The Economist Business Innovation Summit on Harnessing AI

Here are my selective takeaways from the Economist Business Impact Summit in London on March 21, 2024.

What is the CEO's Viewpoint on AI Applications?

The summit opened with a CEO panel discussing AI's evolving role. Key highlights included AI's efficiency in understanding customer trends and centralizing global marketing operations.

• In addition to AI-aided customer communication, AI is proving effective in understanding customer behaviors and trends.
• "We've centralized our marketing operations from 13 countries into a single office, utilizing AI for multi-language content creation."
• The key message to employees: "AI is not replacing jobs; it's augmenting the capabilities of those who use it."
• Improved customer support insights are evident, with a projection of "99% AI-driven customer support in our firm soon".
• "Our productivity gains have reached 40-50%." My personal additional comment is that this is a significant step towards OpenAI's goal of a tenfold increase.
• In future, we expect AI's to help translate customer perspectives into innovative product features.

Looking ahead, the evolution of AI in business can be mapped out in three distinct phases:

• Hyper Productivity (Current Phase): We're currently in a phase where AI is significantly enhancing efficiency and productivity.
• Hyper Personalization (Next 2 Years): The focus will shift towards tailoring experiences and services to individual customer needs and preferences.
• New Business Models by AI (3-5 Years): The future holds the potential for AI to innovate and create new business models, the specifics of which are yet to be discovered.

Appreciation and credit for these points goes to Du?an ?enkypl (Groupon), Solange Sobral (CI&T), Mark Sandys (Diageo), and Tom Lee-Devlin (Economist).

Getting Started with AI: Practical Application

A major theme of the summit was the practical initiation into AI. Echoing my previous posts, the key message from the event was to "just start using AI practically." Instead of waiting for groundbreaking strategies, it's more effective to integrate AI into everyday tasks. This could range from using AI to summarize reports for meetings, identifying key trends in data, or improving customer satisfaction.

As Peter Drucker famously said, "Culture eats Strategy for breakfast." This rings true in AI adoption. Through regular usage and practical application, businesses develop their AI capabilities, much like the concept of 'Dogfooding' for Google.

We should all be asking ourselves how AI can assist in our current projects to enhance productivity. For instance, before tackling a dense 50-page research paper, why not use AI to provide a summary first?

Of course, start with modern tools like GPT, Copilot, and Brand Guardian for better results. The output is high in quality. With AI, it's becoming challenging to distinguish between content created by humans and machines. However, an important issue is that the quality of AI's output is directly linked to the quality of data input.

An interesting discussion also emerged around AI's role in fintech and insurance data analysis, particularly a study from Oxford. The research highlighted how AI could perpetuate existing biases, such as gender disparities in lending. However, AI can also be programmed to identify and correct these biases. This leads to an important notion: rather than idealizing the past, we should use AI to rectify historical inaccuracies and biases. AI's capacity as a prediction machine makes it invaluable in identifying trends without the need for extensive reading or summarizing.

The session by Data Pioneers on harnessing business data to power AI, featuring Sonya Barlow (LMF Network), Pinar Ozcan (Oxford University), Stephanie Peterson (АХА), and Max Speur (Sabanci Holdings), provided insightful perspectives on this theme.

Amazon on Operations with AI:

Amazon's approach to integrating AI into its operations centers on prioritizing business purpose and customer needs. A key strategy is tackling simple yet impactful projects, like optimizing inventory placement to facilitate regional distribution. This approach is exemplified in regular sessions, where teams present ideas and current projects, which are further supported based on positive results, fostering an environment of continuous innovation and improvement.

Further elaborating on Amazon's use of AI:

• The Context: They ambitiously aim to double their operational size, focusing on connecting different regions and strategically storing inventory in the most optimal locations.
• Where and What to Stock: Personalization plays a significant role in their strategy, using AI to analyze patterns in similar products to anticipate demand for new items.
• Sustainability and AI: Importantly, Amazon is leveraging AI for sustainability. Using algorithms to minimize waste creates a win-win situation where environmental responsibility aligns with operational efficiency.

Special thanks to Steven Armato of Amazon and Tom Lee-Devlin from The Economist for their insights on harnessing AI for Amazon's operational networks.

