AI + You | 5 steps for impactful experimentation

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1:18On the final Thursday of every November, many homes across the U.S. burst with the sounds of a parade on the TV. Spirited family debates You want to talk politics? Let's talk politics And the squeak of an old oven door For some people, preparation for the holiday begins months in advance While traditions may stay the same, the menu often evolves It's like, what's a new way to make turkey? What's a new way to make pie? It can be so frustrating That's Priya Krishna Priya is a best-selling cookbook author and food reporter at the New York Times. Every Thanksgiving, Priya feels the pressure to conjure new and creative recipes.

2:02So Priya decided to welcome an unlikely sous chef into her kitchen. We thought, what if we just asked AI to generate our Thanksgiving menu for us? Priya wondered, rather than bend over backwards to invent the next turducken, What if AI was the secret weapon every recipe writer didn't know they needed? Priya and her team filmed their experiment. She committed to cooking the AI-generated recipes word for word. We gave ChatGPT a bunch of different prompts. Show us a Thanksgiving dessert that is a spin on pumpkin pie. Show me an unconventional take on stuffing. Show me an Indy-inspired version of turkey.

2:51And that's where things got really interesting. The more specific we made the prompts, the more creative ChatGPT would get. To Priya's surprise, GPD's recipe for stuffing called for Indian naan bread. Naan is not the most intuitive bread for a stuffing. You think you would want something that's super absorbent. But in my mind, I was like, maybe there's something too non. While Priya cooked through each of the AI recipes, she battled against her instincts, desperate to amend peculiar directions and measurements. The first dish Priya finished and plated was the non-stuffing. It looked a little gnarly coming out of the oven.

3:40It sort of looked like a cinnamon raisin bread pudding. Thankfully, other dishes looked more appetizing. The cake looked delightful. It was like a pumpkin spice cake with cream cheese frosting. To judge AI's Thanksgiving dishes, Priya invited four New York Times cooking columnists. The green beans were cooked to that perfect, crisp, tender texture. Bright green, they've got a little crunch. And then on the other hand, you had this turkey that was just dry as a bone. The general consensus was, if I went to someone's Thanksgiving dinner and they served this, I'd be ordering pizza afterwards. Despite GPT's lack of culinary success, Priya came away with some optimism.

4:32There are plenty of uses for AI in cooking. You could tell AI, I've got mushrooms and chicken broth and green beans in my fridge. What are some things I could make? And AI could give you ideas. I'm cooking chicken thighs. What temperature should they be at to be considered fully cooked? What is the roasting temperature for sweet potatoes that are cut into cubes? AI can be very good as sort of a kitchen assistant rather than perhaps the kitchen leader.

5:07Prio is only able to make this useful discovery through experimentation. No chef expects to create a flawless dish on their first attempt. They know from experience that commitment and patience are key in finding the perfect balance of flavors, textures, and smells. Although AI lacks the distinctly human senses and emotions needed to enjoy a turkey with all the trimmings, It can still help us come together and experiment and collaborate to make cooking creative, exciting, and accessible. Even though the AI's integration was far from a triumph, it encouraged Priya to step outside of her comfort zone and open her eyes to new ideas.

5:51Every business leader should take inspiration from Priya's willingness to invite AI into the kitchen and experiment. With patience and an open mind, the potential is boundless. That's why I believe that AI won't revolutionize your business overnight. To unleash AI's true power of scale, you must dive headfirst into an era of ongoing experimentation.

6:33excited I've been about anything in software since the internet. It's definitely a super exciting time to be in this space. All of these things are possible. Let's try to figure out whether this is actually useful or not. The tech just shouldn't replace human beings. Is it the worst thing in the world or is it the greatest thing in the world? This is Masters of Scale.

7:05I'm Reid Hoffman, co-founder of LinkedIn, partner at Greylock, and your host. And I believe that AI won't revolutionize your business overnight. To unleash AI's true power of scale, you must dive headfirst into an era of ongoing experimentation. Normally, on Masters of Scale, we dissect the story of an iconic entrepreneur to prove a theory of scale. But in the spirit of experimentation, today we're bringing you something a little different. We'll hear from an array of experts who have played a major role in scaling AI over the past decade. They'll share stories and lessons from their time at pioneering AI developers like OpenAI, Google DeepMind, and Microsoft.

