The following is a collection of common mistakes that will absolutely derail your efforts to develop a winning AI strategy for your organization.
Based on insights from Richard P. Rumelt’s "Good Strategy/Bad Strategy" and Roger Martin's ("The Five Deadliest Strategy Myths").
Ignore at your peril.
One of the biggest mistakes is using impressive-sounding language that lacks real substance. "Fluff" creates the illusion of strategic depth without offering a clear direction or actionable plans. It lets people sound smart without actually saying anything.
Phrases like "leveraging advanced AI to revolutionize our operations" are meaningless. Focus on clear, concrete actions and objectives.
A strategy that doesn't surface real challenges and clearly define the problems it aims to solve will inevitably fail. If you don't know what obstacles you're facing, you can't develop effective solutions.
Instead of vaguely stating a goal to "adopt AI across the company," identify specific barriers like data silos or skill shortages that hinder AI implementation.
It's important to differentiate between goals and strategy. Goals like "become a leader in AI" are not strategies. They are desired outcomes that should result from executing a well-thought-out strategy.
Rather than simply setting a goal to "increase AI adoption," outline a guiding approach that includes actions like investing in training and improving data infrastructure.
Effective strategic objectives are actionable steps that tackle the identified challenges. Ineffective objectives fail to address key issues or are just plain unrealistic.
Avoid overly broad objectives like "transform all processes with AI within a year." Instead, set practical steps with measurable results, such as "implement AI-driven customer support tools to improve response times by 20% in the next six months."
Long lists of unconnected projects that don't reinforce each other. This often happens when trying to appease all stakeholders without making tough choices. Prioritize initiatives that align with your guiding approach, and make sure they support one another.
Overly ambitious goals without a clear path to achievement. They restate desired outcomes without detailing how to get there. Ground your goals in reality by breaking them into actionable, manageable steps.
Strategies that have universal buy-in often indicate a lack of difficult decision-making. True strategy requires focus, which means prioritizing some initiatives over others. Be prepared to make tough choices that send resources to where they're needed most.
Filling in a generic template with vision, mission, and values statements often skips the hard work of identifying specific challenges and actionable strategies.
Your strategy may well be as unique as your organization. Shape your planning process in such a way as to address your organization's unique challenges and opportunities.
Avoiding common pitfalls in AI strategy requires clear, specific, and actionable plans that address real challenges. Steer clear of vague language, define the obstacles, distinguish between goals and strategy, and set practical objectives.
Focus on coherence and alignment across all actions, and be willing to make the necessary tough decisions to drive your AI strategy forward. By doing so, you can ensure your AI initiatives are impactful and drive significant business value.
