To redesign work for AI, leaders have to look beyond adding new technology to existing jobs and processes. AI changes which tasks can be automated, which decisions can be accelerated, where human judgment creates the most value, and how work should move across an organization.
Many companies have not yet made that shift. Deloitte reported that only 16% of surveyed organizations had fully redesigned roles, processes, and operating models to integrate AI into work. Simply layering AI onto legacy ways of working can create incremental efficiency without capturing the larger opportunity. To better understand how leaders can redesign work for AI, I asked leaders to share their best advice.
Leaders on How to Redesign Work For AI
Channing Ferrer, Chief Revenue Officer and CEO of Americas of Brevo: Start with your org chart rather than your tool stack. Building AI-ready teams usually means restructuring roles around what AI now makes possible rather than bolting AI onto old job descriptions. Invest in giving people real hands-on time with the tools (not just a training deck), and reward experimentation publicly so trying-and-failing with AI doesn’t feel riskier than ignoring it.
Christian Lund, co-founder of Templafy: My best advice is to build your workforce around the two skills that will become more valuable as AI gets better: setting direction and validating outcomes. As AI takes on more of the execution involved in knowledge work, employees need to get better at what happens on either side of it. On the front end, that means being able to define the objective, provide the right context, and direct AI toward the outcome the business needs. On the other end, it means having the expertise and judgment to challenge the output, recognize when something is wrong or incomplete, and ultimately decide whether the work is good enough to use. The long-term implication is a redistribution of knowledge work that puts a greater premium on direction and judgment – knowing what to ask of AI and knowing when the output is good enough.
Bob Etris, Managing Partner of Evans Inc.: Building an AI-ready workforce starts with a disciplined leadership mindset. Too many organizations approach AI as an IT initiative driven by pressure to look modern. In reality, it is a fundamental redesign of how your business operates, rewriting daily workflows, roles, and legacy processes. True value lies in human augmentation, not just automation. AI should help people make better decisions, remove unnecessary friction from employees’ work, and recover time to focus more on higher-value problems. If your team feels AI is being done to them instead of for them, adoption will always be an uphill battle. The organizations making the most progress are investing in continuous education as much as they are in new tools. Leaders don’t need every employee to become an AI expert overnight, but they do need to create an environment where experimentation is encouraged, questions are welcomed, and learning becomes part of the culture.
Shenal Vanderwall, CEO of Meeedly: I believe changes are necessary in two key areas. Organizations have already begun adapting and growing alongside AI. Through conversations with companies, I have seen how AI has helped reduce unnecessary spending, increase revenue, identify operational inefficiencies, and improve overall workplace productivity. Modern enterprises already rely heavily on employee training and external consultants to improve performance. The same approach will be applied to educating existing employees on emerging AI technologies and helping them adapt to new ways of working. Organizations that fail to embrace AI-driven transformation risk becoming less competitive in the future. I believe successful adoption will come from demonstrating measurable results through small, practical changes before expanding across the entire organization. Redesigning future workflows around AI capabilities and aligning future hiring strategies with these principles will be essential. Companies that effectively integrate AI into their operations will create more efficient, innovative, and productive workplaces. Before investing in the tool, it is important to ensure the team is ready to utilize it.
Jeremy Barnett, CEO of RAD Intel: The fastest way to build an AI-ready workforce is to document the judgment already inside the company. Founders and operators make hundreds of decisions based on experience, context, and pattern recognition, but most of that knowledge lives in their heads. AI becomes useful when you give it access to how the business actually thinks, then let people challenge, refine, and apply the output. That’s what we integrate across our entire AI portfolio every day. We turn individual judgment into shared intelligence so every team can move faster without losing the context that made the company successful.
Gentrit Gojani, co-founder and CTO at KODE Labs: Start with what your people already know. Most companies buy an AI tool first and then go looking for work to give it. The better first step is to gather the knowledge your business actually runs on and put it in a form software can use. A lot of it sits in documents nobody has opened in years: manuals, procedures, past reports. More of it sits in the heads of people who have been there fifteen years and are close to retiring. If the system does not know how your business specifically works, it gives generic advice, your experts spot that within a week, and they quietly stop using it. People will use a system that knows their world. They ignore one that sounds like it read a textbook. Next, make the answers checkable and put the AI where the team already works. Checkable means one agreed source for each fact, so when someone doubts a number, they can trace it back in seconds. Teams work in handoffs and group conversations, and when the AI sits inside that flow, every problem solved and every correction made stays in the system instead of leaving with the person who made it. That is also how you keep the knowledge of a veteran operator after retirement, which is one of the most expensive things a company loses today. Then move routine work to agents. Almost every process in a business is a sequence of steps that crosses several systems, with a person acting as the glue: pull a number from one place, check it against a document, decide, enter the result somewhere else. An agent can run that whole sequence, and the valuable part is what happens at each step. It reasons its way through using what the first two moves gave it: the knowledge you captured, the connected data, what the team did the last time this happened, and the instructions your own people have written for it. An agent without that grounding will only ever be a script. Hand the work over one process at a time. Let the agent propose while a person approves, watch how often the person changes the answer, and widen what it can do on its own once the record supports it, with that authority narrowing again automatically if quality slips. The result shows up as capacity: your experienced people spend their time on judgment and exceptions, and the business handles more work without hiring people it cannot find anyway.
