Adrian Wolfberg Of Organizational Insight Consulting On How Artificial Intelligence Can Solve Business Problems
An Interview With Chad Silverstein
The most positive impact would be strengthening those things that are human such as creative thinking, critical thinking, situational awareness, and empathy.
In today’s tech-driven world, artificial intelligence has become a key enabler of business success. But the question remains — how can businesses effectively harness AI to address their unique challenges while staying true to ethical principles? To explore this topic further, we are interviewing Adrian Wolfberg.
Adrian Wolfberg, PhD, is an author, organizational scientist, and researcher, and whose work focuses on how people generate, interpret, and act on knowledge. His work bridges the worlds of analysis, organizational practice, and strategic decision-making, with particular emphasis on how leaders and institutions manage uncertainty, cross disciplinary boundaries, and adapt to emerging risks and opportunities. Drawing on a 40-year career spanning national security, organizational research, and knowledge management, he is the author of two books and many scholarly and practitioner works on analysis, organizational adaptation, leadership, elite sports decision-making, and climate security, including his most recent work on collaborating human and machine thinking in the era of artificial intelligence.
Thank you so much for joining us in this interview series! Can you share with us the backstory about what brought you to your specific career path in AI?
My path to AI came through studying how knowledge is produced, not through technology adoption. What drew me into AI was the question of how knowledge changes when humans and machines both participate in producing it. The path to getting there was not direct. In the spring of 2020, as an adjunct professor, I started teaching students in a doctoral program at the University of Maryland to use a research methodology called systematic literature review. These types of reviews synthesizing existing knowledge from empirical studies — called the gold standard of “what we know” by the medical community — and use a rigorous scientific process to integrate empirical data from many studies to definitively, as much as possible, answer questions of concern. In the spring of 2022, again as an adjunct professor, I had the opportunity to supervise a number of undergraduate students at Rutgers University who, based on my guidance, used the systematic literature review method to address their questions of national security concern. One of the students intended to fold into their study the effects of artificial intelligence (AI) on the intelligence profession but the student did not have enough time during the semester because the study focused on other facets of the intelligence profession. That missing gap of not capturing what we know about the intelligence community’s beliefs and perceptions about AI triggered my interest. I spent the fall of 2022 on my own study to answer this question using the systematic literature review process and published a report called the “Perceptions of Artificial Intelligence/Machine Learning in the Intelligence Community.” The results of that study showed me that even though AI was still in the early stages of widespread integration into work and life, no one was thinking about how the flow of creating knowledge may be affected by it. By the spring of 2023, the initial introduction of large language models like ChatGPT became available to the public and came to my attention. By mid-2024, the increasing use of large language models in business and everyday life prompted me to return to the idea of how knowledge is created when both humans and machines are involved with knowledge production, the result of which was my book, “Who Leads When AI Thinks?”
Can you share the most interesting story that happened to you since you started working with artificial intelligence?
Throughout the process of writing and publishing the book, “Who Leads When AI Thinks,” I continuously scanned anything published that might cover AI from my perspective, but I could never find anything that does. So, the most interesting part was discovering a gap: many people were writing about what AI could do or how to govern AI usage, but far fewer were asking how AI changes the flow of knowledge, judgment, and leadership responsibility.
You are a successful leader in the AI space. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?
I think my contribution to the AI discussion comes from being a thought leader in the AI space. From my perspective, and what I included in my book, the three most important character traits are learning in order to build adaptive capacity (CURIOSITY), sharpening one’s mental lens in order to frame and reframe problems and reality as they change (DISCIPLINE REFLECTION), and making knowledge useful across boundaries in order to understand how contexts can affect one’s framing and reframing ability (BOUNDARY SPANNING).
Curiosity: There are multiple kinds of learning. An example from one of them is called cooperative learning which occurs when people are aligned, low stress, and a shared sense of direction. Here is a business example: A product development team in a tech firm is iterating on a well-known product line. Customer feedback is limited but direct and clear (e.g., requests for minor design tweaks). How learning facilitates reframing is by enabling minor adjustments and improvements with alignment across departments. On the other hand, learning may hinder reframing by striving for consensus which can stifle innovative thinking or prevent questioning of the product’s strategic fit.
Disciplined Reflection: With sharpening one’s mental lens, this entails understanding how we process, clarify, and apply knowledge. There are a number of patterns to this and for using an example, I will use “knowledge uptake” which is the internal capacity one has to incorporate new information and apply it. This is a healthcare example: A public health director receives an AI-generated alert predicting an increase in opioid overdoses. How cognition facilitates reframing occurs when the director asks the data science team what features the AI model is prioritizing. The response reveals that the prediction is based on social media activity. The director integrates this input into broader surveillance data before deciding on community interventions. By questioning the knowledge basis behind machine-derived inferences, the director avoids overreacting to a possibly inaccurate pattern. But it can also hinder. Say a director accepts the AI prediction as fact, assuming it reflects hospital data. Acting immediately, the director diverts resources toward emergency response, only to learn later the signal came from social media chatter unrelated to local conditions demonstrating how failing to question the evidence base can misdirect interventions.
