The rise of consumer AI agents that perform tasks end-to-end is setting a new standard. B2B products must evolve from simply providing insights ('help me do this') to executing entire workflows ('do this for me') to meet rising user expectations.

Unlike software with discrete feature releases, AI capabilities are updated continuously in the background. Product teams must build mechanisms to constantly re-educate users on what the tool can now do, as its evolution is invisible to them and requires overcoming the 'blank page problem' repeatedly.

Simply building what users ask for can trap a product in old paradigms, like reinventing Photoshop's lasso tool for an AI context. A successful strategy involves staying slightly ahead of user adoption, introducing new capabilities that fundamentally change their workflow, and guiding them toward a more efficient future.

Previous technology shifts like mobile or client-server were often pushed by technologists onto a hesitant market. In contrast, the current AI trend is being pulled by customers who are actively demanding AI features in their products, creating unprecedented pressure on companies to integrate them quickly.

Historically, software was built for predictable human workflows. Now, with AI agents executing thousands of unpredictable, low-latency queries simultaneously, product design must prioritize their needs. These agents will eventually select their own infrastructure, fundamentally changing the B2B buying process.

Unlike electricity or semiconductors, which were enterprise-first, AI's power is accessible to consumers and businesses simultaneously. This creates a dynamic where employees, using AI in their personal lives, become impatient with slower, more cautious corporate adoption.

As people grow accustomed to AI agents effortlessly handling complex tasks in their personal lives, their tolerance for clunky, manual enterprise software will decrease. This 'consumerization of AI' will create bottom-up pressure on B2B vendors to incorporate similar agentic and intuitive capabilities.

The proliferation of AI has dramatically reduced development time, shifting the primary constraint in product delivery from engineering capacity to the customer's ability to learn and integrate new features into their workflow. More output no longer guarantees more value.

AI's proliferation means users now subconsciously expect products to anticipate needs and offer proactive help, not just be functional. This shift raises the bar for product experiences, demanding a move from designing features to designing behavior.