Laksh Gangwani, Chief Growth Officer of ViewTrade: Leaders building an AI-ready workforce should start with an honest conversation with their people about what they are actually trying to accomplish with AI. There is a lot of fear and anxiety tied to it, and pretending that’s not there just slows everything down. If the goal is to amplify your best people, work with them to focus on three areas: democratizing information, using AI to prepare and streamline work, and keeping human judgment at the center of decision-making. A lot of time gets wasted just looking for the right information. Leaders need to democratize this, take what your smartest people know, and make that knowledge accessible to everyone. Once people and AI have the right information, AI can also take the cognitive load of following processes off people’s shoulders. It shouldn’t just answer questions; it should help prepare and assemble the actual work. But decision-making stays with the human. AI’s job stops before that point, and it’s a line leaders can’t afford to blur. It’s tempting to let AI creep into decision-making because it’s fast and it sounds confident, but keeping human judgment at the point where consequences happen is what keeps trust intact, with your workforce and with your customers. Now, everyone in the organization is as smart and effective as the smartest person in the organization, and that’s the power of AI.
Phil Thomas, Chief Strategy and Operating Officer of Scotiabank: Our role as leaders is to help our workforce see AI not as another tool, but as a new way of working. Every major technology shift changes how work gets done. AI is different because it’s changing work itself. The opportunity isn’t simply to automate individual tasks or improve efficiencies. It is to rethink workflows from end-to-end, enabling people to spend less time gathering information, navigating processes and completing repetitive work, and more time applying judgment, solving problems, serving clients and generating new ideas. That shift requires leaders to create an environment where curiosity, experimentation and continuous learning are rewarded and become part of everyday work. People need the confidence to challenge long-held assumptions and explore what’s now possible. At Scotiabank, our ambition is to become a trusted AI-native bank, embedding AI at scale to transform client and employee experiences, while doing so safely and responsibly. Through Scotia Intelligence, our unified approach to data and AI, we are providing our global workforce with the tools, but our success to date has been largely driven by employees embracing AI and exploring new ways to apply it. Ultimately, an AI-ready workforce is defined by a willingness to learn, adapt, and unlock human potential.
Dr. Amanda Main, Chief Innovation Officer of the University of Central Florida’s Barry S. Miller College of Business: I have spent a lot of time helping people learn to use AI, and honestly, the training is not the part I worry about most. I worry about what happens when someone uses it well. A question sits underneath many conversations about adoption, whether people say it aloud or not: If AI lets me do this faster, what happens to me? Employees notice whether the time they save gives them room to do better work or merely clears space for more tasks. They also notice what happens when an experiment fails. With a clear personal downside and a vague upside, people will experiment quietly and share selectively. That is rational. Leaders need to answer that question before scheduling the workshop. People who help rethink their jobs should have some say in what those jobs become, and they should see some benefit from the value they create. That might mean more interesting work or simply keeping a little of the time they saved. Otherwise, we are asking employees to make their work easier to replace and trust that everything will work out for them. Most people are far too sensible to take that deal.
Pervinder Johar, CEO of Avathon: Building an AI-ready workforce starts with recognizing that this is not simply a technology rollout or a training initiative. It’s a fundamental shift in how work gets done. Organizations need people who understand AI, but they also need people who deeply understand the business. An AI engineer may understand the technology, but experienced operators understand the realities of running a factory, managing a supply chain, or maintaining a critical asset. That operational knowledge is exactly what enables AI systems to learn, reason, and make better decisions. Leaders also need to prepare for the reality that AI will increasingly take on many of the tasks and roles as they are performed today. As AI becomes more capable of learning, reasoning, coordinating work, and executing decisions, many operational responsibilities will become autonomous. The opportunity for people is not simply to continue doing the same work with better tools, but to take on new roles designing, managing, governing, and optimizing AI-enabled operations. The organizations that succeed will be those that invest as much in preparing their people for these new responsibilities as they do in deploying the technology itself.
