs, and design sprints that help customers transform innovative ideas into practical solutions.

The lab environment encourages what industry experts call "Lab Learning," where teams can experiment and explore the full potential of AI technologies in a controlled setting.

Building internal expertise and capabilities

Developing in-house Gen AI expertise provides long-term strategic advantages. It cultivates teams with a deep understanding of both the technology and the organisation's specific context.

The Fraunhofer FIT³ Generative AI Lab highlights the importance of combining technical expertise for prototyping with an understanding of organisational dynamics to integrate these systems into existing processes and workplaces. This dual focus ensures that AI solutions are not only technically sound but also practically implementable within the organisation's current operations.

Key Roles and Functions of Generative AI Labs

Generative AI labs serve multiple functions within an organisation, extending beyond mere technology development. Understanding these diverse roles helps justify the investment in establishing such labs.

Idea generation and innovation acceleration

Gen AI labs help businesses brainstorm new ideas, designs, and concepts based on existing patterns. In a lab environment, this accelerates ideation and innovation. This capability is especially valuable for companies seeking to differentiate themselves through novel products, services, or approaches.

The creative potential of Gen AI is significant. Even as far back as 2023, BCG’s experiments⁴ showed that around 90% of participants improved their performance when using Gen AI for creative product innovation. This suggests that Gen AI labs can serve as creativity multipliers within organisations, helping teams generate better ideas more quickly than they could through traditional methods alone.

Technology integration and customisation

An internal Gen AI lab provides the expertise needed to effectively integrate AI technologies with existing systems. Language Model integration is a key function, enabling teams to manage, deploy, and scale AI models efficiently throughout the organisation.

Through customisation, companies can create AI solutions that address their specific needs rather than adapting their processes to fit generic AI tools. Infosys Generative AI Labs⁵, for example, emphasises ready-to-use industry solutions and accelerators that help businesses embed generative AI into enterprise systems and applications.

Research and development of AI applications

An internal AI research lab can serve as an R&D centre for exploring new AI applications specific to the company's industry and challenges. They can develop proprietary generative AI models or fine-tune existing ones to create unique capabilities that competitors cannot easily replicate. For organisations looking to move faster, engaging external partners with proven research capabilities can complement internal efforts and reduce time-to-value.

The Fraunhofer FIT approach demonstrates how labs can adopt "a thorough, socio-technical approach to fathom the realm of generative AI and the profound transformations it triggers across societies, industries, and individuals". This research dimension ensures that companies remain at the leading edge of AI advancements relevant to their field.

Business Impact and Value Creation

The justification for investing in a Gen AI lab ultimately comes down to its impact on business outcomes. Evidence from various industries such as Healthcare, Supply Chain, and Financial Services, suggests that such labs can deliver substantial value.

Efficiency and productivity enhancements

Generative AI can significantly improve operational efficiency through automation of routine tasks and enhancement of existing workflows. According to industry insights, it can assist software engineers by generating and maintaining code, finding and resolving bugs, and automating code testing, allowing engineers to focus on more complex problems.

In practical applications, a US-based biopharma company using Infosys solutions enabled medical writers to automate summarisation of clinical trial reports using LLMs, reducing manual effort by 30%. Such productivity gains represent tangible returns on investment in Gen AI capabilities.

Personalisation and customer experience

A Gen AI lab can develop solutions that create highly personalised customer experiences. Hyper-personalisation is one of the key benefits of generative AI. It can tailor customer experiences by analysing individual data to personalise interactions.

For financial services, semantic search powered by Gen AI helps wealth managers find insights from thousands of documents instantly, resulting in greater customer satisfaction. These enhanced customer experiences can translate directly to improved retention, higher satisfaction scores, and increased revenue.

Data synthesis and insight generation

Gen AI labs excel at analysing and synthesising large datasets to generate actionable insights. This capability allows organisations to extract more value from their existing data assets and make more informed decisions.

Processing large amounts of unstructured data and identifying patterns that humans might miss represents a significant competitive advantage. Companies with Gen AI labs can develop custom analytical models that address their specific information needs rather than relying on general-purpose solutions.

Implementation Considerations

Establishing a GenAI lab requires careful planning and consideration of several factors to ensure success.

Talent and skill development

Building an effective GenAI lab demands specific expertise. Companies must decide whether to hire internally, develop existing talent, or outsource based on their needs. Hiring internally gives more control over the process and builds long-term capabilities. But in many cases, partnering with an external specialist can help bridge short-term skill gaps while internal teams ramp up.

Ongoing training and skill development are essential components of a successful Gen AI lab. AI-assisted training programmes designed to enhance team skills in generative AI ensure teams are equipped to utilise AI's potential.

