3 ways to secure the best AI partner for your business

Here's how to sort the wheat from the chaff and uncover an IT partner that can help your organization make the most of AI technology.
Written by Mark Samuels, Contributor
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Deciding to dabble in AI is just the starting point. If you're going to use generative AI and other emerging technologies in your business, you'll need to make sure you have a platform that allows you to exploit data safely and effectively. 

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Those kinds of platforms are likely to be provided by an external technology provider. So, how are businesses sorting the wheat from the chaff when it comes to AI partners, and what does a great technology partner look like? Three business leaders give us their views.

1. Focus on your use case 

Carter Cousineau, vice president of data and model governance with Thomson Reuters, recognizes that sorting the wheat from the chaff is a challenge, particularly in areas that have developed at a rapid pace during the past 12 months. 

"On generative AI, there's a bit of a mix right now," she says. "There's a lot of hype and I'm personally very curious about what use cases will stick." 

Such is the level of hype that Gartner recently placed generative AI at the peak of inflated expectations on its Hype Cycle for Emerging Technologies, 2023.

The tech analyst says the scale and rapid adoption of generative AI applications is heralding a new wave of workforce productivity and machine creativity.

Also: 4 ways generative AI can stimulate the creator economy

Cousineau believes professionals should ride this wave of data-led innovation while also thinking carefully about what the implementation of AI and large language models (LLMs) means for their business, especially when they're looking to build or buy technology.

"My first question to many professionals is, 'do you need a large language model?'" she says. "Because the cost to do that can be quite significant. So, you want to be careful that it's an area where you need to invest in a large language model." 

As with any other technology purchase, your starting point for investing in AI systems and services should be a clear business case. 

Cousineau says Thomson Reuters is exploring a range of use cases and her team works with people across the organization to ensure data governance is prioritized.

"We look at these tools from an ethics and harm-mitigation standpoint," she says. "Depending on the use case, we look at what's happening and try to mitigate potential concerns quickly."

Thomson Reuters already has a couple of key vendor partnerships. Enterprise information is stored in the Snowflake Data Cloud as a single source of truth for data-led innovations. 

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Cousineau also refers to her company's nascent partnership with Microsoft Copilot, which is an AI assistant that helps professionals create documents, summarize presentations, and more 

"There are use cases and we're just in the final stages of sorting that work out. And we're also looking at some of our existing products and thinking how we can take some of the bones and build a different feature or capability that would better service customers," she says.

"Even when we use LLMs internally, it's very important that our staff have a safe environment to use this technology. So, we look at it from the view of our external customers and our employees, and we look at supporting all LLM environments."

2. Find a flexible partner 

Tulia Plumettaz, director of machine learning at e-commerce giant Wayfair, recognizes that one of the thorny questions for business leaders to consider when it comes to AI is whether it's better to put an early stake in the ground or to play more of a waiting game.

Go in too early and you risk spending too much money with one partner that gets left behind as the market moves on. But, go in too late and your competitors could leave you behind.

Add in a series of other considerations, such as vendor lock-in and concerns around the exploitation of enterprise data assets, and business leaders face a tricky conundrum.

Also: Generative AI will far surpass what ChatGPT can do. Here's everything on how the tech advances

"Think about Gen AI today," she says. "There are big hurdles and questions around data ownership, such as who owns the asset and are the big vendors going to use your data for training their own models or are they not. That's something we're focusing on now. We are learning the landscape when it comes to data and legality in that space." 

Plumettaz explained to ZDNET recently how her company is working with Snorkel AI to boost the online search experience for consumers -- and just as Wayfair is dabbling in machine learning, so the company is exploring other fast-emerging areas.

"We are exploring applications of conversational AI," she says. "There are definitely use cases in our space that we believe we can get into quickly."

Wayfair and Snorkel have created an integrated relationship that's allowing Plumettaz and her business colleagues to think carefully about potential use cases.

"With Snorkel, we're taking a much more long-term view on this space, which is about trying to understand the differentiator at an enterprise level," she says. 

Plumettaz is working with Snorkel to develop foundational models that will help the company make use of its key data assets, such as the products it sells and their defining characteristics.

Also: The ethics of generative AI: How we can harness this powerful technology

With this structure in place, Wayfair will be able to think about how other AI specialists might help the business meet its long-term aims.

"We're focused on understanding where this field is going while also carving out low-hanging fruit," she says. 

"We're asking, 'what is going to be the one thing that makes the data asset that Wayfair has into a unique value unlock that we are not going to get with a generic, off-the-shelf product?'" 

3. Stay open to experimentation 

While generative AI is getting a lot of media attention right now, Lalo Luna, global head of strategy and insights at Heineken, says business leaders must recognize that ChatGPT and other conversational bots are far from the only AI game in town.

"I think companies need to be more concerned about how they're going to adopt and embrace, not only AI but also traditional machine learning and other data-intensive processes," he says.

Also: Generative AI and the fourth why: Building trust with your customer

That's what Luna is prioritizing at Heineken, where his team is using Stravito's enterprise insights platform to share insights through an internally branded platform, known as Knowledge & Insight Management (KIM).

Even though the power of data is recognized by his business peers, Luna believes some other companies are still lagging when it comes to understanding the value of information. 

Rather than waiting for market leaders to emerge, he says now is the time for professionals to start working out which vendors will help their organizations.

"Data and AI is already a competitive advantage," says Luna. 

"Business leaders shouldn't be afraid of the technology. They need to be concerned about how they are going to upskill their people, and how they are going to build technological ecosystems that help, not only their consumers, but also their internal people to make better decisions."

Also: 5 ways to sell your game-changing idea to the rest of the business

Stravito recently announced it has added a proprietary generative AI engine to provide businesses with verified insights in a more engaging way.

Luna says it's important to stay open to new ideas in AI -- and he envisages Stravito being a key player in helping his company make the most of the thousands of reports it holds.

"More and more we need to jump in into these kinds of things," he says. "Success is all about jumping to the experimentation phase."

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