5 Key Takeaways from IBM’s Service Partner Exchange London 2024

What an extraordinary AI London event! I've recently returned from an incredible day fully immersed in everything Generative AI at IBM’s exclusive Service Partner Exchange in London. The event was a hub of energy, with attendees from consultancies and system integrators that work with IBM technology exploring how to move from experimenting with generative AI to large scale production.

The impact of Generative AI on business is undeniable, and the insights and knowledge gained from this diverse group are invaluable. Whilst multiple studies show that few GenAI pilots make it into production, the message was clear that service partners can help move their clients into large scale production, by addressing governance, indemnity, performance and cost challenges. The event was a testament to Generative AI's transformative power and the almost limitless business potential of AI when it is implemented correctly.

But what truly grabbed my attention?

My top five key takeaways from IBM’s AI Service Partner Exchange London 2024.

1. The analyst's view of how to get GenAI from Pilot to Production

Neil Ward-Dutton , VP Automation, Analytics and AI EMEA at IDC presented some fascinating findings based on research from IDC based on UK businesses.

IDC research shows that a whopping 68% of UK organizations are already investing significantly in Generative AI, or have already implemented use cases highlighting the value they place on AI technology. However, the level of investment is not evenly spread. For example, 78% of finance organizations are either investing significantly or have already implemented use cases, whilst Government / Education is furthest behind; 50% are still focused on initial testing and 8% have not started. 

Manufacturing organizations’ progress is modest; 39% are focused on initial experimentation, whilst retail / wholesale and transportation / media and utilities are broadly similar, with 28%-30% experimenting. 

So, whilst we have significant progress in particular industries and verticals, the rate of adoption shows that there is more progress and opportunity available to many others.

68% of UK organizations are investing significantly in GenAI, or have already implemented use cases.        

2. Moving pilots out of the sandbox and into the real world

Currently only 5% of Generative AI pilots make it from pilot to production. Why is that? According to IBM's Hans-Petter Dalen , this is mainly due to the fact that most generative AI pilots are not tied to business outcomes.  As a result, businesses rarely adopt AI, which is supposed to help them, pilots go nowhere, and data scientists grow increasingly frustrated as their work never makes it to production. 

So, how can organizations fix this?

There is no single ‘one size fits all solution’ to heal all ills – just like no one LLM model answers every business need.  Scaling pilots to production involves aligning pilots with real business problems and outcomes, successfully navigating regulations, and ensuring the right architecture, security, and governance are in place. In addition, the right model (which may, in fact, be a small language model), expert guidance, and active cost management and business engagement are also key to unlocking the full potential of Generative AI.

But it doesn’t stop there—leadership from the top and change management are crucial components too.  All this brings change management challenges, which may not readily map to organisations who want to move rapidly. Still, each item needs to be addressed to scale Generative AI successfully.

Only 5% of generative AI pilots make it into production.        

3. The best approach to AI Governance and AI Indemnity, Performance and Cost

A recurring event theme was the importance of 'business trust' and the idea that innovation accelerates when there is confidence in the processes that shape our technological and business futures.

This highlights the need for transparency in how generative AI operates and emphasizes the crucial role of governance in building trust and ensuring the success of generative AI in business.

Currently, many see ChatGPT as the solution but struggle to identify the underlying 'business question' it, or other LLMs, should address. Choosing the right LLM takes time and consideration. The cost, performance, and risk levels of large language models (LLMs) vary widely. Smaller Language models may be more suitable for specific tasks, balancing the cost, quality, ESG impact and speed of responses.  In particular model costs can vary by up to 50x for the same business use case depending on the LLM used.  These factors significantly impact the overall cost and effectiveness of AI solutions and therefore, time should be taken to pick the right model.

86% agree or strongly agree “GenAI is a major new corporate workload like ERP or ecommerce that will require an incremental increase in technology spending in the next several years.”        

4. The Importance of Data Security, Compliance, Legislation and Regulation:

There was a fascinating conversation on the legal implications of introducing generative AI and AI into business processes. Implementing and embedding multiple AI models securely poses significant challenges, particularly as industry regulations, the EU AI Act, and other legislation evolves.

Companies must clearly understand what they are implementing and continuously maintain their risk and security posture—it’s not a one-time task. Legal considerations, such as what actions are permissible with a given model, require careful oversight, and companies need robust evidence and audit trails to ensure compliance and accountability.

Therefore, effective AI implementation requires continuous monitoring and a robust governance framework that evolves alongside the organization. Governance platforms like IBM watsonx.governance, built on IBM’s extensive AI expertise and research background, helps future-proof service partners by providing a collaborative environment with comprehensive audit trails that protect the organization's reputation.

Notably, IBM’s offering includes legal indemnity, reflecting their confidence in their responsible deployment of generative AI. This is something few, if anyone else, offers but needs to be part of your consideration criteria when picking a partner and platform.

A holistic approach ensures that AI tools are not only compliant and secure but also effectively contribute to achieving strategic business goals.

5. Whilst Generative AI has grabbed all the headlines, general Artificial Intelligence still matters

There is huge excitement around Generative AI. It is rightly seen as a strong transformative force in the UK, with 86% of UK organizations either agreeing or strongly agreeing that "GenAI is a major new corporate workload like ERP or e-commerce that will require an incremental increase in technology spending in the next several years."  Organizations are right to be excited, as Generative AI's potential benefits are huge. However, generative AI still lags behind AI in overall AI investments.

Research shows that while 36% of surveyed organizations are using Generative AI to create new code or content, 34% of UK organizations interviewed by IDC have invested in predictive analytics, such as retention analysis, next best action recommendations, customer lifetime value predictions, or time series analysis to determine the optimal number of agents in a call centre. Additionally, 30% of UK organizations are using interpretive AI—analyzing unstructured data like images, videos, and language—to uncover patterns within their data.

For example, a financial services company might use interpretive AI to analyze customer feedback from emails and social media, identifying sentiment trends that inform product development, marketing strategies, and customer service enhancements. Similarly, healthcare organizations are leveraging interpretive AI to process medical imaging data, enhancing the accuracy and efficiency of disease detection.

Both Generative and interpretive AI will be transformational for organizations across industries. However, whether an organization invests in Generative AI, other forms of AI, or both, each application must be governed appropriately. Proper governance ensures that AI systems operate responsibly, ethically, and in alignment with regulations, safeguarding both organizational integrity and public trust.

Only 8% of UK businesses say say they are not doing anything significant (with GenAI) yet.        

Moving from GenAI Experiments to Large Scale Production        

With over 5,000 Large Language Models (LLMs) available on the market, selecting the right model can be overwhelming and highly consequential for organizations of all sizes. Models vary widely not only in their capabilities and suitability for different tasks but also in their costs, which can differ by as much as 50 times for performing the same business process. Therefore, choosing the right model most appropriate to your with business needs, good governance, and picking the right service partner to support you, becomes critical.

AI and generative AI are transformational but, like any large technology, they need to be implemented in the right way. Taking more time to engage the right people, partner and technology at the start of an AI program can feel like a slow start to transformational change. However, the consequences of not building the right foundations from day zero means your AI house will likely be built upon a foundation of sand and become one of the 95% that don’t make it to production.  Sometimes going slower to go faster is the only way to win in business, and that is very true of generative AI.

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