My Top 5 Lessons on Scaling AI and Hybrid Cloud from IBM Think London 2024

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My Top 5 Lessons on Scaling AI and Hybrid Cloud from IBM Think London 2024

Adopting AI is no longer a competitive advantage—it’s a survival strategy.

The IBM Think London 2024 event spotlighted how AI, hybrid cloud, and data governance are driving measurable business transformation. With global AI investments set to hit $500 billion by 2024[1] and Gartner forecasting 85% of businesses integrating AI into operations by 2025[2], the message is clear: act now or risk falling behind. From enabling productivity gains to solving complex data governance issues, AI is the engine powering the next phase of digital transformation.

The Think London 2024 event provided a comprehensive roadmap for scaling AI from pilots to full-scale production, showcasing real-world success stories, best practices, and a glimpse into the future of AI technology.  But where were my top five takeaways and what actionable insights should every business leader should consider?


1. AI and Hybrid Cloud: The Strategic Pillars for Growth

The opening keynote, delivered by Kareem Yusuf, IBM’s Senior VP, set the tone for the event. He emphasized that the time for widespread business adoption of generative AI has arrived. As company leaders race to fulfil AI’s potential to improve productivity and boost innovation, they are shifting from experimenting with AI to deploying and infusing it across their businesses. According to McKinsey[3], early adopters of AI see saw substantial improvements in profitability and cost reductions compared to those that don’t.

Kareem Yusuf, IBM Senior VP

At the forefront of this shift are efforts to reinvent customer service, modernize countless lines of code, and automate workflows. But success demands more than identifying the right use cases; it depends on having an open and trusted technology architecture – built on a hybrid-by-design cloud infrastructure – that scales the secure and compliant use of AI models across multiple IT environments.

Real businesses are benefiting from scale AI. Andy Hard, from University Hospitals Coventry, demonstrated how his organization is leveraging AI to streamline operations in the healthcare sector, delivering real-world benefits like reducing patient wait times by 20%. This is particularly significant given that healthcare, a traditionally conservative industry, is now embracing AI to meet growing demand and operational challenges. This example made it clear that AI isn’t just about innovation for its own sake—it’s about the importance of solving real business problems in real time.

Andy Hard, from University Hospitals Coventry

2. watsonx Assistants: The Productivity Revolution

Eliot Frederiksen’s session on watsonx assistants was another standout moment for me. AI’s ability to augment human capabilities and reduce the burden of repetitive tasks is where its true value lies. Leading-edge companies are outperforming their competition in profitability, innovation, and revenue growth using generative AI assistants to transform how work gets done.

Speakers, Frederiksen and Parul Mishra explained how watsonx assistants have been transformative for many businesses, particularly in reducing customer response times by up to 40%, an improvement that significantly enhances the customer experience.

Parul Mishra Vice President, Product Management, IBM watsonx Assistants and Business Automation

What’s remarkable about this is how watsonx assistants don’t just automate simple tasks—they intelligently understand and respond to complex customer queries. This has led to more than just efficiency; it has driven higher satisfaction levels, allowing employees to redirect their focus to higher-value work. According to a 2022 study by Accenture, AI-driven automation could boost productivity in some sectors by as much as 40% over the next decade[4]. This highlights the scalability and immediate ROI that AI offers, particularly when deployed in customer-facing roles.


3. Building AI-Ready Architecture: The Backbone for Scaling AI

As generative AI continues to expand, the volume of data is skyrocketing, with stored data projected to increase by over 250% in the next five years. Organizations need to harness this ever multiplying data to train, test, and refine their AI models, but they must place a strong focus on governance and security if they are to succeed getting AI into production at scale. As businesses integrate AI, their infrastructure must adapt to handle the increasing demands of AI workloads efficiently.

