Artificial intelligence (AI) sits at the centerpiece of growth conversations, with organizations racing to automate workflows, improve efficiency, and scale faster than ever before. However, according to operations and transformation leader Elaine Joggerst, many companies are overlooking a fundamental reality: technology cannot fix processes that are not fully understood.
“You have to understand really where you are,” says Joggerst. “Because without it, people will mess you up.” As AI reshapes how work gets done, sustainable growth depends less on adopting new technology and more on understanding the operational foundations already in place. Before organizations can build scalable systems for the future, they must first uncover how work is actually being performed today.
The Hidden Gap Between Documented Processes and Reality
Many businesses still operate on workflows that evolved over decades. Paper-based processes were transferred into spreadsheets, manual approvals became digital forms, and temporary fixes slowly became permanent operating models. “We’ve kind of bandaged it up for a while,” Joggerst says.
The challenge is that official process maps often bear little resemblance to reality. Employees frequently develop workarounds that help them accomplish tasks more efficiently, even if those methods differ from documented procedures. “Sometimes the people doing the processes don’t do it like you think they do it,” she says. “They found a workaround that was great for them.”
These adaptations may remain invisible for years, but when organizations attempt to automate processes without understanding these unofficial workflows, they risk embedding flawed assumptions directly into new systems. Joggerst recalls leading a global change initiative only to discover that one region had diverged significantly from the organization’s established procedures. “We haven’t done it that way in a decade,” she remembers being told. “We haven’t followed the Americas process in so long. In fact, we don’t know what that process is.”
Building AI Readiness Starts With Organizational Understanding
The excitement surrounding AI has created another challenge. Many organizations are pursuing implementation before developing a shared understanding of what the technology can realistically accomplish. “People don’t see it as a tool or data. They see it as magic, which is just wrong,” says Joggerst.
Teams need clarity on where AI can create value, where human expertise remains essential, and which processes are suitable candidates for automation. Only after establishing that foundation can organizations prioritize what should be operationalized with AI. Just as importantly, employees must be included in the process. Without employee buy-in, even the most sophisticated technology initiatives can struggle to gain traction.
The individuals closest to day-to-day operations often possess insights that leadership cannot access through documentation alone. They understand exceptions, bottlenecks, customer needs, and historical decisions that shape how work gets done. “You really want them on your side,” Joggerst says. “If you don’t have them on your side, they won’t necessarily take it in and appreciate it.”
Why Strategy Often Becomes the Scaling Bottleneck
“Companies want to do amazing things all the time. Strategy comes from everywhere. They can imagine it. They have no idea how to get there.” Organizations frequently identify attractive growth opportunities but fail to build realistic pathways for achieving them. Timelines become compressed, dependencies are underestimated, and leaders assume transformation can happen faster than operational reality allows.
“I think strategy is the one that is kind of the weak link there,” Joggerst says. By contrast, technology and operations teams often take a more measured approach. Technology leaders understand where systems break. Operations teams understand the practical requirements for implementation. Sustainable growth emerges when strategic vision is grounded in operational feasibility rather than optimism alone.
AI Success Depends on Governance, Not Replacement
As agentic AI evolves from executing tasks to governing processes, many leaders are focused on how much work automation can replace. Joggerst says it’s the wrong question. “Please keep your employees. You can’t go and fire everybody.” Instead of cutting the workforce, thus eliminating institutional knowledge, organizations should repurpose it. Experienced employees understand company history, customer expectations, and operational context in ways autonomous systems cannot.
Joggerst envisions three critical roles for employees in AI-enabled organizations: governing AI systems, identifying new automation opportunities, and serving as human escalation points when technology reaches its limits. The final role may prove especially important. As companies rush to deploy AI-powered customer experiences, many are unintentionally creating barriers between themselves and their customers.
“I couldn’t get through the AI and I just gave up,” Joggerst says of a recent purchasing experience. “I really wanted to buy your product, but I couldn’t find what I was looking for.” Organizations that remove every human touchpoint risk damaging the very relationships they hope to scale. The companies that achieve sustainable growth will be the ones that first understand their people, their processes, and their operational reality before layering technology on top. To move forward successfully, organizations must first be willing to look back.
Follow Elaine Joggerst on LinkedIn for more insights.
