Faculty Member at The University of Texas at Austin McCombs School of Business
Answered 2 months ago
Name: Joe Sagrilla Title: CEO & Principal Consultant Company: Horizon Business Consulting LLC 1.What would you change first if you saw a team you were advising was burnt out? RESPONSE: "Even great teams burn out trying to deliver the impossible. The first thing I do is pressure-test whether expectations are realistic - usually by mapping what's been promised against actual funding, skills available, and whether anyone defined success criteria. A lot of the burnout I see stems from this mismatch." 2. As a consultant, how do you assess whether a team is actually ready for AI? RESPONSE: "Most teams can't connect their AI use case to the P&L - that's the first red flag. I also test whether the process can tolerate errors or needs human review that doesn't just duplicate the original work."
I'm Michael Akiva, Managing Partner of Jacoby & Meyers, contributing again to one of your queries that may be helpful for your article. I oversee operational delivery across a multi-office personal injury firm. If I see signs of burnout, I look at how volume is interacting with structure. In a firm that has operated for more than 50 years, steady intake is expected. Burnout usually surfaces when case volume increases but documentation standards or communication checkpoints are shortened to keep pace. Reinforcing discipline at intake and records collection prevents downstream pressure in medical coordination and litigation. When assessing AI readiness, I look at whether a team is overwhelmed by information or by ambiguity. In personal injury work, large medical files and investigative records are common, so AI would likely be most useful in organizing and retrieving data across complex case files. The goal would not be to replace legal judgment but to reduce administrative burden so attorneys and case managers can focus on strategy and client communication. Any adoption should support an already disciplined workflow rather than attempt to fix structural gaps.
1. What would you change first if you saw a team you were advising was burnt out? When I see a team member starting to burnout, the first thing I will look at is whether they have a a managable workload. Unrealistic workloads combined with shifting objectives will take their toll on a person's energy and motivation. The first thing action I will take is is to re-evaluate delivery expectations with other stakeholders, and try to create some breathing space for the person suffering from burnout. Another measure I take is giving regular feedback on project results and outcomes, and the responses of wider stakeholders. It is motivational and prevents my team from feeling like a cog in a machine. I would definitely not instantly try to offload some of their work with an AI tool. 2. As a consultant, how do you assess whether a team is actually ready for AI? I start by evaluating processes: if process workflows are neither well documented nor approached with consistency, then adding AI is folly and will create confusion rather than add value. A team is ready for AI once they have established clear and consistent processes, allowing them to gauge how AI improves their work, rather than blurring processes and subsequently, their outcomes. 3. What are the AI use cases teams ask for the most and how do they compare to the ones you actually recommend? AI tools that automate reporting and status updates are popular requests. But, I usually recommend starting with tools that support decision making: for example, in areas such as risk identification and data analysis. These tools will faciliate decision taking but will keep people close to the impact of their decisions (as I said above, being able to see tangible results and outcomes is highly motivating) meaning they feel in control, and empowered, rather than feel that AI automation is replacing them.
In a fintech platform where people are moving real money across 150+ countries, burnout often comes from fear of making the wrong call. When teams are reviewing payouts or compliance flags, a mistake isn't just a typo, it affects someone's funds. The first thing I change is tightening decision boundaries so people know exactly what they're responsible for and when to escalate. When assessing AI readiness, I only ask one thing: if this AI decision is wrong, can we reverse it safely? If the answer is no, it stays human. In our case, AI helps sort, tag, and prioritize support tickets and alerts, but anything that directly releases or blocks funds requires human review. It's tempting to use AI to speed everything up. But if you can't undo a mistake, you shouldn't automate it, especially when people's money is involved.
