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We don’t often get to say this: our guest runs the organization that certifies much of the quality profession. Sid Bhatnagar is the CEO of ASQ, the American Society for Quality, a global body with members in more than 130 countries and 200-plus member-led communities. He sat down with Nick Capman for a conversation about how AI is changing what leadership actually requires, and we’re grateful he made the time.
Sid came to the CEO role by an unusual path for a quality organization. He’s an engineer by training who spent most of his career in technology strategy and implementation, much of it as a CIO in fast-moving financial services. He joined ASQ as Chief Information Officer in 2020, became COO and Chief of Staff, and has served as CEO for about three years. That mix, deep technology background running an organization built on process discipline, made him a sharp guest for this topic.
The conversation covers a lot: where AI pressure is actually coming from, how to balance human judgment against machine speed, what to do with the employee whose job AI will change, and how to think about return on investment when the technology won’t sit still. Here’s what stood out.
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Sid’s key insights and practical takeaways
If you’re short on time, here are the most important lessons from the discussion.
The pressure on executives now comes from the top down, which is new. For most of business history, culture around a tool came up from the technology group or the business unit. AI reversed that. Sid’s point is that the demand is coming straight from boards and investors, who want to see AI drive operational efficiency and keep products and services competitive. The executive’s job expanded as a result. You’re no longer just a decision-maker; you’re the steward of AI adoption and the driver of AI culture, and that’s a role the C-suite mostly hasn’t had to play before.
Insights arrive faster, so timelines compress. The old pattern was a leader posing a problem to their directors and VPs and waiting. Now an executive can get credible information quickly by working through a tool like ChatGPT or Claude over a cup of coffee, then handing it to the team differently: not “find this out for me” but “I’ve done this research, validate it.” Sid’s example is concrete. Work that used to carry a six-month expectation now gets expected in three.
AI may need to separate from IT the way IT once separated from everything else. Nick made the structural argument, and Sid extended it. Decades ago there were no IT departments. Now Nick sees AI and digital titles splitting off from IT, and he thinks executives shouldn’t be surprised to see a dedicated AI function emerge. The FDA Group is in the final steps of bringing on an AI consulting firm, and Nick frames it as an ongoing relationship like accounting or IT, not a one-time project, because annual planning can’t keep pace with the technology.
Who owns AI strategy is a genuinely open question. Sid asked a room of executives who owned their AI strategy and got different answers every time. Right now, ownership tends to land on whoever knows AI best in the organization. Nick ran the same experiment on a panel: he offered six departments and asked who owns it, and one panelist answered “yes.” Another attendee proposed organizing it like the branches of the U.S. government, executive, legislative, judicial, which Nick called the winner. The takeaway both men landed on: there’s no single right answer, because AI touches everything. What matters is what works for your organization, and the willingness to shift as you learn.
Keep humans on both ends of the process, not just in the middle. Most people talk about “human in the middle.” Sid reframes it. Ideation comes from the human side, what problem are we actually trying to solve. Then the tool does its work. Then validation comes back to the human. The tool is bookended by human judgment on both ends. That structure keeps an organization from getting caught in a loop of AI generating work for AI to check, and it keeps people focused on identifying real problems rather than letting the technology run loose.
Lean toward the critical parts of the business, not the tasks. Both converged here, and it’s the most useful career insight in the episode. Nick brought up a book he’s been listening to on the shift from tasks to systems. Task-oriented work will go away. What won’t is critical decision-making. His analogy: an anesthesiologist doesn’t get paid for “here comes the fun juice,” and a pilot doesn’t get paid for easy takeoffs and smooth landings. They get paid for what they know when things go wrong. His advice to professionals is to lean toward the critical decision-making parts of the business rather than sharpening skills AI will absorb. Sid tied it back to ASQ’s core: process thinking, system design, root cause analysis. Things go sideways regardless of which tool you pick, and the question is whether you have the mindset and skill set to design and think at the system level.
The mindset to hire for is innovation, change management, and system thinking. Sid named these three directly. The people worth bringing in aren’t the ones who claim they’ve done something 200 times, because nobody can say that about technology moving this fast. They’re the ones willing to say something new is happening and let’s explore it, not implement it blindly. He drew a line between the leading edge and the bleeding edge. As a nonprofit, ASQ can’t take R&D-style financial risks to sit on the bleeding edge, so the right mindset in the leadership team becomes essential.
Leadership through this is a healthy tension, a pull rather than a push. Sid described the executive’s job as pulling hard enough to bring people along but not so hard that something breaks. Historically leadership leaned more on push. The change management piece matters more now because the moment people fear for their jobs, innovation shuts down and appetite for change disappears. Building the ecosystem where that fear doesn’t take over is the executive’s responsibility, and it’s a space many leaders haven’t had to operate in before.
Executives owe their people upskilling, and employees owe the effort in return. Both framed this as a two-way responsibility. Sid sees it as critically important to upskill and reskill existing talent, not just hire for the future. ASQ has people with a year of tenure and people with 35 years, and he wants programs that serve across that whole range, which his people operations team is now putting in place. The flip side: if leadership is adopting AI constructively and has provided the skills, and an employee still refuses, that becomes a cultural-fit conversation. Nick added the employee’s half. It takes an employee of faith to raise their hand and say a tool could take over part of their job, trusting that a leadership team of good character and competence will reskill them or create new opportunities. That faith only works if leadership has earned it.