Cybersecurity, Drawbacks, and Perils:

Cyber: Nicole Eagan, Chief Strategy Officer at Darktrace, provided some illuminating insights on cybersecurity. AI is increasingly becoming a tool for empowering cyberattacks, with sophisticated phishing techniques so refined that they're indistinguishable and infiltrating platforms like Slack and Teams, not just email. The sheer volume of these threats is beyond the capacity of traditional IT groups, suggesting that AI at a personal level on individual machines might be the only effective defense.

Drawbacks: Addressing the drawbacks and cautions of AI, it was noted that while initial gains are evident, there could be a long-term reduction in innovation capacity. A study on robotics in Spain likened the situation to the reliance on tools like Google Maps, which, while beneficial, might detract from developing deeper strategies and data handling. Safety is paramount, as demonstrated by Amex's approach of internally testing AI chatbots before external deployment. This point was underscored by Prof Bilal Gokpinar (University College London), Kerry Sheehan (UK Regulation Taskforce), Alexander Drummond Express), @Beatrice York (afiniti.com ), and Jonathan Birdwell (Economist Impact).

Emerging Tech - Opportunity and Perils: Furthermore, discussions about the power and perils of emerging technologies highlighted the importance of regulation in this rapidly evolving field. Technologies under scrutiny included brain-computer interfaces (even non-invasive ear versions), vector databases, and quantum computing, which could revolutionize or jeopardize cybersecurity. Blockchain technology, meanwhile, presents a potential defense against deepfakes. The use of ethical hackers within organizations and the application of supercomputers for simulating weather and climate change were also topics of interest. Experts contributing to this discussion included Julian David (Tech UK), Nicole Eagan (Darktrace), Naina Bhattacharya (Danone), Dr Christina Yan Zhang (The Metaverse Institute), and Arjun Ramani (The Economist).

Creativity and AI: Philip Davies (Siegel+Gale) told us that human judgment has a role in the creative process, even with AI. We also learned about IBM's role in naming Stanley Kubrick's HAL 9000.

Writers are already working with AI, and AI needs input from human judgment. My additive comment is that the human part will necessarily become smaller in order to take on bigger challenges, otherwise, the bigger gains for humanity will stay elusive. And what a great message on simplicity punctuated with Mick Jagger's and the Rolling Stones' message to Andy Warhol! Very enjoyable!

Purpose versus Profit: One of the concluding topics at the summit was "On balancing Profit and Purpose," a session I enjoyed participating in. The discussion covered various aspects:

• The dialogue underscored a natural balance between harnessing AI's role in delivering cost-effective products and services versus the potential for ethical abuses. This is a balance that companies like Google (recall their original motto: Don't Be Evil) and OpenAI have already experienced.
• A resonating comment was made: the past wasn't perfect, and idealizing it shouldn't lead us to demonize the future. Historical biases, now acknowledged, should be actively reduced in the future.
• A continuing concern in AI adoption is job loss, which remains a fear and barrier to adoption within many organizations. Only some stakeholders are immediately on board, while others will wait primarily based on employment concerns and dystopian fears.
• Additionally, I emphasized the growing issue of misinformation and the erosion of truth as major threats stemming from AI. This challenge extends beyond societal impacts to corporate realms, affecting customer perceptions and company trust. The critical question is, how can companies ensure their messages are trusted and valued in an era where skepticism is rampant?

Thanks to fellow panelists Chandrima Ganguly, PhD , Diverse AI; Roger Rohatgi , BP; Philip Davies ; Marloes Pomp European AI Forum, with moderation from John Ferguson of the Economist.

If we play this all forward, we will see (in my opinion)

• i) a super acceleration in marketing, code development, and customer service.
• ii) the speed-up of everything. Disrupt or be disrupted. and
• iii) Investors will pay attention to the leaders who do a great job using AI tools.?

And finally, a big thank you for all of the many side conversations.

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It has been great to participate in the The Economist Business Innovation Summit! https://lnkd.in/dZuBEGGE #EconBIS

ROSA ARANDA Barrio Geoffroy Gérard , Rafif Srour , Guillermo de Haro Rodríguez , Santiago Iniguez Paris de l'Etraz, PhD , Juan José Güemes Christopher Thompson, Pamela Rolfe Diego del Alcázar Benjumea Isabela Alcázar PhD Manuel Mu?iz