Copyright Forbes
Nick Leighton, CEO and bestselling author. Exactly Where You Want to Be - guiding leaders in business growth and AI strategy. Business leaders understand that failure to adopt new technology will ultimately lead to losing their competitive advantage and market share. The challenge is that technology is advancing faster than most companies can adapt, resulting in a serious gap between what’s technologically possible and what organizations are equipped to handle. Too many companies are trapped in a cycle of failed pilot projects and poor deployments, as business leaders aggressively push AI. The problem isn’t with the technology itself, but the underlying approach to the implementation of AI. Instead of trying to chase every new gadget on the market, it’s critical for business leaders to recognize that most governance processes, training and bureaucracy in organizations were designed for a slower, pre-AI world. This is creating friction between cutting-edge technology and obsolete operating models. Success with AI depends on business leaders being able to create the right framework that allows organizations to quickly evolve alongside technology. 1. Align Your Team’s Strategy With AI Most AI adoption fails because it’s treated as a management mandate rather than a vital part of the business strategy. Leaders should start by communicating why AI is critical to the growth and success of the organization. For leaders wanting to champion AI, it should become a regular part of everyday conversations by talking openly about AI in executive meetings and sharing wins and lessons learned. Of course, actions speak louder than words. Every business leader needs to be ready to openly demonstrate their personal use of AI in the workplace. This will encourage others to embrace these tools. In addition, organizations need to have clear KPIs and specific, measurable goals in place that align with the adoption of AI. For example, leaders can set goals for each department to automate at least one process for the month with AI. This approach helps translate abstract technology objectives into tangible missions. 2. Activate The Team Through Training Most companies simply hand generic AI tools to their workforce and expect an instant productivity boost. But most people won’t instinctively know how to get the most value out of AI. Some organizations are solving this challenge by introducing AI literacy training. Unfortunately, this leaves the user with only a high-level understanding of how to use an AI model. Instead, leaders should focus on providing AI training that is relevant and specific to each team's competencies or role in the organization. For example, the finance team can be trained on how to use AI to summarize earnings reports or spot financial errors, while the sales team can learn how to leverage AI for customer engagement. In addition to training, companies can support building a network of AI champions who are ahead of the pack in terms of AI experimentation. These champions can support the organization by promoting adoption, sharing best practices through peer-to-peer sessions. 3. Amplify AI Best Practices One of the most effective strategies for scaling AI is broadcasting wins and providing access to best practices that the company has discovered throughout the AI journey. Celebrating wins openly helps build excitement throughout the organization and rewards individuals for actively embracing change. It also highlights ideas and new ways of leveraging AI that others might not have considered. Every win with AI, even if it’s saving just an hour, should be celebrated. It's important not to criticize failures with AI, as this could discourage experimentation and testing new approaches. Cascading best practices can be as simple as building a prompt library that can be shared with the team. This repository can serve as a one-stop shop for the prompts, workflow automation scripts and other resources that have proven successful. This repository can easily be updated and shorten the training curve for employees who are less experienced with AI. 4. Accelerate Adoption By Removing Bureaucracy Adoption of new technology is traditionally slow. New tools typically require an extensive and cumbersome review and approval process. As an AI-forward leader, it’s important to work toward streamlining the approval process without compromising security or creating other risks for the organization. One solution is to create a small, cross-functional team of experts to review and approve new applications. This approach ensures that the review process is thorough but also allows promising AI use cases to be green-lighted for use quickly. While there are significant opportunities to gain efficiency with AI, how the organization responds can also slow down its adoption. Too many companies are driving AI as a means to simply reduce their workforce. This could backfire, as eliminating jobs or punishing productive employees with more work may slow down their willingness to use AI in the first place. 5. Govern AI Through Effective Guardrails Many have concerns about the unbridled use of AI within their organizations. Without the right guardrails in place, AI could easily create legal, compliance, ethical and data security risks for the company. This concern is causing many leaders to hesitate when it comes to the implementation of AI. A great way to mitigate this risk is by creating clear guardrails for the team to follow that still empower safe innovation. A good strategy is to create a simple, one-page guide that the team can reference that outlines what activities are prohibited, what proprietary data is safe to use in the model, expectations on validating AI output and how to escalate concerns to management. An alternate approach would be to enable AI to govern itself by providing it with access to your governance policies and instructing it to prioritize compliance over any conflicting request. By feeding this information into the training dataset, the AI can also help notify the team if any information being provided requires special instructions to keep the company protected. Final Thoughts At my agency, we’ve made it a rule that every leadership team meeting includes one AI update—either a success story, a lesson learned or a question we’re tackling. When my head of operations started using AI to draft scope-of-work templates in seconds (what used to take an hour), it inspired three other team members to try similar automations. Now it’s routine. The ripple effect came from consistent modeling and a little public praise. With AI, hesitation equates to falling behind. By treating AI deployment as a strategic business transformation rather than a technical project, business leaders can give their organizations the right tools to continue chasing the latest technology.
