By Bryan Kirschner, Vice President, Strategy at DataStax\n\nBill Gates has seen (or, for that matter, caused) some profound advances in technology, so I don\u2019t take a contrarian position lightly, but I think the way he describes his epiphany about the importance of AI is only half right.\n\nAfter being \u201cawed\u201d by OpenAI\u2019s GPT model acing the AP Bio exam, the model was asked a non-technical question: \u201cWhat do you say to a father with a sick child?\u201d Gates describes the results this way: \u201cIt wrote a thoughtful answer that was probably better than most of us in the room would have given. The whole experience was stunning.\u201d\n\nI don\u2019t dispute that. As a user of ChatGPT to both get work done faster and kick the tires on what it can do, I\u2019ve been impressed (it replied to a prompt to \u201ctell me about Aristotle in the style of Roy Kent,\u201d the expletive-prone \u201cTed Lasso\u201d character, with uncanny flair).\n\nBut as we all shape business strategy around the implications of generative AI, we also need to look 180 degrees away from concepts like \u201cstunning\u201d or \u201cuncanny\u201d toward \u201cpurpose-built,\u201d \u201cpredictable,\u201d and \u201cproductive.\u201d\n\nThat\u2019s because we\u2019d absolutely expect a model trained on (say) 10,000 sympathy cards or 1,000 eulogies to come across as sensitive, consoling, and well-spoken, hitting the right tone better than most of us could do on the fly. It should be entirely unsurprising\u2013at least for people of the cultural or religious background for whom the original content was produced.\n\nFor all the risks of hallucinations or bad behavior from models trained on the open internet, generative AI strategy in all our organizations is about unlocking the potential of well-intentioned people to create well-intentioned AIs tailored to their specific context. Fine-tuning models that run \u201con top\u201d of foundation models requires less data, costs less, and can be completed quickly.\n\nMarc Andreesen provides an evocative example of what is well within reach technically:\n\nEvery child will have an AI tutor that is infinitely patient, infinitely compassionate, infinitely knowledgeable, infinitely helpful. The AI tutor will be by each child\u2019s side every step of their development, helping them maximize their potential with the machine version of infinite love.\n\nTomorrow\u2019s most successful organizations will have tens or even hundreds of AIs working alongside and on behalf of their human staff in planful, constructive ways. Two operational concepts\u2013the \u201ceager intern\u201d and the \u201cautonomous agent\u201d\u2013can help jumpstart your journey.\n\nAI as an \u201ceager intern\u201d\n\nBusiness school professor and technologist Ethan Mollick offers what I\u2019ve found to be very useful framing for how to think about generative AI: \u201cIt is not good software, [rather] it is pretty good people.\u201d\n\nAnd rather than thinking about AIs as people who replace those already on the payroll, treat them like \u201ceager interns\u201d that can help them be more productive.\n\nThis metaphor can help on two fronts. First, it keeps the need for human supervision front and center. Just as hiring and productively managing interns is a valuable competency for an organization, so too is using ChatGPT, Microsoft\u2019s CoPilot, or Google\u2019s Bard. But you would no more blindly trust this class of model than you would even the most promising intern.\n\nSecond, and as important: IT isn\u2019t responsible for hiring interns in Finance and HR. Likewise, Finance and HR (and every other function) must build their own competency i figuring out how to use these tools to be more productive. The job to be done is closer to answering domain-specific staffing questions than IT questions.\n\nThis is table stakes on the path to the breakthrough in productivity: \u201cautonomous agents.\u201d \n\nAgents of productivity\n\nAutonomous agents chain together tools so the AI, once given an objective, can create tasks, complete tasks, create new tasks, reprioritize the task list, complete the new top task, and loop until the objective is reached. (This is a good introduction to use cases that includes an example of how something like Andreesen\u2019s infinitely patient math tutor might be built.)\n\nBut if you\u2019re a CEO who wants to accelerate getting to \u201cAI for all,\u201d I recommend taking 10 minutes with your leadership team to read my colleague Ed Anuff\u2019s explanation of how a consumer-focused agent could be built today. Here\u2019s a key excerpt:\n\nYou want to build a deck in your backyard, so you open your home-improvement store\u2019s mobile application and ask it to build you a shopping list. Because the application is connected to an LLM like GPT-4 and many data sources (the company\u2019s own product catalog, store inventory, customer information and order history, along with a host of other data sources), it can easily tell you what you\u2019ll need to complete your DIY project. But it can do much more.\n\nIf you describe the dimensions and features you want to include in your deck, the application can offer visualization tools and design aids. Because it knows your postal ZIP code, it can tell you which stores within your vicinity have the items you need in stock. It can also, based on the data in your purchase history, suggest that you might need a contractor to help you with the job \u2014 and provide contact information for professionals near you.\n\nThis type of experience is not just the future for your customers. It needs to be the future of all your employees, too. How can AI help marketers track your brand on social media? How can it assist legal teams with contracts? How can it help HR recruit, hire, and develop people?\n\nYour functional teams and business units should be gaming out ideas and getting started on autonomous agents today. There\u2019s no time like the present to get more productive: The technology is ready and waiting.\n\nLearn more about how DataStax enables real-time AI here.\n\nAbout Bryan Kirschner:\n\nBryan is Vice President, Strategy at DataStax. For more than 20 years he has helped large organizations build and execute strategy when they are seeking new ways forward and a future materially different from their past. He specializes in removing fear, uncertainty, and doubt from strategic decision-making through empirical data and market sensing.