AI Summit London 2024: Highlights

AI For Business


Like any digital transformation, adopting and implementing AI is difficult and requires more than the technology itself to be successful. Below are some of the learnings and recommendations VML Intelligence took away from this year's summit:

Built for the future capabilities of AI: “You should build with what AI can do in the future in mind, not what it can do now,” says Colin Jarvis of OpenAI, noting that developments are changing so rapidly that by the time you build something, the field will already have advanced.

AI alone is not a differentiator: AI is quickly becoming a necessity, and the technology itself won't be the differentiator — it's the user experience, unique data and services you bring to it that will be the differentiator, Jarvis said.

Balancing human control and automation in user experience: Speakers noted that AI will need to take over from human expertise in certain cases where processes or decisions are more complex or require more empathy. Tim Bond, Associate Director of Media at IPSOS, quipped that AI is “an exoskeleton that needs a human at its heart, otherwise it's just a bag of bones.”

Don't chase novelty; have clear goals. Sid Choudhury and Sawat Choudhury of Mars Wrigley's digital commerce division noted that marketers often make the mistake of “chasing shiny toys” rather than prioritizing outcomes. Fractional CTO Adil Asif said many AI implementation projects fail due to a lack of clear goals or poor decision-making. It's important to remember that AI is not software and is not iterative or sequential, he said. “AI is more about discovery than development, and we as business leaders should conceptualize it that way.”

Consider the unintended consequences: Daniel Hulme, chief AI officer at WPP, cautioned that AI won't solve every problem. “Just because you can doesn't mean you should,” Adam & Eve's Sarah Chapman agreed. Both suggested it's important to consider the knock-on effects of AI on your supply chain and your team. As Chapman noted, “we typically give the simple tasks to juniors,” but at the same time, it also provides a respite from intense work. Want to automate? all Is it boring?

Ethical, transparent and accountable: Responsible AI is becoming mainstream as companies launch task forces, draft key principles, and organize committees to oversee AI adoption, but there is still work to be done. Daniel Hume said there should be a focus on transparency, noting that “AI is currently unexplainable” and intent needs to be made easier to determine. Emma Di Orio, Global Data Privacy Officer at Diageo, argued that data privacy should be seen as a business driver, a path to trust and competitive advantage. Finally, Alyssa Lefebvre Shkopac of the Responsible AI Institute agreed that “trustworthy AI will win in the marketplace.”



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