.svg)
Enterprise technology is barreling toward the future at two different speeds. For decades, transformation was a slow and controlled process, with ERP implementations taking months, if not years. Today, AI solutions are developed in a matter of weeks.
Now, the gap between the speed of innovation and the speed of control is creating a significant roadblock for leaders. Most organizations are eager to move forward with AI, but few can make progress with outdated controls holding them back. Instead of slowing innovation down, some experts say the solution is developing a governance model that can finally keep up.
Alan Shearer, AI Strategy and Automation Specialist and Founder of SparkMind AI Agency, helps clients navigate this very challenge today. With 26 years of consulting experience as an ERP advisor and solutions architect at companies like American Express and FedEx, Shearer has a deep understanding of legacy systems and why traditional models struggle to keep pace with evolving needs. To solve the conflict between speed and value, he says, a modern governance framework will be necessary first.
For Shearer, the most obvious path is a 'North Star' approach that ties every AI initiative to measurable business value. The structure is simple: the C-suite defines the vision (e.g., 'increase sales by 20%'), and a steering committee translates it into practical guides. That way, every action is measured against the company's ultimate goals.
Show me the strategy: Forcing a direct link between every AI initiative and a measurable business outcome can prevent what Shearer called 'random science projects.' "Effective governance must clearly define how every solution is graded against the company's strategic goals. It's surprising how many clients can't clearly communicate their own goals and KPIs."
The common denominator: Eventually, that guide becomes a universal filter, allowing teams at every level to make autonomous decisions that still serve the core mission. "The North Star approach is the common denominator across all layers of the company."
But the North Star isn't a fixed point in the age of AI, Shearer explained. It's a direction of travel that demands constant adjustment. Misjudge the balance, and face potentially severe consequences.
A state of mind: An organization's entire operating rhythm must adapt to a faster, more iterative pace, Shearer said. "Flexibility is key. It's more than a process. It's a cultural shift, a different mindset that has to be understood from the start."
The ripple effect: The speed of automated workflows magnifies the impact of a single error, making oversight non-negotiable. "When you chain together agents to execute tasks, one mistake will ripple downstream in a heartbeat."
Escaping the sinkhole: Orchestration offers a direct remedy for one of the most persistent and costly pain points in IT. "Integrations can be a sinkhole of time, energy, and money. It doesn't have to be that way with the right applications and orchestration."
A clear framework solves another leadership challenge, according to Shearer: how to pivot without it coming across as a 'failure.'
Let the data lead: Shearer's approach removes personal blame from the equation and focuses the team on a collaborative, objective solution. "AI tools can monitor your systems, identify early deviations from KPIs, and proactively communicate that you're off course. They don't just flag problems. They suggest detailed course corrections."
But for Shearer, the real starting point for any AI transformation is people. Human-centric structures grounded in education are the foundation of effective governance, he explains. But in his experience, they also rely on a network of internal champions who understand AI within the context of their roles.
Get in the lab: To create a shared understanding of AI's opportunity and risk, Shearer said, "Take a 'lab approach' to AI transformation and form a change agent network with people from every level. Education needs to go all the way to the top, from board members down to the people doing the work."
Depends where you sit: A one-size-fits-all policy might fail, Shearer cautioned. Instead, tailor governance to the real-world risks faced by each role. "Security means something different depending on your role. For someone handling client data, that's their focus. For legal departments, it's securing documents. For management, it's protecting strategic goals from competitors."
Own your work: As automation handles routine tasks, the human role must shift from 'processor' to 'owner,' with full accountability for the final output, Shearer said. "You are the owner and the gatekeeper. You can't just take what an AI gives you and pass it on as a reflection of yourself."
Modern AI governance can be a playbook for acceleration, but only if organizations choose to see it that way. By setting a clear North Star, empowering people to take ownership, and utilizing flexible tools to manage risk, they can establish a system that generates more value than risk. His final message for enterprise leaders was clear: the biggest challenge of all will be building the culture and structure to use AI effectively at scale.
