photo credit: Tara Winstead / Pexels

Key Takeaways

• Successful AI adoption depends on redesigning workflows and organizational structures rather than simply purchasing new technology.
• Organizations need to determine which tasks AI should handle, where human judgment remains essential, and how responsibilities change after implementation.
• AI delivers greater value when tasks, handoffs, and ownership are structured to reduce friction and minimize unnecessary review or correction.
• Building trust requires employees to understand both the capabilities and limitations of AI while maintaining clear expectations for human oversight and accountability.
• Organizations that redesign workflows, roles, and culture are better positioned to turn AI investments into measurable business results.

Devrie DeMarco holds an MBA from Vanderbilt University, where she specialized in marketing and business strategy and earned honors including the Unsung Leader Award and the Owen Service Award. She began her career as an Associate Product Manager at Revlon before joining Conde Nast as an Associate Publisher for the Digital Lifestyle Group, where she coordinated advertising initiatives and exceeded revenue targets. Devrie DeMarco later served as Head of Sales at BermanBraun, contributing to the company’s exponential growth and industry recognition. Since March 2014, she has served as Managing Director at MediaLink, a UTA Company, where she continues to drive meaningful growth for clients and has earned accolades for exemplary project leadership and strategic foresight.

Her media and marketing background informs her perspective on how organizations must rethink workflows and roles to realize value from artificial intelligence adoption.

While AI investments begin with a purchasing decision, meaningful results do not come from the tool alone. AI initiatives can stall, fail to produce desired results, or create new points of friction even if an organization procures the best model for the job.

The real question is not whether AI can perform a task. It is whether the organization has established the conditions and organizational transformations necessary to create value, measure outcomes, and avoid friction.

That distinction matters because AI operates inside existing work, not outside it. When a system is added that changes the steps or methods involved in a process, the workflow itself needs to adapt accordingly. An AI model may summarize information quicker, but the saved time or effort becomes negligible if no one trusts the output, knows when to check it, or understands how it changes the next step.

Moreover, research by MIT Sloan argues that AI’s efficacy depends on how tasks are sequenced, grouped, and handed off between people and machines. A tool may handle one step well and still add little if the next step requires repeated review or correction. The gain comes when the sequence changes enough to reduce friction, clarify ownership, and let each handoff serve a clear purpose.

Redesigning job roles matters for the same reason. A job that once consisted of searching, drafting, checking, and escalating may now divide those activities between people and technology.

The organization will need to reassess roles and determine which tasks to delegate to AI, where human judgement is paramount, and whether the redesigned workflow adds new tasks or skill requirements. Clear expectations become necessary because AI does not simply remove effort; it shifts where human attention, review, and accountability belong.

This is why role and workflow design carries more weight than procurement. Studies on the value and efficacy of AI in practice support the importance of such restructuring to achieve desired results. For instance, McKinsey’s research on generative AI found that workflow redesign had the strongest impact with self-reported EBIT impact among the organizational attributes tested.

Creating the right conditions for AI to deliver net-positive results also requires changes at the cultural level. Trust in new technologies and processes needs to be built into the operating environment, in addition to managing expectations for AI’s capabilities and applications.

Successful AI adoption depends on people knowing the limits of a system, not merely admiring its capabilities. Without that structure, skepticism and overconfidence can coexist in damaging ways.

A service operation shows the practical stakes. AI may draft responses, classify requests, or surface knowledge for staff, but meaningful improvement depends on the decision path around those outputs. Someone must know when a generated answer is sufficient, when a case needs human review, and which outcome defines success.

The essential step in AI adoption is therefore not choosing the most impressive platform. It is creating the conditions that equip the organization with the capabilities needed to use it.

Workflow, roles, and organizational culture are not secondary to deployment. They are the means by which AI moves from demonstration to results. The same research found that only 21 percent of respondents using generative AI said their organizations had fundamentally redesigned at least some workflows.

FAQs

Why is workflow redesign important for AI adoption?

Workflow redesign helps organizations integrate AI into existing processes by clarifying how tasks, decisions, handoffs, and responsibilities should change when technology takes over or supports specific activities.

Why does AI adoption require changes to job roles?

AI can shift which tasks employees perform, requiring organizations to reassess responsibilities, determine where human judgment is needed, and identify new skills or activities created by the redesigned workflow.

How does organizational culture affect AI implementation?

A supportive culture helps employees understand AI’s capabilities and limitations, build appropriate trust in the technology, and maintain accountability for decisions involving AI-generated outputs.

Can buying the right AI tool guarantee successful adoption?

No, an advanced AI system may produce limited value if employees do not trust its outputs, workflows are not redesigned, or responsibilities for reviewing and acting on AI-generated information remain unclear.

How can organizations get more value from AI investments?

Organizations can improve AI outcomes by redesigning workflows, clarifying roles, establishing human oversight, developing employee capabilities, and measuring whether the technology actually reduces friction or improves business results.

About Devrie DeMarco

Devrie DeMarco holds an MBA from Vanderbilt University and a Bachelor of Arts in Advertising from the University of Georgia. She began her career as an Associate Product Manager at Revlon, later serving as Associate Publisher at Conde Nast’s Digital Lifestyle Group and as Head of Sales at BermanBraun. Since March 2014, DeMarco has served as Managing Director at MediaLink, a UTA Company, where she continues to drive strategic growth for clients and has earned recognition for exemplary project leadership.