Businesses Must Redesign Operating Systems for AI Success, Says Author Melissa Reeve
Melissa Reeve, author of "Hyperadaptive: Rewiring the Enterprise to Become AI-Native," contends that most organizations struggle with AI adoption because they try to integrate it into outdated systems built for predictability. Instead of isolated AI initiatives, companies need to become "hyperadaptive," fundamentally changing their people, processes, and culture to sense faster, learn continuously, and make smarter decisions. Reeve emphasizes that the window for this transformation is narrowing, advocating for a systemic overhaul rather than merely bolting on new technology.
Organizations seeking to leverage artificial intelligence effectively must undergo a fundamental transformation of their operating systems, rather than simply adopting new AI tools. This is a core insight from Melissa Reeve's book, "Hyperadaptive: Rewiring the Enterprise to Become AI-Native."
Reeve, who previously served as the first VP of marketing at Scaled Agile and co-founded the Agile Marketing Alliance, argues that current operating models, largely designed for the industrial era, are ill-equipped to support AI. These models prioritize consistency over speed, with top-down strategies, functional silos, and slow decision-making processes. Attempting to integrate AI into such structures often leads to isolated successes or what is termed "random acts of AI," where some teams accelerate while others remain stagnant.
According to Reeve, the shift to an AI-native approach is urgent. While digital transformation allowed companies approximately a 10-year adaptation window, AI demands a quicker response, closer to 18 months. She posits that successful companies replace their underlying operating systems, aligning people, processes, and culture to operate with AI, rather than adding AI on top of existing structures.
Effective AI integration requires deliberate investment in training, process redesign, and cultural shifts. Brad Miller, former Chief Information Officer at Moderna, stated that while 90 percent of companies desire generative AI, only 10 percent succeed, attributing the disparity not to technology but to the failure to build mechanisms for workforce transformation. Moderna's CEO, Stéphane Bancel, famously challenged his team to bring 15 new drugs to market in five years, achieving 100% generative AI adoption in six months by investing in comprehensive training and coaching.
Reeve stresses that learning in an AI-driven environment must be continuous and bidirectional, not a static curriculum. She cites PwC's "prompting parties" as an example of cross-functional peer learning. An "AI learning flywheel," involving AI Activation Hubs and AI Leads, is proposed to capture, refine, and disseminate knowledge throughout the organization, enabling real-time adaptation.
True AI transformation involves moving multiple organizational dimensions concurrently. Reeve identifies critical, often overlooked areas such as incentives (rewarding learning over being right), decision rights (re-evaluating who makes decisions as AI collapses hierarchies), and organizational structure (shifting from functions to value streams and dynamic teams).
The advent of AI also brings significant workforce changes. The World Economic Forum projects 92 million jobs will be displaced by 2030, but also forecasts the creation of 170 million new jobs, resulting in a net positive. Historically, technological revolutions have seen jobs evolve from manual tasks to building, monitoring, and maintaining systems. Companies like Unilever are proactively investing in upskilling their existing workforce, using AI to match employees with emerging roles, recognizing it as a strategic, long-term investment.
According to Fast Company, these insights provide a blueprint for businesses aiming to successfully navigate the AI era by becoming truly hyperadaptive. (Source: Fast Company)
