What do leaders in sports and entertainment, healthcare, consulting, and financial services have in common?
First, they’re using data, AI, and cloud technologies in ways that would have been unthinkable only a few years ago. And they’re sharing their experiences from the front lines of the AI platform shift in our new Azure podcast, Leading the Shift. Their insights are sharp, practical, inspiring, and sometimes even funny, as they build, learn, and reflect on what it takes—and what it means—to deliver value during a period of unprecedented change.
Our first slate of episodes spans a range of technology and leadership topics and guests, from developers to data scientists and C-level executives. We’re hearing insights on everything from the unique opportunities of unstructured data to how generative AI is changing the nature of value creation and technology leadership.
Here are some of the topics that are bubbling to the surface in our first eight episodes.
Leaders understand that the opportunity of generative AI extends beyond content
They build trust from the start
They think of their data as a strategic asset
Leaders understand that the opportunity of generative AI extends beyond content
Many people think of generative AI in the context of process automation and content or code generation, but it can help virtually any industry or discipline better discover, translate, and understand relationships among vast amounts and types of data, whether they’re customer service touchpoints, financial reports, or potential drug candidates.
[Generative AI gives us the ability to connect] the entire customer journey. If a consumer is taking the time to write a review or call in, those become very meaningful signals and even more so when a brand knows where in the journey that consumer sits. It’s actually one of the most powerful things that brands can now tap into by leveraging AI.”
—Shirli Zelcer, Chief Data and Technology Officer, Dentsu
The multimodal capabilities of generative AI technology aid inclusivity and value creation by enabling organizations to tap into what Hiren Shukla, Global Neurodiversity and Inclusive Value Leader, EY, calls “compressed innovation,” such as the expertise of employees whose innovation potential would otherwise have gone unrecognized.
We’ve got some team members that are primarily nonverbal. Yet their technology acumen and interaction with AI is so powerful that it exceeds any average user, because they’re able to interact very differently. And if we were to judge a book by its cover, we would miss this value completely.”
—Hiren Shukla, Global Neurodiversity and Inclusive Value Leader, Ernst & Young, LLP
They jump in and experiment
One of the most prominent themes is the importance of experimentation. Unlike the deterministic technologies of the past, where a given set of inputs will always result in the same outputs, generative AI is probabilistic, meaning that the outcomes are uncertain. For this reason, the most powerful way to build expertise with generative AI is simply to start using it.
I think the most important thing is to be bold, to experiment, to find ways to incorporate data and AI into your line of business or to your work in small, incremental, low-risk ways so that you can be prepared for when there’s a greater necessity for adaptation.”
—Perry Hewitt, Chief Marketing and Product Officer, data.org
The speed of innovation means that capabilities are increasing all the time. While the idea of experimentation can feel squishy, especially in organizations accustomed to waterfall methodologies, it’s necessary to build capacity and value with probabilistic technologies.
Experiment and adapt. It’s almost like an AI renaissance we’re in. It’s an era of rapid iteration. So leaders need to foster and encourage and imbibe a culture of experimentation. That could be going through small prototypes or small AI pilots or integrating AI into existing workflows. Just do it. Just get it done. Just foster that experimentation mentality.”
—Ade Famoti, Global Head, Research Incubations, Microsoft Research Accelerator
They have a clear North Star
All of our guests approach generative AI with a spirit of experimentation, but they understand that experimentation requires rigor. This means starting with clarity and alignment about the problem they’re trying to solve.
The most important thing for me, for our team, is always explaining why we’re doing what we’re doing. Because the engineers are brilliant, the data scientists are brilliant. If they understand why they’re doing what they’re doing, they’re going to get to a better outcome than I would have envisioned if it were only my thought on how to proceed. So, we invest a lot of time, whenever we’re considering a project or trying to solve a problem, making sure everyone understands why we’re doing what we’re doing.”
—Charlie Rohlf, Vice President, Stats Technology Product Development, NBA
Leaders also think about the opportunities of generative AI not only for today’s challenges, but for the longer term as well.
[Our leaders] wanted to make sure when they saw the disruption that we understood what it meant for us as a company, how we could operate more efficiently, how it could potentially disrupt our offering. They also wanted to understand how it would impact our customers. And by exploring those things and really getting very close to the technology, we identified an initial opportunity, and that’s how Research Assistant came to be.”
—Cristina Pieretti, GM of Digital Insights, Moody’s
They build trust from the start
Trust is a through-line in virtually every episode, from the earliest framing of the use case to the processes and tools used during development and launch to co-creating with rather than developing for customers. The common theme: trust is central to adoption and use of emerging technologies.
[Something that has been] a big learning throughout my career is the importance of trust and proximity: how important it is to be close to the problem that you’re trying to solve and spend a lot of time upfront, whether that’s user interviews or market research and analysis to…understand it more deeply.”
—Perry Hewitt, CMO and CPO, data.org
Usually, we determine what’s going to be the minimum viable product, and then we start developing. Here we realized that it was so uncertain, both for us and for our customers, that we had to emphasize more on showing our customers the art of what’s possible…And what I meant by that, it’s working closely with our customers and experimenting with them and doing proofs of concept with them, so we could demonstrate the value to them [and] also learn in the process.”
—Cristina Pieretti, GM of Digital Insights, Moody’s
Guests also discuss more tangible approaches to building trust. They recommend involving the legal and governance team at the beginning, establishing strong processes and tools for data governance, content safety, model evaluation, bias mitigation, and other needs.
They think of their data as a strategic asset
Generative AI creates new challenges and new opportunities related to data. One challenge is the nature of generative AI models, which are pre-trained on an existing corpus of internet-scale data, lowering the barriers to entry. “But they don’t know your business, people, products, or processes,” says Teresa Tung, Global Lead of Data Capability, Accenture.
Without that proprietary data, she says, models will deliver the same results to you as they do your competitors. The way to turn that challenge into an opportunity, Tung says, is to think about your data—whether it’s synthetic, structured, or unstructured—as a product.
Just like we have other products that you can buy, data itself should be assetized as a product.”
—Teresa Tung, Global Lead of Data Capability, Accenture
And, while proprietary data enables organizations to develop differentiated products, solutions, and services, Shirli Zelcer points out that the value actually goes both ways. Generative AI can also help organizations combine and unlock the value of their business data.
With generative AI, we actually have the power to combine unstructured data with first-party and third-party data and get so much more insight and predictive capability.”
—Shirli Zelcer, Chief Data and Technology Officer, Dentsu
Finally, as Charlie Rohlf says, it’s still early days. “We’re just scratching the surface on what this data is capable of doing.”
Stay tuned for new episodes dropping every two weeks on your podcast platform of choice.
Learn more about the platform shift
If you’d like to learn more about how leaders at the forefront of data, AI, and cloud technologies are leading through the platform shift, please listen, follow, and subscribe to Leading the Shift—wherever you get your podcasts.
The post 5 insights from the front lines of the platform shift appeared first on Microsoft Azure Blog.
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