In the current landscape of enterprise technology, Artificial Intelligence has moved rapidly from a futuristic concept to a consumerized reality. Every executive wants it, and every employee has access to it. Yet, despite the billions invested in digital transformation and the hype surrounding Generative AI, a familiar and frustrating pattern persists: the "Great Backslide."
This phenomenon occurs when an organization implements a cutting-edge tool or process, only to find that six months later, a significant portion of the workforce has quietly drifted back to their old ways of working—relying on spreadsheets, emails, and manual workarounds.
To truly capture the value of AI, we must look beyond the "shiny toy" aspect of the technology and address the human element. The solution lies in the intersection of empathetic change management and robust Process Orchestration.
The Psychology of the "Drift"
Why do employees revert to legacy processes even when superior technology is available? The answer often isn't incompetence; it is anxiety.
In a recent roundtable discussion with transformation leaders across the media, finance, and staffing sectors, a clear consensus emerged: technology is often shoehorned into organizations without considering the operational reality. When employees feel that AI is something happening to them rather than for them, they retreat to the known safety of their old habits.
David Brakoniecki, Chief Delivery Officer at BP3, pinpointed the root cause of this resistance. "Change drives uncertainty, the uncertainty drives fear, and the fear is what's creating the resistance," he noted.
When AI is introduced as a black box—or worse, a replacement—it generates a survival response. Employees cling to their manual processes because they offer a sense of control in an environment that suddenly feels volatile.
Symptoms of Adoption Wavering
The drift back to old habits is rarely a sudden rebellion; it is a slow erosion of the new process. While overt resistance is easy to spot, leadership must be vigilant for subtler symptoms of adoption wavering.
1. The "Shadow Process" and Parallel Ledgers
One of the most common external symptoms not explicitly covered in the boardroom is the creation of "shadow processes." This occurs when an employee dutifully enters data into the new AI-driven system to satisfy management but maintains a private Excel spreadsheet "just in case." This double-entry reduces productivity and indicates a fundamental lack of trust in the new system's reliability.
2. The "Swivel Chair" Effect
When integration fails, employees act as the "glue" between systems. If the new AI tool doesn't seamlessly talk to legacy systems, users end up manually copying and pasting data between windows. This friction is a primary driver of the backslide; if the old way was seamless (even if slow), and the new way is disjointed, the old way wins.
3. The 30% Stalemate
Industry peers in the insurance sector have noted that while you may win the hearts and minds of 60-70% of the staff, there is often a stubborn 30% who refuse to adapt. These detractors often cite that the new tooling takes longer or lacks the nuances of their manual methods.
4. Data Decay
A subtle sign of wavering is a drop in data quality. When employees are forced to use a system they don't buy into, they often input the bare minimum required to proceed. If your AI relies on this data, the output becomes flawed, confirming the skeptics' bias that the "AI doesn't work," creating a self-fulfilling prophecy of failure.
The Role of Process Orchestration
This is where Process Orchestration becomes critical. You cannot simply drop an AI model into a chaotic environment and expect efficiency. You must orchestrate the workflow to guide the human and the machine.
Orchestration acts as the guardrails for the transformation. It ensures that the "new way" is the only logical way to proceed. By integrating AI agents directly into the workflow—managing handoffs between people, bots, and algorithms—orchestration removes the friction that causes employees to drift.
David Brakoniecki highlights that while the market is obsessed with the consumerized, "co-pilot" style of AI, the real structural value lies elsewhere. "Horizontal AI and co-pilot mode... is a lot easier for most organizations [to start with]," he explains, but notes that "agentic AI... is probably where you're going to get disproportionate value creation over time."
Agentic AI, when properly orchestrated, doesn't just assist a human; it performs tasks autonomously within a governed framework. This reduces the burden on the employee, lowering the barrier to adoption because the "new way" actually requires less effort than the old way.
Strategies to Stop the Backslide
To prevent the drift and ensure your AI transformation sticks, organizations should adopt a three-pronged strategy:
1. Don't Reinvent Change Management
Leaders often panic when dealing with AI, thinking they need an entirely new playbook. They don't. As Brakoniecki advises, "Start with what you know, and think about how to expand it or adapt it to the new situation, rather than try to think of a completely new governance model."
Successful organizations in the staffing and retail sectors have found success by branding their projects internally—giving the roadmap a name and identity—and using "co-creation" sessions. By involving the detractors in the design phase, you turn resistance into ownership.
2. Orchestrate the Handoffs
Don't rely on users to remember to use the AI. Embed the AI into the process orchestration layer. If the workflow automatically triggers an AI agent to draft a document, categorize an email, or analyze a claim, the user doesn't have to make a conscious choice to adopt the technology. It simply happens. This moves the organization from "users with tools" to "orchestrated outcomes."
3. The Co-Pilot Gateway
Use "Co-pilot" modes as a training ground. Allowing employees to use assisted AI for low-risk tasks (like summarization or drafting) builds trust. It creates a "safe" environment where the uncertainty—and therefore the fear—is managed. Once the workforce is comfortable with the concept of AI assistance, moving to fully orchestrated, autonomous Agentic AI becomes a natural evolution rather than a terrifying leap.
Conclusion
The technology is ready, but are your people? The difference between a failed pilot and a transformed enterprise isn't the sophistication of the algorithm; it is the sophistication of the process surrounding it. By recognizing the symptoms of the "drift" and using process orchestration to cement new behaviors, leaders can turn the fear of the unknown into a competitive advantage.