By Dr. Gleb Tsipursky

In the rolling hills of southern Ohio, Adena Health System is quietly undertaking a transformation that could reshape how health systems across the country think about Generative AI. Rather than treating AI as a buzzword or bolting it onto existing operations, Adena’s leadership is strategically designing a digital workforce—one that enhances care, improves clinician well-being, and reimagines how patients engage with healthcare. At the center of this change are Jamie Smith, Chief Information Officer, and Heather Sprague, Chief Human Resources Officer: I interviewed them about how they are steering AI implementation with clarity, purpose, and a deep respect for the human side of health.

A Strategic Start With Tangible Goals

Adena is no stranger to technological progress. As Smith described it, the system prides itself on being “cutting edge, but not bleeding edge”—eager adopters, but grounded in strategy. Their current push into AI reflects this ethos. Recognizing the uncontrolled proliferation of tools labeled “AI” across the industry, Smith and Sprague are pursuing targeted investments with well-defined objectives. Rather than simply reacting to vendor hype, they’ve set three core use cases for AI over the next 12 to 18 months: predictive analytics, clinician efficiency, and consumer-grade patient interactions.

The predictive analytics effort aims to move beyond retrospective data analysis. “We want to stop reacting to things weeks after they happen,” Smith emphasized. By training models on three years of historical patient data combined with current socioeconomic trends, Adena plans to forecast patient volumes, staffing needs, and financial projections with greater precision. This marks a fundamental shift from hindsight-driven planning to proactive, evidence-based decision-making.

Building Tools That Work for Clinicians

But it’s not just the back office where AI is being deployed. Adena’s most immediate AI pilot focuses on easing clinician burden—a leading contributor to burnout across the healthcare sector. Partnering with Microsoft’s DAX Copilot and Epic Systems, Adena is testing a language learning model that enables ambient listening and auto-documentation. Physicians speak naturally during patient visits while the AI captures and structures the conversation into medical notes, which the clinician then reviews for accuracy.

This seemingly simple change could have an outsized impact. “The first thing I want is to get the clinician away from the keyboard,” said Smith. “Let’s bring back face-to-face care.” By reducing documentation time, physicians could gain back nearly an hour each day—hours they can reinvest in patient care or personal time. Adena is tracking clicks, time in the record, and chart closure rates with Epic’s Signal analytics to ensure the pilot delivers measurable gains.

To prepare the ground, Smith’s team conducted a cross-functional needs assessment and benchmarked best practices from partners like the Ohio State University and national vendors. Physicians were involved from the outset. “Their feedback drove this entire initiative,” he noted. Weekly user groups and monthly committees surfaced key pain points and ultimately guided the selection of AI tools. This user-led approach is essential for buy-in, especially in regions like Appalachia, where there’s still cultural resistance to new technologies.

Navigating Resistance and Generational Gaps

Resistance is not just anticipated—it’s already showing up. While newer physicians fresh out of residency are often eager for innovation, some seasoned clinicians remain skeptical, preferring legacy tools or even dictation phones. As Sprague pointed out, “We’re trying to create solutions that work for all generations in our workforce.” It’s a delicate balance, and the challenge is not just technical, but deeply human.

Smith acknowledged the broader implications with refreshing candor. “If we do this right, we should be able to designate fewer people to manual functions and allow them to focus on a better overall patient care experience,” he said. “That’s a tough message because there is so much uncertainty in this healthcare arena.” While there’s no intent to cut jobs, the shift toward automation inevitably reshapes roles. Sprague emphasized retraining and reskilling as key strategies. “It’s about working at the top of your license—shifting responsibilities to the most appropriate role,” she said. This could mean moving certain tasks from physicians to nurses, or from nurses to medical assistants, while simultaneously expanding roles in IT and data governance.

From Patient Experience to Human Experience

One of the more striking shifts in Adena’s language is the move from “patient experience” to “human experience.” Sprague notes this change is intentional, blending the perspectives of patients, employees, and physicians into a unified focus on people, technology and processes. The human experience should have a direct impact on our employee’s well-being. This reframing goes beyond bedside manner or online portals—it’s about designing a healthcare environment that respects the time, intelligence, and needs of everyone involved.

