The parabolic growth of accessible AI tools has intriguing implications for businesses. Analysts imagine that generative AI, for example, will have a massive impact on productivity across multiple business functions. Many organizations are scanning the horizon for a long-term AI-fueled transformation, eager to make the most of bullish CAGR projections. And while leaders mustn't lose sight of long-term goals, staring out too far into the future may be overwhelming — distracting, even.
Rather than redesign their business' entire approach just to meet AI somewhere along the horizon, leaders can instead take a more practical route and ask how AI can improve their current strategy. Can AI accelerate current tactics? Can it help teams do things better? Can it help organizations reach their goals with less overhead? The answer, especially regarding product, customer success and internal processes, is overwhelmingly "yes."
AI's impact on product development begins with the nitty gritty. Generative AI tools like ChatGPT can help teams with everything from documentation to marketing briefs and website content. My team has leveraged AI for these very purposes, letting AI rewrite code into additional languages once we create the initial sample code. Humans are still an essential part of the process, but AI helps provide a kickstart.
Tech companies have taken AI a step further, embedding it into their products. AI represents both a tremendous opportunity and a threat for security solutions providers. Bad actors have new tool sets that enable them to create more sophisticated attacks faster and more intelligently. Cyber product teams use the same tools to defend against emerging threats and offer in-product help to ensure their customers are more productive, better informed and ultimately satisfied with the experience.
Non-tech companies should be thinking about the experience around their products, and, indeed, many are. Car manufacturers use AI to enhance their collision-detection systems. Healthcare solutions providers embed AI in their diagnostics and imaging products. Nike uses AI to power its product personalization efforts.
Customer-experience chatbots have been around for a long time, but concerns about data privacy, unnatural language and unhelpful results have kept them from becoming ubiquitous. Recent advancements have helped fine-tune chatbots such that they can answer questions more efficiently and accurately than a support desk person. AI-enhanced chatbots have helped transform these experiences from feeling like an impersonal human replacement to a better and more responsible customer experience, yet some consumers are still wary. Most will use chatbots, provided there is always an option to transfer to a live agent.
Chatbots aren't the only way organizations can infuse their customer experience with AI. Many companies effectively employ powerful data analytics, feeding valuable purchase and customer data into algorithms that help create ever-evolving seamless, personalized omnichannel experiences – think about how Spotify recommends new songs based on listeners' history and allows them to switch from one device to the next easily.
For both product and customer experience teams, much of the AI magic happens behind the scenes. Chances are those teams are also using intelligent tools to automate workflows and speed up processes so that people can do their jobs more effectively. Teams for nearly any business function can use AI to do everything from creating images for a slide presentation to drafting website content and writing documentation.
Leaders interested in process-focused AI can begin by asking, "How can AI help deliver a product or service more effectively?" and "What are we spending time on that AI could/should be doing?" By leaning into existing tools, such as those that Microsoft, Google and OpenAI provide, organizations can simplify mundane tasks involved in creating documents, spreadsheets and slide decks to free up their workforce for more creative and mission-critical work.
On my product management team, we're exploring all facets of our roles and asking ourselves how AI can help us spend more time analyzing information instead of gathering and summarizing it. This approach has been a tremendous shortcut for some components of our research and is a helpful way to think about AI as it relates to our company's trajectory. When we ask how AI can help us fulfill our goals, we stay focused rather than become distracted navigating to some nebulous AI-enabled future along the horizon.
Making AI work for us — not the other way around — is also a useful reminder that modern intelligent tools aren't here to replace employees. In fact, a human in the loop is critical, regardless of AI's application. Product teams must validate AI's documentation; customer experience teams need to review modeling output for errors and continue to interact with customers when the time comes.
The next time you make a decision about AI, remember that it is just a practical means to achieving business goals and not the end goal in and of itself.
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