While generative AI is taking the business world by storm, it’s important to note that it’s not necessarily new. Many of today's promises for AI were also made in the past. It was less than 40 years ago that LISP machines, packed with expert knowledge, were supposed to unlock the promises of AI. What went wrong? ''People believed their own hype,'' said S. Jerrold Kaplan, cofounder of one leading artificial intelligence company, TeKnowledge. So as generative AI occupies massive mind space for most innovative companies, the question they must ask is, “Are we doing this the right way?”
Unfortunately, many aren’t. In fact, there’s a bit of an Ouroboros vibe—the ancient symbol of a snake eating its tail. As companies rush to use AI, they’re making mistakes that could cost money and put the organization in jeopardy. Even the most innovative companies—the ones consistently ahead of the tech curve—must proceed with caution because we’re still in a "flying the plane while we build it" mode
With that in mind, let’s explore how companies can use companies AI to forward innovation while protecting themselves.
Product people must always find the next big thing or improve upon the last big thing. Even the most creative teams may stare at a blank whiteboard and scratch their heads, trying to dream up compelling ideas.
Generative AI is great for idea and concept development. For example, input your identified customer problems and use AI to think of potential solutions. Then take each of those solutions and ask how to solve them. To be clear, you’re not going to find transformative innovation in the answer, but AI can help guide your team toward new ideas that you can discuss.
And that’s the key—find novel ideas that align with your company’s overarching innovation goals. From there, use human smarts to build them out. Humans are essential to using generative AI to your advantage because you need to do more than just generate a bunch of ideas.
It’s essential to understand the problem customers are attempting to solve. That requires listening and thinking—not just assembling data from various sources. Currently, humans are better at that. So while generative AI can create the idea list faster, it's still just a guide. You need to dig deep to understand the problem.
Product development is built on key performance indicators (KPIs) that guide product people from ideation to launch. But what KPIs should you choose? This is a perfect job for AI, especially for companies that track diverse and complex metrics for products.
Use your knowledge of the industry you’re targeting and your organization's data and let AI guide you toward KPIs that will help keep product development moving forward and on time. Another bonus: Your AI research may uncover new KPIs you hadn’t thought of previously but may make more sense based on the product.
And that’s the great thing about cutting-edge technology like AI; it can guide you toward discoveries and illuminate new ways of doing things. You simply cannot survive as an innovative tech company while holding onto a “that’s the way we’ve always done it” mentality, and AI can help counter that crippling mindset.
Companies are adopting an InnovationOps philosophy, which operationalizes innovation to build innovative philosophies into corporate DNA. The idea is to bring together an organization’s people, processes and innovative jobs to be done.
A key component of such a philosophy is emphasizing collaboration and data availability. The more insight an organization has, the more likely it is to glean discoveries that might have otherwise remained siloed. Learnings from generative AI initiatives help to support an InnovationOps approach, as it provides unique insight that cross-functional teams can access and use for their specific projects and products.
A common fear many have about generative AI is that it will take their jobs. Not exactly. It’ll be a human who understands how to use AI. Don’t underestimate people's importance in making AI a driving force in your innovation efforts.
Generative AI can go wide, but it doesn’t necessarily go deep. You need smart people who understand the industries you work in, the history of the products you’ve developed and the unique problems your customers face to input the correct data into large language models (LLMs) to get the output necessary to move the needle. Every organization needs to start training product people to become prompting experts to get to the buried product treasure more quickly.
Don’t put anything into an AI tool you wouldn’t want to show up in someone else’s query or give hackers access to. While inputting every bit of information you can think of in an innovation project is tempting, you have to be careful. Oversharing proprietary information on a generative AI is a growing concern for companies. You can fall victim to inconsistent messaging and branding and potentially share information that shouldn’t be available to the public. We’re also seeing increased cyber criminals hacking into generative AI platforms.
Generative AI’s knowledge isn’t up to date. So your query results shouldn’t necessarily be taken at face value. It probably won't know about recent competitive pivots, legislation or compliance updates. Use your expertise to research AI insight to make sure what you’re getting is accurate. And remember, AI bias is prevalent, so it’s just as essential to cross-check research for that, too. Again, this is where having smart, meticulous people on board will help to refine AI insight. They know your industry and organization better than AI and can use queries as a helpful starting point for something bigger.
The promise of AI in innovation is huge, as it unlocks unprecedented efficiency and head-turning output. We’re only seeing the tip of the iceberg as it relates to the promise the technology holds, so lean into it. But do so with governance—no one wants snake tail for dinner.
Learn how Sopheon is using AI to help its customers achieve innovation goal in an ever-changing business environment.
This article originally appeared in Forbes.