1. The first thing I change in a burnt out team is not the workload, it is the structure. Most burnout I see comes from too many layers, too many status updates, and too many people coordinating instead of doing, so I collapse the middle layer and let leaders work directly with a small group of accountable specialists who own outcomes. 2. I assess AI readiness by looking at documentation, decision logs, and workflow clarity. If a team cannot explain how work moves from brief to delivery without three meetings and ten Slack threads, AI will just amplify the chaos instead of removing it. 3. Teams often ask for AI to write reports or automate stand-ups, but I usually recommend starting with workflow clarity and repetitive admin like drafting briefs, summarising calls, and updating task systems. AI works best when it removes coordination overhead, because that is what made factory-era hierarchies necessary in the first place. Name: Callum Gracie Title: Founder Company: Otto Media (https://www.ottomedia.com.au)
1 / I always start by slowing things down, not speeding them up. Burnout often comes from leaders piling on solutions instead of clearing space for teams to breathe -- I've seen exhausted staff show more creative energy after just cancelling half their recurring meetings. 2 / I ask if they've solved their boring problems first -- things like messy workflows and unclear roles. If a team wants AI but still struggles to track deadlines or align priorities, it's like trying to install smart lights in a house with broken wiring. 3 / Most teams dream about AI for productivity hacks -- meeting notes, summaries, chatbots. But I usually nudge them toward better use cases like backlog grooming or knowledge management, where AI quietly saves hours without needing daily hand-holding. - Damien Zouaoui, Co-Founder, Oakwell Beer Spa, Denver https://linkedin.com/in/damienzouaoui
(1) I start by reducing non-essential work and clarifying priorities. Many teams aren't burned out from quantity alone--it's the constant context-switching and lack of control that drain them. Creating space for small wins rebuilds confidence and momentum. (2) Readiness for AI depends less on tech infrastructure and more on process consistency and data habits. If the team doesn't trust their own dashboards or can't explain their workflows, AI will just automate confusion. (3) Most teams ask for help automating meetings, task estimates, or sprint planning. I usually point them first toward AI for metrics hygiene--flagging anomalies, standardizing tags, and reducing reporting overhead--because trusted data unlocks everything else. -- Hans Graubard, Co-Founder & COO, Happy V
Whenever teams ask me about using AI for big analytics projects, I tell them to pump the brakes. We start with something simple, like automating weekly reports. At Seisan, that's what we did first. The team saved hours and started asking what else the AI could handle. Those small, real wins made them comfortable trying bigger things later. So find a low-stakes task, prove it works, and then go from there. If you have any questions, feel free to reach out to my personal email
When my crews started burning out, we tried something simple. We shuffled schedules and built in quick breaks during tough jobs. Suddenly people started talking more, mistakes dropped, and you could just see the team relax. Don't treat burnout like a personal failure, it's a process problem. You just have to listen and then make small changes. If you have any questions, feel free to reach out to my personal email
I find it helps to automate those annoying repetitive tasks. At ShipTheDeal, we set up an automatic weekly report and suddenly everyone just felt lighter. Burnout with remote work sneaks up on you, so I ask my team directly if they're okay. Honest, regular talks work better than any magic bullet. If you have any questions, feel free to reach out to my personal email
President & CEO at Performance One Data Solutions (Division of Ross Group Inc)
Answered 2 months ago
Most teams come to me wanting flashy AI dashboards, but I usually start them with something simpler, like automating their data entry. Once they see it saves time and cuts down on errors, they get on board and start asking what's next. My advice is to tackle the boring automation first. It builds momentum and actually frees people up for the work that matters more than another report. If you have any questions, feel free to reach out to my personal email
Every delivery team I work with initially wants AI to handle their boring admin tasks. But I've noticed the real game-changer isn't that. It's the AI that helps with communication, like auto-scheduling or status updates. That's what actually stops people from getting overloaded and working at cross-purposes. We switched to smarter scheduling and it cut down the weekly confusion, giving people back actual hours. My advice is to start with AI that keeps everyone on the same page instead of trying to automate everything at once. If you have any questions, feel free to reach out to my personal email