Smith envisions a near future in which AI serves not as a replacement, but as an augmentation of care. He speaks of a “Nirvana” state: doctors finishing work on time and reclaiming their evenings, patients getting faster service and deeper insights into their health, and AI tools serving as reliable, invisible teammates.

That vision includes wearables and connected health devices generating real-time analytics, accessible through user-friendly AI interfaces. The goal isn’t shiny tech for its own sake, but “a digital workforce that enhances the caregiver team,” Smith said.

A Roadmap Rooted in Well-Being

The path ahead for Adena is complex. The AI governance council they’re forming will help ensure consistency, compliance, and safety. Risks—from biased data to over-reliance on automation—are real. But so are the opportunities to improve care, reduce clinician burnout, and build a health system fit for the future.

What sets Adena apart is their refusal to separate technology from the human side of healthcare. “Technology needs to be grounded in the benefit it brings to your local population and community,” said Smith. In a world racing toward AI-driven solutions, Adena’s thoughtful, inclusive, and transparent approach may be exactly what healthcare needs.

As more systems begin to integrate AI across the continuum of care, the model being built in southern Ohio is worth watching. It’s not about chasing the next trend—it’s about shaping the future of health with purpose.

About the Author

Dr. Gleb Tsipursky was named “Office Whisperer” by The New York Times for helping leaders overcome frustrations with hybrid work and Generative AI. He serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his two most recent ones are Returning to the Office and Leading Hybrid and Remote Teams and ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI. His cutting-edge thought leadership was featured in over 650 articles and 550 interviews in Harvard Business Review, Inc. Magazine, USA Today, CBS News, Fox News, Time, Business Insider, Fortune, The New York Times, and elsewhere. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages. His expertise comes from over 20 years of consulting, coaching, and speaking and training for Fortune 500 companies from Aflac to Xerox. It also comes from over 15 years in academia as a behavioral scientist, with 8 years as a lecturer at UNC-Chapel Hill and 7 years as a professor at Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.

By Dr. Gleb Tsipursky 

In today’s whirlwind of technological acceleration, organizational leaders are rightly asking one vital question: what mindset do we need to adopt generative AI successfully? From my interview with Rick Madan, SVP and Head of TEKsystems Global Services (TGS), the answer isn’t about racing toward shiny new tools—it’s about grounding that enthusiasm in business clarity, emotional intelligence, and governance that scales with ambition.

Through nearly three decades of technology leadership, Madan has seen plenty of hype come and go. But when it comes to Gen AI, he’s clear: while the possibilities are expansive, the process must begin with deep introspection and intentional design.

Learning From the Past to Move Into the Future

TGS’s journey with AI began not with generative models, but with traditional machine learning frameworks nearly eight years ago. The company’s early successes were rooted in hard business problems that demanded precision and creativity—like helping a global oil and gas firm predict the end-of-life of multi-million-dollar subsea sensors. That project alone saved tens of millions by using predictive models to anticipate and preempt equipment failures. From there, the team at TGS expanded their AI expertise into manufacturing optimization for chipmakers and later into fraud detection, customer support automation, and document processing for industries from finance to telecom.

But the real turning point, Madan explains, came with the emergence of generative AI in late 2022. No longer confined to deterministic workflows, organizations began exploring more autonomous, goal-driven AI agents capable of deriving insight and acting upon data without rigid rule-based structures. And that shift opened up a new world of both opportunity and complexity.

One illustrative case involved a global retailer that had initially leveraged traditional ML to personalize online shopping experiences. While the results were strong—enhancing CX and conversions—introducing generative AI agents created a step-change in outcomes. These agents not only improved fraud detection by collaborating in real time (e.g., between security and transaction agents), but also delivered dramatic increases in Net Promoter Scores. Yet, as Madan cautions, the jump from improvement to transformation required more than just technology.

It Starts With the Consciousness, Not the Code

For Madan, the most successful Gen AI projects don’t begin with technical requirements—they begin with a candid conversation about what matters most to the organization. Before any system is architected or any agent deployed, TGS takes its clients through a discovery process focused on what he calls the “consciousness” of the business. What are the real pressures—economic, regulatory, stakeholder-driven—that are shaping strategic decisions? What specific outcomes are desired, whether in customer satisfaction, cost optimization, or new revenue?