7:48Their experiences with AI provide a perfect lens for any leader to better understand and navigate this new era, even in industries far from the leading edge of tech trailblazers. With the burgeoning AI revolution, it's helpful to remember that this isn't the first time modern humanity has transitioned into an intimidating new technology. When the world was first introduced to Netscape in 1994, many predicted that the internet would revolutionize the world of media and retail. Of course, this came true, but it took well over a decade to fully come to fruition. During that time, the technology gradually improved and the cost of entry lowered.

8:33Computers became faster to manufacture and the resolution of videos featuring sneezing pandas or cats in fedoras streamed in a higher and higher quality. However, it took years for many businesses to take advantage of the opportunities afforded by the internet. Today, we're experiencing change in a more compressed time frame. AI is evolving at an exponential pace. So, the way we enact patient experimentation must mirror that speed. As AI can be overwhelming, we want to cut through the noise and offer five steps to experiment impactfully with AI. By the end of the episode, I hope you're inspired by AI to forge a new creative and collaborative relationship.

9:19I also wrote a book earlier this year on this subject called Impromptu, the first book on AI that is co-written with AI. Some of these experiments will be brand new to leaders, while others may be recognizable experiments, but with an added twist. Here's your five steps to experiment impactfully with AI. Step one, learn what's out there.

9:49Thorough preparation is essential for any experiment. So let's begin by exploring some of the preliminary questions that leaders must consider before weaving AI into their business. Our first AI thought leader is Mael Gaveh. Mael is the CEO of Techstars, a global investment firm that focuses on early-stage startups. She currently mentors countless entrepreneurs who are transitioning to AI. I think there's a lot of people right now who are like, oh my God, there's this new wave of innovation coming at me. I'm not an engineer. I'm really worried that I'm going to be left behind. I don't have the resource.

10:29I don't have the skills. Like, what do I do? If you can relate, Maelle offers some sage advice. It's a marathon, not a sprint. You do not have to run at full speed because right now, right here, it's only 100 meters. You need to save your energy and your resources to get to the finish line. It's important to go through the finish line, but you don't have to be the first one. That's okay too.

10:58Mael's right. Any reliable experiment takes time. And many experiments fail. But that's exactly why we experiment. When we discuss the race with AI, it's important to remember that everyone's race is different. It could be a race for market share, a race for capital, or even a race for business survival. Regardless, if you lack confidence in your current ability to harness AI, that's entirely understandable. Despite which race you're running in, we're still near the starting line. Considering when it is the right time to incorporate AI into your business, this is not a one-off process. You're not going to decide today whether you do AI or not do AI, whatever that means.

11:46AI will certainly align with some of your business needs. AI amplifies language and communication, which is relevant to every business. Whether it's in meetings, sales, marketing, or customer service, there is a touchpoint for AI. However, today's available AI models may not immediately reshape your business. But the question isn't if you should begin experimenting with AI, but how much you should incorporate AI in your business following the experiment. Mael poses some questions for you to determine your need for AI integration at this moment in time. Does that answer your customer need? Does that help bring your business forward?

12:25And if it does, great. And if you don't know yet, in my world, I think it's okay to wait another month, another quarter. I'm not saying 10 years, but just a little bit, and then revisit on a regular basis whether or not there's new application of AI, new development that would make sense into your business. And so experiment. Try things and see what sticks. I want to echo Mael here. Just because AI might not integrate correctly into your business today doesn't mean that it won't six months from now. As humanity's partnership with AI has only just begun, it's vital to keep your finger on the pulse and experiment with all the new use cases that develop.

13:06Step 2. Focus on the pain points.

13:16After you've decided to begin your journey with AI, it's wise to choose a specific area of your business to focus your experiment. Note, the AI discussed in this section refers to traditional AI rather than generative AI. In short, traditional AI can analyze data and tell you what it sees. It relies on explicit programming, whereas generative AI can use that same data to create something entirely new, like generating text, images, music, or even human-like conversation. The next voice you'll hear is Fei-Fei Li. Fei Fei is co-director of the Stanford Human-Centered AI Institute. She also served as vice president of Google and chief scientist of AI at Google Cloud.

14:04Healthcare has been a deep concern for Fei Fei from a young age. Reed, as you know, I come from an immigrant family. And I think as a friend, you also know my mother. she's chronically sick with a very severe cardiovascular illnesses. So as far as I can remember, I was taking care of my mom. Over her decades of caretaking, Fei-Fei has experienced every facet of the U.S. healthcare system, from ICUs to ambulances to operating rooms to home care. Her main takeaway? Healthcare is complex. Around 2012, there was a very exciting technological revolution happening in Silicon Valley, which is self-driving car.

