Leading the way with AI

Peter Bell at Twilio describes how the world’s biggest companies are harnessing Artificial Intelligence

Artificial Intelligence (AI) is behind a lot of the innovation we are seeing in 2024 – from using generative AI tools like Chat GPT for everyday content creation to potentially revolutionising industries.

But despite its new and exciting applications, it’s decades-old and well-established. What was once a technology exclusively for early adopters has recently taken a stratospheric leap forward thanks to the development of Large Language Models (LLMs), catapulting AI onto the mainstream agenda.

Unlike many digital transformation initiatives, it’s the large-scale Fortune 500 companies who are leading the AI revolution. Coca-Cola, for example, partnered with Bain & Company to invest in the latest OpenAI technologies. Artists were invited to use the brand’s iconic assets to create their art, using AI to drive responsive, personalised brand experiences. The results? 10-30 times faster concept iteration, and 38% higher messaging resonance.

Such transformative use cases challenge the notion that large, well-established brands are slow to adopt emerging technologies. While startups have had the advantage of agility and are not shackled by legacy tech, when it comes to AI, it’s all about data resources - something larger corporations have in spades.

With this in mind, can smaller enterprises truly keep pace, and if so, what can they learn from the “Fortune 500 advantage”?

AI frontrunners stealing the show

Plenty of large-scale brands are making great use of AI and yielding tangible, real-world benefits for consumers. So, we’ve picked out some of our favourite examples.

Walmart

Walmart has been leveraging Natural Language Processing (NLP) to refine its website search results for years. Recently, they added voice recognition to their ordering system, allowing customers to use voice assistants, like Alexa, to order household items. The AI tool complements these requests, using past purchasing history to determine the customers’ preferred brand and size to place in the cart.

In-store, Walmart uses a similar voice recognition solution for its employees. The “Ask Sam” app deploys voice recognition and AI chatbot technology for knowledge-sharing purposes, ensuring work schedules or product locations are easily accessible.

Walmart’s integration of AI and voice recognition technology enhances both customer experience and employee efficiency, showcasing its commitment to innovation in retail.

Amazon

Amazon’s use of AI ranges from the predictable to the truly futuristic. Many consumers will already be using their AI-backed summarised product reviews feature, which concisely summarises the most common pros and cons outlined in customer feedback. 

However, some lesser-known and advanced use cases include the palm-based payment system Amazon One. This new kind of biometric identification uses GenAI images of the hand’s surface and veins to identify shoppers with the tap of a palm, eliminating the need for a digital wallet or traditional payment methods.

Apple

Apple has long been a pioneer in AI, with its Siri voice assistant becoming a household name. However, it has recently announced ambitious plans to integrate AI capabilities across its ecosystem.

Collaborating with OpenAI, Apple unveiled “Apple Intelligence,” a collection of AI-driven features aimed at enhancing user experiences across their devices. Tools can help remove unwanted objects from photos, summarise articles and emails, and format, proofread, and draft text – to name just a few.

Siri is also getting some significant upgrades that will make it more context-aware and adept at managing complex queries through advanced natural language processing.

Data quality and the “Fortune-500 advantage”

While AI successes are often linked to Fortune 500 companies, businesses of all sizes are enhancing their AI capabilities by leveraging the customer data they already collect. However, the true enabler of AI is the quality of that data - an obstacle all companies must address to ensure success. 

AI is only as effective as the data it runs on. If the data fed into AI models is old, inaccurate, or biased, the output will reflect those flaws. In the end, the quality of the results is only as strong as the quality of the input. 

For example, in healthcare scenarios, artificial intelligence has the potential to detect and diagnose patients at a rapid rate for earlier, more proactive treatment. But with reward there is considerable risk – and the stakes are high. MIT Technology Review found multiple examples of AI reaching the wrong diagnosis due to mislabelled data or data they couldn’t trace to a known source.

And it’s not a straightforward issue to course correct; there are plenty of culprits behind “bad data” - from data being trapped in disparate silos in your tech stack, to data processing delays.

Good data is consolidated, consistent across systems, unique (i.e., no duplicate entries), compliant with privacy regulations, and updated in real time. To achieve this, organisations need just a few key capabilities in their tech stack, including being able to: easily integrate new tools, leverage real-time event pipelines, automate quality assurance checks, and ensure data pipeline observability. 

With this foundation, businesses can better train their AI and machine learning models. For example, historical data stored in a warehouse can be leveraged to make predictions about customer behaviour and preferences, which can then inform all customer engagements from marketing, sales, and customer service perspectives.

AI best practice – for companies of all sizes

It isn’t a question of if you should be leveraging AI, but a matter of how you plan to do so. While AI promises to help accelerate processes and unlock advanced insights that would otherwise be left untapped, it’s clear is that data is the silver bullet to truly unlocking its potential. While larger companies have the advantage of more data to train their models on, the quality of that data is what organisations need to be prioritising.

By focusing on data hygiene, privacy compliance, and interoperability, you can tap into the innovative potential of artificial intelligence – no matter the size of your organisation.

Peter Bell is VP of Marketing EMEA at Twilio 

Main image courtesy of iStockPhoto.com and ookawa