Steve Wallin, Aman Thind, and Oliver Presland

Steve Wallin, Aman Thind, and Oliver Presland’s session on building AI-ready architecture drove home the importance of having a flexible and scalable hybrid cloud infrastructure. State Street Bank, for example, enhanced its AI-driven risk management system by 30% using a hybrid cloud architecture designed specifically for generative AI. This improvement is critical in the highly regulated financial sector, where timely and accurate risk assessments are crucial for maintaining compliance and customer trust. The fact that they were able to leverage AI to streamline risk management—an area traditionally burdened with complexity—demonstrates how AI can drive significant improvements even in conservative industries like finance.

In addition to risk management, many industries can benefit from AI-ready architectures. The retail industry, for instance, can use AI to enhance supply chain efficiency and improve personalized customer experiences. As AI becomes more embedded in daily operations, businesses need to ensure their infrastructure is robust enough to support AI’s ever-growing demands. The message here was clear: you can’t scale AI without the right architecture in place.


4. AI Governance: The Key to Trust and Compliance

With increasing regulations such as the EU AI Act and GDPR, companies must implement AI in a way that ensures compliance, security, and ethical integrity. Kristen Bennie from Barclays and Matthew Candy from IBM Consulting made a strong case for the importance of governance frameworks.

They highlighted the sobering statistic that 62% of organizations struggle to scale AI because of governance issues. Without proper oversight, businesses risk exposing themselves to legal liabilities, data privacy violations, and potential reputational damage. IBM’s watsonx.governance platform stood out as a solution designed to address these challenges, offering transparency, auditability, and data security at every stage of AI deployment.

Furthermore, Bennie shared how Barclays has integrated AI governance into their operational model, ensuring that every AI solution meets strict regulatory and ethical standards. For businesses looking to scale AI, it’s crucial to embed governance from the start. As AI becomes more widespread, having robust governance frameworks will be a key differentiator in building trust with customers, regulators, and the public.


5. Scaling Generative AI: Moving Beyond Pilots

A statistic that stood out during the event was that only 5% of AI pilots make it into full-scale production. [CC1] This figure, shared during a session on scaling generative AI, illustrates the challenge many businesses face: they’re stuck in experimentation mode. However, the #ThinkLondon sessions also provided a roadmap for overcoming this challenge.

For businesses looking to break free from the pilot phase, the key takeaway was that AI success hinges on moving beyond isolated experiments. By getting business buy in at the start, embedding AI into core business processes and ensuring proper data governance, businesses can unlock AI’s full potential and drive significant operational improvements.

IBM Think London 2024

Conclusion: The Future is Now—But Only for Those Ready to Act

IBM Think London 2024 made one thing clear: scaling AI is no longer an option—it’s a necessity. Companies that embrace AI today will be the ones leading their industries tomorrow. The event’s sessions highlighted that the journey from AI experimentation to full-scale deployment is paved with challenges, but those who invest in the right infrastructure, governance, and strategic alignment will reap enormous rewards.

With the global AI market expected to reach $1.6 trillion by 2030, the time to act is now. AI has already proven its ability to transform industries, and businesses that hesitate, risk being left behind. Whether it’s improving customer response times by 40%, enhancing risk management systems by 30%, or boosting productivity across sectors, AI is delivering tangible, measurable benefits. The insights from this event reinforced one critical message: the future belongs to those who scale AI responsibly, efficiently, sustainably and now.

End.


Author

Kieran Gilmurray | 2 * Author | 9 Time Global Award Winner | 7 Times LinkedIn Top Voice

Need my support and guidance to understand how you might you generative AI and automation in your workplace? Then Find me on social media LinkedIn | Kieran Gilmurray | Twitter | YouTube | Spotify | Apple Podcasts visit website: Https://KieranGilmurray.com or book a meeting with me.

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[1] IDC’s Worldwide AI and Generative AI Spending – Industry Outlook | IDC Blog

[2] Gartner Top 10 Strategic Technology Trends For 2020

[3] Global survey: The state of AI in 2021 | McKinsey

[4] Accenture Report: Artificial Intelligence Has Potential to Increase Corporate Profitability in 16 Industries by an Average of 38 Percent by 2035


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