Speculation abounds regarding the future of AI. Are we inside a hype bubble on course to pop? Is the potential for workplace automation overblown, setting businesses up for a hard fall?
MIT Senior Lecturer Paul McDonagh-Smith provides sound perspective in the face of wide-ranging conjecture. When it comes to AI, he reminds us, cycles of enthusiasm and disillusionment have long dominated popular discourse.
As McDonagh-Smith writes, “the question of whether AI is overhyped or is rising to meet its expectations has been a constant over the last 70 to 80 years.” He urges us to recall several key developments (and less enthusiastic periods) across the history of AI:
- From advent to first cold spot. Beginning with the Dartmouth summer research project in 1956 credited with “launching” AI, leaders in the field “set high expectations that were later chilled in the AI Winter of the 1970s and 1980s,” he shares.
- Attributes of winter. According to McDonagh-Smith, the early AI winter resulted from data and computational limitations that divided AI’s potential from practical application. This gap inspired less investment as well as “a growing skepticism and sense of AI overhype.”
- ML enters the conversation. Machine Learning resparked excitement for AI in the 1980s. Expansion of data and computing enabled new capabilities like natural language processing. The evolution that followed in the 1990s and 2000s inspired AI’s expansion into deep learning, “enabled by breakthroughs in neural networks, and exponential improvements in the capability and performance of AI,” McDonagh-Smith shares.
From these roots to the acceleration of GenAI, the AI hype cycle has been in constant motion. If, as some fear, another AI winter is on the horizon, how can businesses avoid the fallout—or even hold off the season-change indefinitely?
McDonagh-Smith advises leaders to focus on AI’s “last mile engineering,” what he defines as “the space between AI Models and an AI Mindset that we can apply individually and collectively in our teams and companies.” This divide is the reason expectations around AI have exceeded AI ROI for several years and is the product of another gap—the one we maintain between the physical and digital worlds where work happens.
An AI mindset demands more than technical proficiency. As McDonagh-Smith writes, it requires “giving our workforce and customers permission for greater creativity, curiosity, collaboration, critical thinking, and compassion.” In other words, leaders should embrace the ways in which AI technology and humans each bring unique skills to the table.
There are indeed skill gaps on both sides. “As I work with a broad range of companies, I see many organizations struggling with data silos, inconsistent data formats, and complex privacy concerns that span geographies and jurisdictions,” shares McDonagh-Smith. These challenges in tandem with a greater need for the aforementioned “soft” skills necessitate that leaders ask tough questions of their businesses and themselves.
According to McDonagh-Smith, the following inquiries are vital to help close gaps:
- What is my AI Data strategy?
- What is my AI People strategy?
- What is my AI Enterprise strategy?
Regarding the final question, the process can be easier “if we adopt a dual speed approach to AI strategy in our organizations where we conduct ‘fast’ experiments that capture data that then informs the ‘slower’ longer term (for example 18-24 months) strategic AI trajectories to be explored and mapped.”
Other significant and related questions McDonagh-Smith has urged leaders aspiring towards AI-driven organizations to consider are:
- What are the organization’s critical business problems and strategies, and can AI be used to deliver on those strategies?
- Is the organization’s data AI-ready (that is, are the datasets that are being trained suitably complete and managed with the proper governance)?
- What is the AI maturity level of organization employees, and where are there key skills gaps? What kind of programs should be in place to upskill employees on critical new competencies?
Rather than focusing on the news cycle, McDonagh-Smith is interested in how AI will impact not only businesses but human identity and even our souls. The technology opens an unparalleled opportunity for introspection and growth. As he writes, we have “the chance to realize that we are at our best not as isolated beings, but as a vibrant part of an interconnected, intelligent system of humans and machines working together to frame and fix problems that are bigger than we are individually, but smaller than we can be together.”