This isn’t just philosophical. It’s practical. Too many companies fall into the trap of letting technical jargon or platform vendors steer the conversation before truly defining what success means. By anchoring the process in outcome clarity—whether it’s stabilizing margins in a volatile supply chain or restoring historical efficiency benchmarks—TGS ensures Gen AI investments are tied to measurable, impactful goals.

Once this clarity is established, Madan’s team can deliver rapid prototypes that test hypotheses and prove value early. The days of long, speculative AI development cycles are over. Success is now about moving from minimal viable products to agent-based solutions with real user impact—fast.

Navigating Resistance, Risk, and Reality

Even with the right vision, Gen AI adoption isn’t frictionless. Madan aligns closely with a four-part framework for addressing common implementation challenges: psychological resistance, technical integration, structural misalignment, and systemic risk. But he adds his own nuance, starting with what he calls emotional change management (ECM).

In his view, too many organizations underestimate the human element of AI integration. Roles are shifting, legacy systems are sunsetting, and long-standing ways of working are dissolving. “Emotional change management almost becomes like a frog boiling for the contrarians,” he says. Meaningful adoption happens when organizations respect emotional realities—not bulldoze through them.

Technically, legacy systems, uneven data quality, and siloed infrastructures can still stall even the most promising AI initiatives. Structurally, Madan recommends cross-functional steering groups, not just to ensure stakeholder buy-in, but to reflect the reality that data and AI touch every part of the enterprise. Gen AI is not just an IT concern—it’s a business transformation issue.

And when it comes to risk, TGS is uncompromising. From data privacy and bias mitigation to hallucination control and IP protection, every generative solution must be backed by robust governance. Internally, TGS has codified its AI ethics under a simple but powerful acronym: FATE—Fair, Accountable, Transparent, and Ethical. Their legal team plays a central governance role, reviewing models, redlines, and outputs to ensure compliance and social responsibility. For clients lacking internal audit functions, Madan recommends tapping into specialized AI governance firms to build that oversight structure from the ground up.

Hype Is Loud—But Reality Is What Matters

Madan is quick to point out the hype cycle that Gen AI is riding right now. He’s seen the rhetoric from CEOs declaring they’ll run billion-dollar operations with no humans, only AI agents. But as he shares, many of those early braggarts are now reversing course. Customer satisfaction and business outcomes have dipped, and companies are relearning a lesson as old as enterprise itself: people still matter.

“There’s nothing that really feels like you could ever build in a lab that’s going to compete with the specialness of what we have inside us—our soul and our spirit,” he says. “But there is code that can be a companion, that can be an augment, that can be an accelerator to those things.”

The future of Gen AI, in Madan’s view, must involve a recalibration—not toward dehumanization, but toward harmony. That means building systems where agents assist rather than replace, where AI enables trust rather than undermines it, and where technological ambition never outpaces ethical responsibility.

Moving Forward With Grounded Optimism

The pace of AI innovation will continue to accelerate, and enterprises that fail to adapt will find themselves outpaced and outmaneuvered. But as Madan emphasizes, adaptation doesn’t mean surrendering to every buzzword. It means bringing humanity, ethics, and business rigor to the forefront of how we approach transformation.

For leaders seeking to harness Gen AI’s power, the key isn’t to jump in headfirst—but to step back, ask the right questions, and build from a foundation of clarity, empathy, and accountability. Because in a world of agentic possibility, it’s still the human mindset that determines whether the journey succeeds or stalls.

About the Author

Dr. Gleb Tsipursky was named “Office Whisperer” by The New York Times for helping leaders overcome frustrations with hybrid work and Generative AI. He serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his two most recent ones are Returning to the Office and Leading Hybrid and Remote Teams and ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI. His cutting-edge thought leadership was featured in over 650 articles and 550 interviews in Harvard Business Review, Inc. Magazine, USA Today, CBS News, Fox News, Time, Business Insider, Fortune, The New York Times, and elsewhere. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages. His expertise comes from over 20 years of consulting, coaching, and speaking and training for Fortune 500 companies from Aflac to Xerox. It also comes from over 15 years in academia as a behavioral scientist, with 8 years as a lecturer at UNC-Chapel Hill and 7 years as a professor at Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.