I scaled a solar operation from zero to $40M+ annual production in under three years, and I can tell you the first thing I change when I spot burnout is the scheduling chaos. When I took over operations at my previous company, crews were getting sent to start new jobs before finishing existing ones--what we called "glass on the roof" syndrome--and it destroyed morale because nothing ever felt complete. I immediately built a company-wide scheduling matrix that let teams finish what they started, and we saw production triple in eight months while actually reducing crew stress. On AI readiness, I assess it the same way I assessed nuclear weapons procedures on submarines: can your team handle the baseline process manually first? Before implementing our six-month Salesforce system, I made sure every department head could articulate their workflow on paper--if they can't explain it simply, adding AI just automates confusion. The discipline I learned as a Quality Assurance Inspector taught me that technology multiplies whatever system you already have, good or bad. Teams usually ask for AI to handle customer communications and scheduling, but what I actually recommend first is using it for the unglamorous stuff--permit tracking, inspection follow-ups, and warranty documentation. At Your Home Solar, the biggest operational win would be AI that catches when a utility company hasn't responded in 10 days, because that's where projects stall and customers get frustrated, not in the sexy customer-facing stuff everyone wants to automate first. **Ernie Bussell, CEO, Your Home Solar**
When my engineers feel burnt out, I first check their workload. I once paused a few side projects so the team could focus on critical safety checks. They immediately felt less overwhelmed and the quality of their work went up. Morale improved and we stopped missing deadlines. My advice is to have an honest conversation about what's actually needed and cut the rest. If you have any questions, feel free to reach out to my personal email
I can tell if a team is ready for AI not by their tech, but by how well they actually know their own processes. At Design Cloud, teams wanted AI to speed things up, but it only works when their existing workflow is solid. People often overestimate what it can do. If your foundation is shaky, AI won't help you. My advice? Fix your processes first. AI should amplify what you're already doing right, not patch what's broken. If you have any questions, feel free to reach out to my personal email
When I ran digital transitions at CLDY, I learned something. A team can be excited about AI, but if nobody can write down their processes, we're stuck. It's worse when people are hesitant to point out workflow problems. Knowledge trapped in someone's head kills any automation effort. So my first test is simple: can you walk me through your project steps? If not, we're not ready. It's about communication, not code. If you have any questions, feel free to reach out to my personal email
1. The first thing I change when a team is burnt out is workload clarity. Burnout usually isn't about working hard. It's about working on too many half-prioritized things at once. I force a ruthless re-prioritization so the team can actually finish work instead of living in permanent triage. 2. I assess AI readiness by looking at process maturity and data hygiene, not excitement level. If a team can't clearly define how work flows today or trusts its data, layering AI on top just automates confusion. Readiness shows up as clean inputs and clear ownership. 3. Teams usually ask for AI to write updates or auto-generate reports. What I actually recommend first is using AI for triage, risk flagging, and documentation summaries so PMs spend less time chasing context and more time making decisions. The real win is reducing cognitive load, not just producing more content. Justin Belmont, Founder Prose
Burnout is usually a signal of broken work systems before it is a people problem, so the first change is to reduce low-value administrative load and clarify what truly matters. Gallup reports that employees who experience frequent burnout are 63% more likely to take sick leave, making workload design and prioritization urgent levers. Readiness for AI is assessed by examining process maturity, data quality, and clarity of decision rights, because automation only amplifies what already exists. Teams often ask for AI to automate status reporting and documentation, while the higher-impact recommendation is using AI to support planning, risk identification, and scenario modeling where it meaningfully improves delivery outcomes.
Burnout is usually rooted in fragmented processes and constant rework, so the first change is simplifying workflows and eliminating low-value reporting and manual handoffs. McKinsey research shows that up to 30% of tasks in many roles can be automated with existing technologies, which immediately reduces cognitive load. Readiness for AI is assessed by examining process stability, data quality, and clarity of ownership, because automation only scales what already exists. Teams often ask for AI to generate reports and documentation, while the stronger recommendation is applying AI to demand forecasting, capacity planning, and early risk detection where it drives measurable delivery improvement.
Burnout usually points to unclear priorities and excessive manual work, so the first change is simplifying delivery workflows and removing low-value status reporting. McKinsey estimates that up to 60% of work activities could be automated with existing technology, which immediately reduces cognitive load on teams. Readiness for AI is assessed through process maturity, data quality, and baseline skills in analytics and critical thinking. Teams often ask for AI to automate documentation, while the stronger recommendation is using AI for planning support, risk identification, and scenario analysis where it materially improves delivery outcomes.