Industry leaders have been rapidly deploying AI programs, many of them hoping for a cure-all for operational and technological maladies of all kinds. Their optimism is not surprising, as tech vendors promising productivity gains, cost savings, and reimagined workflows have frequently sold it as such.
But what many organizations have been deploying can be likened to a Band-Aid, not a genuine or sustainable remedy for structural problems in their operations and technology stacks.
In my years of advising Fortune 500 companies on growth strategy, digital transformation, and operating model redesign, I learned that far too many are trying to use AI as a bandage on outdated systems, organizational silos and outdated processes.
Too few realize that the processes underlying the technology need to be reimagined, in order to realize the full potential of AI.
This is because, as powerful as it is, AI can’t fix a system that is fundamentally broken. It’s just a tool that amplifies the strengths and weaknesses of the work systems it touches.
The Band-Aid Approach
In advising organizations on their digital transformation strategies, I have seen AI deployed in the hope it will act as a silver bullet, only to see the technology backfire. For example,
- Chatbots to improve customer service: Some businesses have deployed conversational AI programs to relieve the burden on call centers. In theory, this could mean shorter wait times and lower call volumes. But when chatbots are bolted onto disconnected CRM systems and antiquated databases, they can only field the simplest questions. Any customer with a more complicated question is immediately put back in the line and back on hold. Even with AI in the mix, the situation is as frustrating and dysfunctional as ever.
- AI for supply chain management: AI can make a major difference in demand forecasting, but only if forecasts can be trusted. When AI is used on fragmented, incomplete, or outdated data streams, the output is often unreliable. Without addressing upstream data governance, using AI for demand forecasting can be compared to putting new tires on a car with a burned-out engine. They won’t take you very far.
This basic pattern can be seen in various industries, and it is because organizations are applying AI to existing workflows rather than rewiring those workflows.
In instances like these, AI is actually magnifying inefficiencies instead of eliminating them.
AI as a Core Design Principle
Organizations must stop trying to use AI as a patch and begin treating it as a core design principle to reimagine and future-proof their operations.
- In customer service, redesigning the entire support experience around AI-powered self-service, integrating natural language search with unified customer data, and creating seamless escalation paths to human agents are ways to go beyond the Band-Aid approach. Instead of a chatbot grafted onto a call center, the system is transformed into an intelligent, end-to-end support ecosystem.
- In supply chain management, instead of layering forecasting AI on top of fragmented systems, organizations can build centralized, AI-ready data platforms. These systems would integrate supplier, logistics, and sales data in real time, giving forecasting models accurate inputs. Forecasting can then become faster while also being predictive and actionable.
Real change requires more than new technology. It requires new thinking. Questioning long-held assumptions about how work is done and how teams are structured is a good first step.
This is easier said than done, especially for organizations where workflows have been developed over long periods of time. But new thinking is increasingly necessary, and is the only hope of turning around the dismal stats on satisfaction with AI deployments (and return on AI investments) among IT leaders.
How to Rip Off the Band-Aid
For organizations ready to go beyond the Band-Aid approach and experience the fundamental change that AI can bring, there are several practical steps to take:
- Understand your trouble areas before deploying AI: Map out your organization’s workflows and identify as many root causes of inefficiencies as possible, as this is smarter than simply tracking the symptoms.
- Ensure your data is ready: Make sure that AI has access to pristine, consistent, and integrated data streams. This is the way to generate output that can be trusted.
- Align people and processes: Redesign roles, responsibilities, and incentives within your organization to prepare for a future of AI-enabled workflows.
Organizations that stick with a piecemeal approach to automation risk falling behind rivals that outperform them with workflows fundamentally reimagined for the AI era. They are likely to remain dissatisfied with their AI rollout and struggle to see a clear return on investment.
Organizations that add automation to dysfunctional systems of work will miss out on AI’s full potential. Those that rethink their operations from first principles and put AI in the center of the equation are those poised to prosper in the future.
About Sundar Subramanian
Sundar Subramanian is the Chief Executive Officer (CEO) of Zyter|TruCare, a population health management technology and services company, appointed in mid-2025 to lead its focus on AI-driven transformation and integration of technology with expert services for health plans. He brings extensive experience from PwC (Strategy&), McKinsey & Company, and WellCare Health Plans, specializing in strategy, digital transformation, and operating model redesign in the healthcare sector.
