It might be a cliché, but the one constant in product management is that nothing is ever constant.
But the changes that are now visible on the horizon, driven by the sudden emergence of functional AI tools, are difficult to comprehend even for product leaders who have become accustomed to rapid technological change.
Generative AI platforms like ChatGPT, Bard, GitHub Copilot and Midjourney are the forerunners in a wave of platforms about to revolutionise how all digital assets are created and managed. While we are still only beginning to explore how AI can be utilised, as AI systems become increasingly capable of generating websites, software, video, music, and animation, many of the skills that have been the foundation of the technology industry for decades will become redundant.
This isn’t hyperbole; this is happening now. The industry may be unrecognisable a year from now.
Any professional whose career depends on applying acquired knowledge should consider how AI will impact their future, and any company whose business relies on utilising these capabilities should be doing the same.
If product managers are still required in the future, the teams within which they work and the roles they play will be vastly different. As product leaders, we must recognise the impact this revolution will have on product management and the industries in which we work.
As AI tools for creating software become mainstream, the number of engineers required to create and maintain technology platforms will dramatically decrease.
Instead of written requirements being used to guide human engineers, they are being processed by AI and used to create working software.
Technical roles such as DevOps, quality assurance, security and delivery management will be able to be replaced by AI services operating with minimal human supervision. The ability for infrastructure to be managed as code will even allow AI to manage its own hardware requirements.
With large engineering teams no longer needed, Agile product development methodologies – such as Scrum and Kanban – will also become redundant. These frameworks are designed to improve the effectiveness of teams collaborating on software development.
The discovery and design phase of product development will consume most, if not all, of the time required to build a new product or feature. With fewer engineering constraints, the speed at which new products can be built will be limited only by the speed of their design.
Product roadmaps will become a relic of the past. When the desired attributes of a product have been decided, the development cycle will be nearly complete. The question of “when will it be ready to release?” will no longer be relevant. Once you know what ‘it’ is, most of the work will already be done.
The growth of the SaaS segment has changed the way businesses purchase software. For enterprises, the decision to buy or build software has been trending towards buy, as it has become more cost-effective to purchase and integrate software services than to develop them in-house. Smaller businesses have gained access to tools they could never have developed themselves, now at a price they can afford.
However, SaaS businesses rely on the fact that software development is a specialised capability as the source of the value they create. The ease, speed, and affordability of software development brought about by AI will destroy the value proposition on which the SaaS industry is based.
Enterprises will be able to develop software in-house or purchase services, both for a negligible fraction of the current cost. Service aggregators such as Google and Microsoft will provide smaller businesses with complete suites of tools to run every aspect of their business.
If any feature a product team conceives of can be added to a platform immediately – and then just as quickly copied by competitors – the question is no longer whether something can be done, but whether it should be.
The only criteria to consider will be whether customers believe a feature adds or subtracts value.
In the age of generative AI, product differentiation will be driven by strategic choices and a deep understanding of customer preferences.
Very small, highly-skilled teams will discuss ideas, select potential changes to test, generate updated software and monitor the results of experiments – with each iteration potentially lasting only hours.
Product squads may be replaced by small teams consisting of a product manager/marketer and one or more producers who use AI tools to create any required assets.
Some aspects of the role of the product manager may remain as they are today, as businesses will still need to understand their customers’ problems and anticipate how products can create value. Still, much of this work will be greatly streamlined through the use of AI to compile research, run experiments, and analyse data.
As planning, resource management and roadmaps become obsolete, product managers will need to focus on curation – filtering the overwhelming number of possible functions and features available to create a unique and valuable offering in a carefully selected market.
It seems clear that the use of generative AI will bring about unprecedented changes in how digital products are developed and managed – but right now, this may be the only clear thing.
Now more than ever, product leaders need to be focused on the future – anticipating challenges and targeting opportunities – to create the successful products, and successful product-led businesses, that will inevitably emerge.
Brainmates has partnered with hundreds of organisations to solve their product challenges and help them adapt to new technologies.