Amanda Gulley, Chief of Product and Experience Design of ASU EdPlus: You do not build an AI-ready workforce by buying tools. You build it by changing what people believe is possible. Start with mindset. Everything else is downstream of it. Nothing changes a skeptic’s mind like watching it actually work. Using AI as an assistant and truly redesigning the way you work are two completely different things.
Marc Bivona, Clinical Associate Professor at the University of Illinois Urbana-Champaign’s Landuyt Center for Entrepreneurship: Don’t build an AI strategy. Build an AI culture. The organizations with an edge won’t have access to better models. They’ll have employees who are more curious, more adaptable, and more willing to rethink how work gets done. That means giving people permission to experiment, rewarding curiosity, and redesigning work around what humans do best: judgment, creativity, and relationship building.
Anthony Shaw, Global CEO of North Highland: Too many organizations start with the technology and expect transformation to follow. They roll out AI, but keep the same processes, structures, and ways of working. If the work doesn’t change, the outcomes won’t either. The key is to rethink how work gets done. I find it useful to separate work into three categories: supporting, core, and strategic. Supporting work is often highly repeatable and can be largely automated. Core work can be enhanced by AI to improve consistency, quality, and scale. Strategic work still depends on human judgement, creativity, and decision-making, but AI can dramatically speed up the analysis and insights that sit behind it. Each category needs a different approach, and treating them all the same is where many organisations lose value. Building an AI-ready workforce also means breaking down silos and organising around outcomes, not functions. The organizations moving fastest are bringing people together around missions and shared goals rather than traditional reporting structures. Success becomes less about the size of your team and more about the impact you create. Ultimately, becoming AI-ready isn’t a technology program; it’s an organizational transformation. The leaders who will win are the ones building new capabilities now, rather than waiting for perfect clarity. By the time everything feels certain, the opportunity will have passed.
Frequently Asked Questions About How to Redesign Work for AI
What does it mean to redesign work for AI?
To redesign work for AI means rethinking how tasks, decisions, roles, and workflows should operate now that artificial intelligence can perform or support more of the work. Instead of inserting AI into an existing process, leaders examine the process itself and determine what should be automated, augmented, eliminated, or handled differently.
Why should companies redesign work for AI instead of simply adding AI tools?
Existing workflows were designed around the capabilities and limitations of people and older technologies. Adding AI without reconsidering those workflows can preserve unnecessary steps and limit the value of the technology. The same principle applies when leaders lead digital transformation: changing the technology without changing how the organization works rarely captures the full opportunity. When organizations redesign work for AI, they can rethink the work from the ground up.
Which tasks should AI handle and which should remain human?
AI is particularly useful for repetitive work, information processing, pattern recognition, first drafts, and other tasks that can be performed within clear parameters. Human involvement becomes especially important when work requires judgment, accountability, creativity, empathy, relationship-building, or decisions with significant consequences. As technology reshapes executive decision-making, leaders also need to determine where faster analysis should inform a decision without replacing human responsibility for it.
How can leaders redesign roles for AI?
To redesign work for AI, leaders can begin by breaking roles into the tasks and outcomes they contain. Identify what AI can automate, what it can augment, what still requires human expertise, and what new responsibilities the technology creates. Roles can then be rebuilt around the areas where people contribute the greatest value.
What is the difference between AI adoption and how organizations redesign work for AI?
AI adoption focuses on whether employees use AI effectively. When organizations redesign work for AI, they go further by changing the workflows, roles, responsibilities, and operating models in which that technology is used. An organization can achieve widespread AI adoption while still using AI inside processes that were never redesigned for it.
When should organizations redesign work for AI?
Organizations should redesign work for AI when the technology can materially change how a workflow operates, not simply make an individual task faster. Leaders should look for opportunities to eliminate unnecessary steps, change how decisions are made, redefine responsibilities, and shift people toward work where human judgment and expertise create greater value.
How should companies start to redesign work for AI?
To redesign work for AI, start with a specific workflow or business outcome rather than an organization-wide transformation. Map how the work happens today, identify unnecessary steps and bottlenecks, determine where AI can improve the process, clarify where human approval or judgment remains essential, and test the redesigned workflow before expanding it.
How does AI change organizational roles?
AI can remove some tasks from existing roles, expand others, and create entirely new responsibilities. Employees may spend less time producing routine output and more time reviewing, deciding, coordinating, creating, or managing AI-enabled processes. Leaders who redesign work for AI need to think about role design alongside technology deployment rather than treating it as an afterthought.