14:57It really dawned on me, my God, what we're learning from self-driving car, which is smart sensors, deep learning algorithms, a holistic understanding of the environment, will also be applied to healthcare delivery.

15:18We immediately start talking to Stanford hospitals. Our very first project, I think, started in 2013 was hand hygiene. Hand hygiene in patient rooms is so important to reduce the hospital-acquiring infection, which kills three times more Americans every year than car accidents. Before they could make a difference in hand hygiene, they needed more data. The only way to collect this data was by having a healthcare worker monitor how regularly and extensively colleagues washed their hands. But Fei-Fei saw the importance of reliably collecting the data to make healthcare workers aware of the current state of hygiene and potential risks.

16:01In a single hospital unit, Fei-Fei and her team installed computer vision technology by every hand hygiene station. A machine learning algorithm captured constant images and was trained to detect sanitizer dispenser use. The algorithm's accuracy was compared with an in-person human observer fulfilling the same task. And this is pre-COVID. It sounded like a boring project, even though we knew it was so important. But after COVID pandemic came, it made so much sense. Yep. Even not being a doctor, because it's actually the application of technology to a very important human-centered thing, which is health and well-being and the system of things and just the little thing of making sure the hands are washed.

16:47Massive improvement. Exactly. When the results of Fei-Fei's experiment were analyzed, her team came to the conclusion that AI integration has the potential to surpass the current gold standard of hygiene in hospitals. Another AI pioneer who dedicated years to AI's application to healthcare is Mustafa Suleiman. Mustafa co-founded one of the most influential AI businesses in the world, DeepMind. After Google acquired DeepMind, they launched DeepMind Health. We started DeepMind Health to work on predicting electronic health records to try to detect when a patient was about to experience some kind of deterioration.

17:31Through meetings with doctors and nurses, DeepMind Health learned that the detection of acute kidney injury, or AKI, was a consistent pain point. Spotting the early signs of AKI was notoriously difficult, often slow and unreliable. And with AKI contributing to the deaths of around 100 ,000 patients every year in the UK, the stakes were high. Experts believed that up to 30 % of AKI cases could be prevented if a doctor intervened early enough. We developed an algorithm that could read the electronic health record and try to predict when a patient looked like they were about to go into kidney failure and experience either sepsis or some other kind of serious deterioration.

18:18Once the team saw success in their algorithm, they wove the technology into a new app called Streams. The first users were healthcare workers in the NHS. Now doctors could simply feed the patient's charts and vitals into the Streams app, and it would alert them of a potential AKI risk. The app reduced the cost of treating acute kidney injury by 20 % and sped up the time to detect acute kidney injury from on average 4 hours to 15 minutes. I was very proud of that work because it actually showed that you could simultaneously reduce costs and improve the quality of care. And that's the goal in health systems is that you want to make things cheaper and higher quality.

19:06And that's the promise of AI. Over the course of four years, Google DeepMind's AI also became equally proficient at detecting eye diseases and breast cancer. In 2021, Google took Streams offline due to privacy concerns. But it's helpful to remember, a large part of the Streams app's original success was due to addressing pain points in healthcare. So for AI to make improvements to your business, explore where it's most needed. Getting the most out of human teammates may be one of your most critical pain points. I would say every employee, every knowledge worker should now learn how to be good at delegating.

19:47That's David Luan. David is the co-founder and CEO of Adept, developing models that actively fulfill digital tasks for you, learning software to become an active teammate for knowledge workers everywhere. David shared with me the many layers that go into thoughtful delegation. Delegation itself is a whole skill. How do you frame what the goals are? How do you convey the desired outcome you want to, whether that's your human teammate or your AI teammate, in the fewest number of words possible where you actually get that outcome, prompting is a way to just get the right textual output back. So how do you delegate that whole thing to the model?

20:26I actually think everybody should be thinking about how they use AIs to make themselves more effective. And they should be trying to limber up their skills, doing a little bit of flexibility stretching and bench pressing and other kinds of things. Even if delegation isn't currently a leadership pain point for you, the new specificity and delegating to AI is something you'll need to master. Working with AI doesn't immediately mean it'll generate the perfect results. Delegating to AI is achieved through asking the model questions, often described as prompts. But framing a smart prompt to generate the best result is its own skill.