When there’s a right person but no seat, be honest and be generous. If the organization is shrinking and the revenue or investor pressure is real, hard decisions have to be made regardless of whether it’s the right person. You can’t create jobs to keep good people around. Reduction in headcount is never easy, and Sid said he takes it very seriously and feels it. The obligation is transparency: if there’s room and the person is willing to reskill, find the seat, but tell them plainly what’s happening. Nick went further into what he called the dark cave. When you do have to let someone go and it isn’t performance-based, make the severance generous, write the letter of recommendation proactively, connect them with recruiters, and do whatever you can to maintain the goodwill the employee gave first.
The executive skill set itself has to level up. System thinking at scale, governance, and accountability topped Sid’s list. AI isn’t a tool you can delegate the way you delegate a CRM or an ERP. Every executive has to have a role in the AI strategy, which wasn’t true of past technology. Nick noticed the irony: AI is supposed to make life easier and instead demands more of us. He compared it to education, learning things, then learning how to learn, then learning to think more analytically, and argued that what counted as an exceptional executive a few years ago no longer clears the bar. Sid added a specific new skill: prompt engineering. Not coding, but prompting. Nick joked it could replace college writing as a course, prompting for executives.
Operations and customer service give the fastest return; talent is the long play. Asked where AI delivers the most, Sid was direct. Operations is the bang for your buck, work that took two hours now taking twenty minutes. Customer service is close behind, chatbots built on real knowledge that cut call volume. The bigger, slower payoff is talent: invest in upskilling, reskilling, system and design thinking, and you build the foundation everything else sits on. For life sciences specifically, he flagged information gathering and product development, faster access to standards and regulatory information.
Advocacy for change has to be repeated, shared, and built on trust. For the people on the fence, neither the true believers nor the never-adopters, Sid’s advice is to build advocacy through transparency about what you’re trying to accomplish and why it matters, to the organization, the brand, and the individual. Most resistance to change is fear of losing a job, so leaders have to create a sense of security: we’re investing in this so you can work smarter, not harder, and we’re committed together. Nick added that the message has to be said repeatedly in different ways because a different version lands for different people, and advocacy can’t live in one person or one department. He returned to a formula he uses often: trust is character times competence. Employees will only extend the faith this transition requires if they believe leadership is both trustworthy in character and competent to deliver.
You can’t retrofit ethics. On keeping AI fair and aligned with organizational values, Sid was clear that ethics can’t be bolted on afterward. You start with what the organization stands for and work the AI through that lens. Feed it biases and you’ll get biases back. ASQ has an advantage here, since it lives in the world of standards where there’s little gray area, but for organizations with a potential bend, data quality is everything. Fair and ethical aren’t checkboxes. They’re continuous work.
Good data, not just data, is the real prerequisite. Nick shared an analogy from a recent conference: data isn’t power, good data is. Saltwater isn’t much use until you filter it. Many organizations discover that when they think they’re ready for AI, they’re not, because their data isn’t in order. Sid agreed so strongly that ASQ launched a specialized data quality credential, which has been getting good traction, built around the same three themes ASQ focuses on: innovation, risk, and data quality.
Think about return on time, not just return on investment. On ROI, Sid reframed the metric. Alongside return on investment, there’s return on time. A function that drops from five hours to three is real productivity in dollars and cents. If you’re looking to put in a dollar and get ten back, you’ll struggle to justify AI today. But a chatbot that cuts call volume 30 percent, built on the right system design and data quality, is real. Unless you’re launching a product directly tied to AI, the honest place to look for value is operational streamlining and faster, better-informed decisions. Nick was candid about his own real-world anxiety over the investment The FDA Group has made in its AI product, and Sid’s answer was to treat it as calculated risk with guardrails. ASQ launched an AI chatbot, Quincy, to give members access to its large repository of quality content in multiple languages, without burdening the bank, and it’s seeing good results.
One thing to bring back to your team
Ask who owns your AI strategy, and don’t accept a vague answer.
The most honest moment in this conversation was two experienced leaders admitting the ownership question has no clean answer, because AI touches every function. But “it touches everything” is not the same as “it belongs to no one.” Pick a structure, name the owner or owners, and stay willing to shift as you learn. Then ask the harder follow-up Sid and Nick kept circling: what do you owe the people whose work this will change, and are you actually delivering it, the upskilling, the transparency, the generosity when there’s no seat left?
The organizations that come through this well won’t be the ones with the flashiest tools. They’ll be the ones whose people trusted their leadership enough to lean into the change instead of bracing against it.
Sid Bhatnagar is the Chief Executive Officer of ASQ, the American Society for Quality, a global organization headquartered in Milwaukee with members in more than 130 countries and additional centers in Mexico and India. An engineer by training, he spent most of his career in technology strategy and implementation, serving as a CIO across manufacturing and financial services, including roles at Ziegler and Produce Alliance. He joined ASQ as Chief Information Officer in 2020, became Chief Operating Officer and Chief of Staff, and has served as CEO since 2023. He holds a bachelor’s degree from the University of Wisconsin-Whitewater and is completing an Executive MBA in International Business at Marquette University.
Connect with Sid on LinkedIn here.
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