As a founder of an AI-powered digital transformation and product development company helping businesses innovate, automate and scale, here’s a short guide. 1) Empower your workforce with AI fluency: Maintaining a nimble and knowledgeable workforce is critical, fostering a culture that embraces technological change. Team collaboration in this sense could take the form of regular training about agentic AI, highlighting its strengths and weaknesses and focusing on successful human-AI collaborations. For more established companies, role-based training courses could successfully show employees in different capacities and roles to use generative AI appropriately. Executives should make sure a feedback mechanism is in place to optimize this human-AI collaboration. By having employees actively participate in error identification and mitigation, they can develop an attitude of appreciation toward evolving technologies while also seeing the importance of continuous learning. AI fluency also comes from collaboration across departments and specialists; for example, between engineers, AI specialists and developers. They must share knowledge and concerns to effectively integrate agentic AI into workflows. For your workforce to feel empowered, there must be a mindset change: We don’t need to compete with AI, we (and our cognitive abilities) are evolving with it. 2) Redesign your workflows around: According to a recent McKinsey survey, redesigning workflows when implementing generative AI has had the most significant impact on earnings before interest and tax (EBIT) in organizations of all sizes. In other words: AI’s true value comes when companies rewire how they run. The strategy involves a dedication to upskilling, as well as a complete overhaul of core business processes and aggressive scaling, keeping a keen eye on financial and operational performance. Although machines can’t be left entirely unattended and humans can’t stay on top of processing data in real-time, constant human-AI collaboration may not be the answer to everything when redesigning workflows. 3) Develop new ‘supervising’ AI roles: When recruiting, business leaders should seek candidates who are: 1) Adept at testing for model bias to ensure accuracy and identification of problems early in AI development; and 2) Experienced in cross-departmental collaboration, to ensure that AI solutions are meeting all the team’s needs. If you are an SVP or CTO — and unsure where to start — you may need a strategic partner to gain access to quality talent. This is table stakes to build enterprise-grade, AI-powered technology products to de-risk AI adoption.