21:08To show you what I mean, I want to take a moment to ask Pi a couple of questions. Pi is the large language model I help build at Inflection AI. Hey there, great to meet you. I'm Pi, your personal AI. Hey Pi, give me a title idea for an episode of Masters of Scale about AI. Here goes. How about the automated workforce or robots, humans, and the future of work? These titles suggest that the episode will explore how AI and automation... Stop generating. Hmm, that's not quite what I want. Let me reframe the question. Can you give me a title for a Master to Scale episode about AI that's pithier and tells the audience that they'll learn tangible lessons about incorporating AI into their business?

21:54Absolutely. How about 10 Steps to Becoming an AI-Powered Business or the AI Playbook? These titles are short and sweet, and they hint at the practical focus of the episode. I nailed it, didn't I? That's great. I might use that. I'm your AI at your service. Good prompts are dependent on the subject, and the best prompt doesn't mean it's ultra-specific. I'd like to think of it more as prompt directing rather than prompt engineering. Think of yourself like a movie director. Rather than telling the actor, say the line exactly this way, leave breathing room for creativity by the actor. You might say, try it more in the style of this or inject a little bit of that.

22:37The conceptual lenses you add into your prompts will lead you to the most satisfying surprises. While delegation is far from a new entrepreneurial skill, these core lessons are more important than ever as leaders enter an unpredictable and fast-paced era. So far, we've explored some of the preliminary questions you should consider while diving into the great AI experiment. Next, we'll navigate how leaders should continue the experimental mindset once AI integration is off and running. That's after the break, so stick around.

23:17Real leaders don't back down when the stakes are high. They innovate, they push forward, and then they take the stage at the Masters of Scale Summit. Join us in San Francisco, October 7th to 9th, to hear from the CEO of the New York Times, scientists using cutting-edge technology to find cures, the leader of crypto powerhouse Coinbase, a retired four-star general, and many, many more. Apply now at mastersofscale.com slash apply25. That's mastersofscale.com slash apply25.

24:17aws.amazon.com slash activate and start building. Expanding your business in the U.S. can feel like a maze. Every state has its own payroll, benefits, and compliance rules, which can pull your focus away from growth. That's why founders use Deal. Deal is the Professional Employer Organization, or PEO, that gives you a dedicated HR expert plus Fortune 500 level benefits for your team. The National Association of PEOs says businesses can grow twice as fast if they use one. So if you're scaling, make it simple with Deal. Go to deel.com slash mos and get up to three months free.

25:05We're back with your five steps to experiment impactfully with AI. To see exclusive clips from my interviews with some of our AI thought readers featured in this episode, head to the Masters of Scale YouTube channel. Before the break, we heard how AI won't revolutionize your business overnight. And to unleash AI's true power to scale your business, you must dive headfirst into an era of ongoing experimentation. But now it's time to level up. I want to explore the ways in which you should continue experimenting while beginning to actively integrate AI into your business. So let's jump into the next step for experimenting impactfully with AI.

25:49Step 3. Don't preach the power of AI. Illustrate it. Step 3. Don't preach the power of AI. Illustrate it. While you might be eagerly anticipating all the ways in which AI can evolve your business, there will be other members of your team that are wary. I'm awkwardly placed across this triangle of technology, design, and business. That's Dr. John Maeda. He's an artist, author, and VP of Design and AI at Microsoft. As a student of many disciplines, John is in a unique position to monitor enthusiasm for AI. I can hear the technology folks, this is amazing, let's do more of it. The design, social science part of it, saying we have to ask questions, how does it impact everything, how does it lead to more unfairness, really important humanity questions.

26:47And I can hear on the business product side, well, how is this going to lead to more profitable business? If an experiment has multiple conflicting aims, it's harder to get everyone on board. So, for example, if your business development or marketing team is gung-ho with AI, but your creative team is resistant, make sure to begin a process of clear communication to get everyone in sync with a strategy and roadmap moving forward. You can do this by starting a simple, frank discussion, laying out how you plan to mitigate the downsides and take advantage of the upsides. Navigating those frictions is so critical for AI to make a true difference to business, to the culture, and to advanced technology.

27:35To understand more about how to align your team, we need to hear from Jared Spotaro. Jared is the CVP of modern work and business applications at Microsoft. As an expert on the future of work, Jared has ideated on all the ways that AI could transform the workplace. Like Dr. Maeda, Jared knows that everyone across your team might differ in their enthusiasm for experimenting with AI. It is very rare that you're going to have everyone in a group say, that's it, that's the solution. You have early adopters. They play an important role in evangelizing the rest. You have laggards. They play an important role in kind of anchoring the group to make sure there is some thought that's being applied to the new technology.