As we move through the hyper hype cycle of AI, it reminds me of another monumental hype cycle triggered by the launch of the original iPhone and subsequently the App Store. Much like AI today, the iPhone coupled with the App Store ignited an economic and social transformation. At the time, I was working on Qualcomm’s mobile commerce strategy, where I had the opportunity to conduct extensive mobile strategy discovery with some of the largest retailers and CPGs. It was evident that the mobile app hype was driving companies to prioritize expediency over planning, research, and testing. Furthermore, these companies were basing major strategic decisions on the wrong determinants, driven by the following motivations:
- Branding: Leadership teams were tantalized by mockups showing their logo/app icon on the deck of an iPhone. Brand exposure and association with the iPhone were paramount, while customer value and sustainable engagement became afterthoughts.
- Strategic Urgency: Driven by a lack of preparedness and fear of missing out, company leadership teams were under tremendous pressure to not only develop a mobile app strategy but to quickly commence execution.
- Unrealistic Expectations: Due to the hype cycle, customers, employees, and decision-makers were witnessing the success stories (e.g., Starbucks app) and expecting similar outcomes for any and all mobile offerings.
Not surprisingly, many app launches failed to provide users value and struggled with both acquisition and retention. Without the right motivators and strategic planning, these failures were almost inevitable. Yet over time, with learnings and better planning, these same companies have developed mobile apps that provide user and business value.
With today’s AI hype cycle, many of the same motivators and determinants are prevalent. Even the best-run tech companies are repeating these mistakes. Here’s a post (formerly known as a tweet) that is a case in point:
To avoid these traps, executives should initially focus on discovery and research across these critical dimensions:
- Protection of Assets: First and foremost, companies should ensure their IP, data, content, and copyrighted materials are not being consumed by Large Language Models.
- Competitive Analysis: Secondly, it’s important to thoroughly analyze how AI will disrupt and impact your market(s). It’s very likely that AI-based companies will use the technology to gain a competitive advantage over your offerings. It’s critical to identify threats from incumbents, newcomers, and players in adjacent markets. This analysis should include high-level views of disruptive use cases, workflows, and product offerings.
- Productivity: The productivity gains we’re seeing today via AI are only the beginning. The extent of the productivity gains is unknown, but it’s within the realm of possibility that AI will drive the largest increase in productivity in human history. There are countless opportunities to augment existing operations with AI, regardless of company or industry. It’s imperative to dedicate resources to identify these operational strategies. If well executed, companies will see productivity gains while developing their employees’ AI skillsets.
- Customer Service: Adopt AI-enabled CS systems to augment and improve customer service teams. When implemented correctly, AI-enabled CS solutions will increase availability, coverage, reduce costs, and improve customer satisfaction.
- Commercialization of AI: For most companies, developing AI products and/or commercializing AI should be lower in priority than protecting assets, competitive analysis, productivity, and customer service. As with any major product initiative, taking a smart route requires the right mix of planning, alignment, resources, budget, and sustainable urgency. As those who’ve worked with me know, one of my favorite sayings is: it’s better to measure twice and cut once. Take the time to identify opportunities, ideate, and most importantly, talk to your customers
By learning from past mistakes and focusing on these dimensions, companies can navigate the AI hype cycle more effectively and achieve sustainable success by providing customer value.