When AI changes the job, bring employees with it.
Deloitte found that most surveyed companies have yet to redesign jobs around AI. The people doing the work should help decide what changes and what becomes possible.
The number that caught my attention in Deloitte's 2026 State of AI in the Enterprise report was 84%. That is the share of companies Deloitte surveyed that had not redesigned jobs around AI capabilities. At the same time, many leaders say worker skills are the biggest barrier to using AI well, and educating employees is the most common response. Training matters, but I keep coming back to the people inside that 84%. If the work itself is changing, how are we helping the employees who already know that work shape what their jobs become?
For Ruby, redesigning a job starts by getting to know the person doing it. An employee can tell you which question comes up every week, which handoff delays a customer, and which decision needs more than a quick answer from a model. They also know the exceptions that disappear when a process is described from a conference room. We work hands-on with employees to document those steps, teach them how to use the tools available to their team, and decide together what can become easier and what should remain in human hands. The aim is to give people time for the judgment, relationships, and new responsibilities their work can grow into.
Deloitte's report raises a second question. Among surveyed companies, 37% said they were using AI at a surface level with little or no change to their processes, 30% were redesigning key processes, and 34% were starting to transform products, services, core processes, or even business models. The figures are rounded, and these are leaders' descriptions of their organizations. Still, the distinction matters. A faster version of today's task can be useful. A different way to serve a customer, use company knowledge, or make a decision may be possible once the team understands what the technology can do and rethinks the process around it.
Consider a team that prepares the same type of customer update each month. AI might help assemble a first draft from approved information, saving the employee time. That is a worthwhile improvement. But if the team can also identify changes sooner, bring the right person in before a problem grows, and give the customer a clearer next step, it has begun to create a different service experience. This is an illustration, not a claim about a Ruby client. It shows why we should ask what new capability a process could support, alongside how many minutes a tool might save.
The same thinking matters as AI tools begin to take on more steps in a process. An agent can prepare information or move a task forward, but a business still needs to decide what it may access, when it should pause, and who checks the result. Those choices make sense only when the people who know the business sit beside the people who understand the technology. Ruby helps translate between the two, using the systems a team already has, such as ChatGPT, Claude, or Copilot, and building the training around the work employees actually do.
A smaller company does not have to redesign every job at once. It can begin with one team and one recurring process, work alongside the employees who own it, and ask two questions: which part of this work should become easier, and what could we now do for the customer or the business that was not practical before? The answers will change as the tools do. Employees should be part of finding them, so they feel comfortable and capable in the work they are helping to shape.
Sources & further reading
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