GenAI's next frontier: Scaling for lasting impact

Within two months of launch, ChatGPT had 100 million active monthly users, making it the fastest-growing consumer application in history. Today, more than 2 million developers use Open AI’s technology, and more than 92% of Fortune 500 companies have integrated it into their operations ¹ .

The response from business users, from delivery to the boardroom, was immediate and enthusiastic. Many people rushed to adopt it for simple tasks — rewriting emails, drafting customer service responses and automating repetitive communications — hailing it as a welcome productivity booster.

However, as the initial novelty wore off, it became clear that people were only scratching the surface of what generative AI could do. Individuals quickly embraced chatbots like ChatGPT, Google Bard (now Gemini) and Microsoft Bing Chat (now Copilot) to save time, but individual adoption wasn’t translating into strategic efforts. Organizations were slow to integrate this technology into broader strategy and operations.

In fact, employees were often concealing their use of the tools. “People were using it, but they were hiding it from their executives,” says Paul Varlet, Strategy Partner at Valtech. “Now that companies are officially adopting GenAI, the challenge is getting people to use it strategically, not just for simple tasks.”

This shift from individual experimentation to organizational adoption is crucial for unlocking the full potential of AI to revolutionize customer experiences, optimize operations and ultimately foster greater human-AI collaboration across industries.

Proprietary data: Essential fuel for the journey

Scaling GenAI for true impact requires more than just enthusiasm. It needs data, and lots of it.

“If you don’t‌ have any data to build out the thing, it’s going to go nowhere,” says Richard Bownes, Lead Data Architect at Valtech. “It’s like the petrol for your car. You’ve just got an empty Ferrari without your data source.”

LS Eleven, an ambitious digital initiative launched by UK supermarket chain Asda, provides a great example of this principle in action. LS Eleven, powered by data from the Asda Rewards loyalty program, empowers brands to unlock more focused media planning and seamless omnichannel experiences.

“A lot of retail organizations have heaps of data, but it’s fragmented and disconnected,” says Qaiser Mazhar, Chief Technical Officer of LS Eleven. “Our major initiative is about consolidating all of that data, normalizing it so we can get the value from it and ultimately provide immense value to our customers.”

AI-driven insights enable Asda to offer tailored promotions, product recommendations and cohesive purchasing journeys that are highly relevant to individual customers. The future of retail is about “what we used to have,” suggests Mazhar, with AI acting like “a local store owner who knows you, but now digitally and at scale.”

David DeCheser, Global Chief Creative Officer at Valtech, predicts GenAI’s dependence on data could turn the traditional innovator’s dilemma on its head, arguing that GenAI's dependence on data hands an early advantage to industry giants over more-nimble newcomers. Those large and traditionally slow-moving enterprises own the most valuable, specialized data — the critical asset that startups lack.

But that advantage isn’t guaranteed to last, and managing all that data also presents its own challenges. Without the right infrastructure and data governance, even large organizations can find it difficult to harness AI effectively. “Legacy organizations need to correct their data foundations to properly leverage it — never mind putting in all of the governance needed to ensure it’s used in the right way,” DeCheser adds.

Although data is a critical piece of the GenAI puzzle, DeCheser warns against the pursuit of “perfect data” which can lead to analysis paralysis. “While a solid data foundation is crucial, it’s not the be-all and end-all — nor should it be a roadblock. GenAI’s iterative nature lets you start small, learn improve data as you go and stay agile.”'

Blazing new trails in AI-powered customer journeys

Delivering a truly personalized, data-driven customer experience at scale requires a new level of intelligence. By fine-tuning each interaction, AI is empowering companies to shape customer journeys that anticipate needs, including complex searches.

For companies with vast product inventories, providing an efficient and intuitive search experience is critical. CPC Farnell, a global distributor of electronic components and part of the Avnet group, faced the challenge of helping customers navigate an inventory of more than 100,000 products.

To address this, CPC Farnell implemented an AI-powered search solution using Google Vertex AI.

“Ultimately, it's really trying to guide the customer and do the heavy lifting for them to make sure they get the right product,” says Nick Townend, Director of Product – eCommerce at CPC Farnell, part of the Avnet Group, describing how AI transformed their search functionality. “With over 100,000 products in the CPC group and over a million in Farnell, it’s like trying to find a needle in a haystack. AI can reference previous applications, look at associated products and marry two products together or ensure compatibility.”

Townend further elaborates on how AI enhances the search process through natural language understanding: “It stems back to natural language because customers can talk to us as if they were talking to a sales representative. So you can tell it, this is what I want to build, this is the purpose of the product. And then we can take a suggestion back from the AI engine and you can build the conversation there.”

The AI-driven search system improves product discovery and boosts conversion rates and revenue. By analyzing customer queries and product attributes, the AI engine provides more relevant results, reducing the frustration of “no results” searches.

Charting a course for greater strategic impact

As companies continue scaling their AI efforts, the focus is shifting from routine tasks to high-value initiatives. This phase is where GenAI moves from support role to strategic powerhouse.

For example, Syngenta, a global leader in agricultural science, partnered with Valtech to embed agentic AI into its Golden Harvest Experience (GHX) app. This integration allows the app to deliver real-time, personalized farming recommendations, such as identifying the best seeds to plant under specific conditions. Central to this innovation is Cropwise AI, an advanced platform designed to support data-driven decision-making.

Zachary Marston, Digital Product Manager for Computational Agronomy at Syngenta, explains: “We use large language models as a natural language interface, allowing farmers to ask questions. Behind the scenes, these models act as agents, selecting the right tools and workflows to complete tasks based on user input.”

However, as with any cutting-edge technology, there were challenges. “Some early tools we tested didn’t meet our needs,” Marston notes. “We had to adapt both our strategies and the use cases to fully leverage the technology’s strengths.” This iterative process helped Syngenta refine how AI integrates with their app, allowing it to deliver more relevant, actionable insights to growers, agronomists and account professionals.

The AI platform within the GHX app offers detailed information on crop protection, seed performance and optimal growing conditions. This real-time guidance enables users to make more informed decisions, improving crop yields and promoting sustainable practices. Marston is quick to point out that the goal isn’t to replace people but rather to enhance their expertise. “It’s about giving them better tools to make more effective decisions. We’re helping them do what they’ve always done, but in more informed, more efficient ways.”

This collaboration between human insight and AI’s data-driven power is critical, says Lindsay Ratcliffe, MD UK and SVP Product, Innovation and Data Services Europe at Valtech. “The challenge is to find a balance where AI enables and speeds up great work, while leaving space for innovation and creativity.”

Ultimately, AI serves as a powerful partner, amplifying human creativity and judgment. As DeCheser adds: “AI doesn’t replace experience or taste. You still need human intuition to differentiate between what’s mediocre and what’s great.”

Expanding horizons

As organizations evolve past the initial excitement of generative AI, the focus has shifted to unlocking its full potential by scaling it across all facets of operations.

GenAI is no longer just a tool for automating simple tasks but a strategic driver for industry transformation, enhancing customer experiences and fueling sustainable growth. To truly capitalize on this technology, companies must invest in robust data infrastructure, foster meaningful human-AI collaboration, and apply AI solutions that create tangible value for both customers and employees.

By approaching AI adoption strategically, organizations can stay ahead of the curve and lead the next wave of innovation.

 

¹ Sam Altman, OpenAI DevDay Keynote 2023: www.youtube.com/watch?v=U9mJuUkhUzk