March 26, 2026
This is the first blog in Mike’s 8-part series about how AI can become a force for clarity and acceleration across the broader software development lifecycle.
The adoption of AI in software delivery is becoming more and more common; it’s seen as a panacea to reduce costs, increase throughput, and shorten time-to-market. In practice, many are discovering that while engineering capacity has expanded dramatically, overall business outcomes have not improved at the same rate.
What seemed like a productivity gain actually creates a new organisational bottleneck: the ability to decide what should be built, and why, does not scale as quickly as the ability to build it.
“The focus on only accelerating coding for engineers has exacerbated an existing issue for product managers: insufficient time.”
Product people share a frustration that their time to focus on more strategic ‘human’ activities is already pinched: discovery, experimentation, deep thinking, and deep collaboration with engineering teams – all essential to optimising value delivery – are put aside as a result. Too often they’re reduced to mere ticket writers.
AI-accelerated engineering teams are now demanding ever higher rates of PM inputs simply to stay busy.
Andrew Ng recently illustrated this shift when he observed early-stage teams proposing a move from a 1:4 product-manager-to-engineer ratio to 1:0.5. While extreme, it signals a broader trend that will increasingly affect large enterprises: uneven adoption of AI introduces new bottlenecks and pressure to generate output regardless of its alignment to strategic value.
“When delivery is slow, poor alignment is painful but contained. When delivery is fast, small misunderstandings and imperfections are amplified across teams and releases.”
This shifts the risk profile for the business. Speed without alignment increases the likelihood of:
The challenge, therefore, is not merely to “speed up product” to match engineering, but to facilitate better decision-making despite this acceleration by ensuring humans have enough time to think deeply. AI can create that time.
Mike Mitchell is a Product and Strategy Principal at Equal Experts, helping organisations adopt modern product practices and integrate AI into their product lifecycle in practical, sustainable ways.
His current focus is on helping product teams do more meaningful strategic work with AI, both by accelerating the routine work that crowds it out, and by making AI a more proactive, multi-modal collaborator.
Mike brings 35 years of experience in enterprise software, with a foundation of engineering followed by two decades of product leadership. He is a founder of multiple startups, has advised dozens more, and has led multiple enterprise transformations. Just prior to EE, Mike served as a fractional Chief Product Officer, guiding early- and growth-stage firms to deliver value faster through stronger product strategy and operating models. He holds a degree in Computer Science & Engineering from MIT.
Mike’s next blog in this series covers the ‘missing layer’ in AI Product Delivery.
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