Leaders are pouring billions into AI, but most aren’t asking the right question
When it comes to AI, many organizations are investing heavily, but a crucial oversight persists: few are asking what problem they’re actually trying to solve. As Josh Tyrangiel argues, successful AI adoption has more to do with tackling real business issues than selecting the right tools.
Ignoring the problem, not the technology
AI has become a buzzword synonymous with innovation, yet its implementation is often a haphazard process. Decision-makers focus on adopting the latest AI technologies or partnering with cutting-edge startups, but these approaches can be misguided. Josh Tyrangiel suggests that the focus should shift from the technology itself to the real problems organizations face. “It’s not about the algorithms; it’s about the problems you want to solve,” he says.
What’s the actual problem you’re trying to solve?
Organizations need to ask themselves: what specific pain point do we want AI to address? Are we struggling with inefficient workflows, inefficient customer service, or perhaps inadequate predictions? By clearly defining the problem, leaders can better identify the type of AI solution required to fix it. This clarifies the requirements and expectations from the technology, allowing for more informed decision-making and more effective implementation.
Practical implications of this approach
By prioritizing the problem over the technology, leaders can adopt a more practical and results-driven approach to AI adoption. This means that resources are allocated more efficiently, and organizations are more likely to see tangible benefits from their AI investments. Furthermore, focusing on the problem rather than the solution allows for a more iterative process, where AI is continually refined and adapted to meet evolving business needs.
AI success stories – the exceptions that prove the rule</hassistant
While many organizations struggle to derive meaningful value from AI, there are examples that demonstrate the power of focusing on real business problems. Companies like Siemens and American Express have successfully leveraged AI to improve efficiency and customer experiences, respectively. In both cases, the key to success was identifying and addressing critical business challenges through AI solutions. This approach serves as a reminder that AI is a means to an end, not an end in itself.
The emphasis on real business problems also highlights the need for more effective leadership and collaboration between technical and business stakeholders. By working together, organizations can create an environment that encourages experimentation, learning, and continuous improvement in AI adoption.
Conclusion is not the end
The HBR IdeaCast conversation between Adi Ignatius and Alison Beard offers valuable insights into the potential of AI to drive business success. By prioritizing the problem over the technology, organizations can unlock the full potential of AI and create a more sustainable and effective path forward.
