Ramp's VP of Growth warns that new technology like AI follows a "J-curve" of productivity. Teams may initially become less efficient as they spend time learning and reorganizing workflows away from old tasks. This dip is a necessary investment before productivity explodes, a crucial expectation for leaders to manage.

Despite proven cost efficiencies from deploying fine-tuned AI models, companies report the primary barrier to adoption is human, not technical. The core challenge is overcoming employee inertia and successfully integrating new tools into existing workflows—a classic change management problem.

Teams get the most from AI not by automating steps in an old process, but by reinventing the entire workflow around the desired outcome. This demands a willingness to let go of the "craft" and familiar processes, which can be a difficult cultural shift.

OpenAI CEO Sam Altman observes that even advanced users revert to inefficient, pre-AI workflows. The psychological comfort of established work habits—or "mind-muscle memory"—creates a powerful personal inertia that slows down the adoption of new methods, proving a bigger hurdle than organizational resistance.

Successfully implementing AI isn't an overnight process. SaaStr's Chief AI Officer dedicated three months solely to learning and building agents. This focused effort, which feels like a slowdown, creates a "slingshot effect" where productivity and scale later accelerate dramatically.

The biggest resistance to adopting AI coding tools in large companies isn't security or technical limitations, but the challenge of teaching teams new workflows. Success requires not just providing the tool, but actively training people to change their daily habits to leverage it effectively.

Teams embrace AI more quickly when it enables them to perform entirely new tasks they couldn't do before, like coding or advanced data analysis. This is more motivating than using AI for incremental improvements on existing workflows, which can feel less exciting and impactful.

To successfully implement AI, approach it like onboarding a new team member, not just plugging in software. It requires initial setup, training on your specific processes, and ongoing feedback to improve its performance. This 'labor mindset' demystifies the technology and sets realistic expectations for achieving high efficacy.

A key sign of successful AI adoption isn't a reduced workload, but an increase in the team's ambition and capacity for experimentation. By lowering the cost and time of innovation, AI empowers teams to generate and test more ideas, which is a more valuable outcome than simply doing the same work faster.