Ethical frameworks and governance

Responsible AI development is a critical consideration for any GenAI lab. As Infosys notes, "Even as generative AI creates breakthrough opportunities, it must be adopted with ethical consideration and designed for risk mitigation."

A robust ethical framework for AI development should address issues such as privacy, security, bias, and transparency. With a detailed framework, risk management can be modelled centrally for bias and hallucination, and governance can be adapted constantly to meet legal, security, and privacy guidelines.

Integration with existing systems and processes

For Gen AI labs to deliver maximum value, their outputs must integrate smoothly with existing business systems and processes. This requires careful planning and coordination between the lab and other departments.

The Fraunhofer FIT approach conceptualises this integration across four phases: a) ideation, b) strategy formulation, c) design and development, and d) operating at scale. This methodical approach helps ensure that innovations from the lab can be effectively deployed throughout the organisation.

Potential Challenges and Mitigation Strategies

While the benefits of Gen AI labs are substantial, companies must also be prepared to address several challenges.

Avoiding value destruction

Research from BCG indicates that when generative AI is used in the wrong way, for the wrong tasks, it can cause significant value destruction. Companies must develop clear guidelines for which tasks are appropriate for AI assistance and which require different approaches.

BCG's experiments⁴ in 2023 found that participants performed 23% worse when using Gen AI for business problem solving compared to those working without it. So much has changed with AI since this study but it underscores the importance of understanding the technology's capabilities and limitations.

Managing expectations and measuring success

Establishing realistic expectations for a Gen AI lab is essential for maintaining organisational support. Clear metrics for measuring success help demonstrate the lab's value and guide future investments.

These metrics might include productivity improvements, cost savings, revenue generation from new products or services, or more qualitative measures such as employee satisfaction with AI tools or customer experience ratings. Setting reasonable timeframes for achieving these outcomes helps manage stakeholder expectations and sustain investment in the lab.

Maintaining human-AI balance

Finding the right balance between human creativity and AI assistance requires ongoing attention. AI can act as a catalyst for personal productivity and creativity by lowering skill barriers, enabling more people to access and use knowledge for efficient problem-solving and innovation

Companies must develop nuanced approaches to human-AI collaboration that leverage the strengths of each while mitigating potential weaknesses. This requires continuous education and guidance for employees on how they can work effectively with AI tools.

Future Outlook for Generative AI Labs

The role of Gen AI labs within organisations is likely to develop as the technology matures and new applications emerge.

Expanding applications across functions

While early GenAI applications often focused on specific areas like content creation or code generation, the potential use cases continue to expand as part of a broader corporate AI strategy. Industry experts identify applications across product development, operations, project management, HR, employee management, risk management, and fraud detection.

As these applications mature, Gen AI labs will likely play increasingly important roles in strategic decision-making and business transformation initiatives. The versatility of GenAI technology means that internal labs will continue to find new ways to create value across different organisational functions.

Integration with other emerging technologies

The full potential of Gen AI may be realised through integration with other emerging technologies such as blockchain, Internet of Things (IoT), and extended reality (XR). These labs provide the expertise needed to explore these intersections and develop novel applications.

Generative AI has to be weaved across the technology stack supporting existing systems and processes while accelerating future outcomes. This holistic approach to technology integration represents the future direction for many Gen AI labs.

Conclusion

Establishing a Generative AI lab is a strategic move that enables organisations to drive innovation, enhance productivity, and gain a competitive edge. While building in-house capabilities offers long-term advantages, collaborating with external partners can accelerate early-stage initiatives and provide access to specialised expertise. The key lies in aligning the lab’s outputs with business goals, ensuring responsible development, and enabling integration with core systems. 

Success depends on understanding both the potential and limitations of Gen AI and building capabilities that are not only technically sound but also practically implementable. As new applications emerge, these labs will play an increasingly important role in shaping business transformation. Contact us to explore how we can help you build or co-create your Generative AI lab.

References:

1. IT, amplified: AI elevates the reach (and remit) of the tech function https://www2.deloitte.com/us/en/insights/focus/tech-trends/2025/tech-trends-future-of-ai-for-it.html   
2. Deloitte’s GenAI Lab Programme 
   https://www.deloitte.com/nl/en/services/consulting/services/genai-makerspace.html 
3. Fraunhofer FIT GenAI Lab 
   https://www.fit.fraunhofer.de/en/business-areas/generative-ai-lab.html 
4. How People Can Create - and Destroy - Value with Generative AI 
   https://www.bcg.com/publications/2023/how-people-create-and-destroy-value-with-gen-ai 
5. Infosys GenAI Labs 
   https://www.infosys.com/services/generative-ai/overview.html