Boundary Spanning: Knowledge bridging across domains and disciplines is the ability to connect understanding across boundaries so that all stakeholders are moving in the same direction. Again, there are a number of ways this is done. I’ll pick an example for what I call the “connector.” A connector is someone who is skilled at building and sustaining the routines, processes, and workflows that bridge differences in how knowledge is produced and used. Here is an example. In a business setting, a connector institutionalizes an “innovation checkpoint” after quarterly reviews where cross-departmental teams reevaluate existing assumptions. This ritualized pause enables mid-course reframing of strategic goals. When the connector role fails, they insist all market insights be entered into a customer relationship management system before discussion. This over-formalization slows recognition of new consumer behavior signals and blocks agile reframing.
What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions?
One misconception I have encountered is that the more senior a leader is, the more likely they may be to assume AI can solve almost anything quickly. The misconception is that AI is a technical issue that technical people can solve. But I argue that AI is a leadership issue, which means leaders must be educated on the role they play in shaping an understanding of the nature of the problem their organization is tackling as well as their responsibility to maintain awareness of the changes that take place over time. In the military, there is a saying that no military plan survives enemy contact. In other words, whatever one thinks they want to happen, many factors influence changes in reality and the need for reframing one’s approach. Plans, models, and AI outputs are all created under assumptions and when reality changes, leaders must reframe.
What is the most significant way AI can make a positive impact on businesses today?
AI’s positive impact may be that it forces leaders to clarify what human beings are uniquely responsible for. Yes, AI has many capabilities. But its biggest impact is it forces leaders (and society) to answer the question, what does it mean to be human in an era when both human and machine thinking are combined, and secondarily, what does it imply for the nature and character of organizations? So, the most positive impact would be strengthening those things that are human such as creative thinking, critical thinking, situational awareness, and empathy.
Share 5 ways AI can solve complex business problems? These can be strategies, insights, or tools that companies can use to make the most of AI in addressing their challenges. If possible, please share examples or stories for each.
1. Automation of Well-Structured Problems
For problems that are well-structured, rules are clear, and both inputs and outputs are highly understandable and predictable, these are ripe for automation. Think invoice processing, payroll routines, basic data validation, or scheduling. A business example is automated invoice processing in accounts payable departments. Optical Character Recognition and Robotic Process Automation extract data from incoming invoices and post entries into Enterprise Resource Planning systems without human involvement, except for anomalies or exceptions, which are then directed for human attention.
2. Human-in-the-Loop for Adaptable Processes
Some problems appear simple on the surface because they appear to follow predictable pathways. However, they may require ongoing human adaptability due to the environment or the inputs being susceptible to change or exception. These need a human-in-the-loop. Imagine a customer service interface flagged by AI as “routine,” but the human agent notices subtle contextual cues suggesting a greater need. Or, a manufacturing process that is generally stable but occasionally faces rare anomalies demanding quick reframing. In these cases, humans work alongside machines, supervising, adapting, and providing judgment where exceptions or learning are needed. A healthcare example is would be telehealth triage systems use algorithms to route patient symptoms, but nurses or clinicians intervene directly when symptoms are ambiguous or when the patient’s medical history suggests a need for adaptation such as catching atypical medication reactions that standardized flows might miss.
3. Amplifying Judgment for Complex, Data-Rich Problems
For complex, unstructured problems, but ones that do not demand constant reframing, the challenge is in scale, data richness, and subtlety. Examples include medical imaging, large-scale fraud detection, or monitoring global supply chains. Such problems benefit from the pattern recognition and computational power of AI, but humans remain the final decision-makers or interpreters. The approach is not to replace judgment but to amplify it. AI can process far more data than humans, surfacing patterns, outliers, or predictions that might be missed. In a national security example, organizations called information (or intelligence) fusion centers use AI to scan enormous data streams (e.g., social media, satellite feeds) to flag patterns indicative of threats. Human analysts review these AI-generated outputs to make operational decisions, but the need for adaptation is lower when the threat criteria remain stable over time.
4. Navigating Messy, High-Stakes Situations
The most challenging situations involve messy, high-stakes issues with no clear rules, shifting contexts, and the need for significant reframing. Think crisis response, innovative product breakthroughs, or navigating organizational culture change. In these situations, human expertise, intuition, and creativity are irreplaceable. AI may supply information, simulations, or hypotheses, but the core work is done by humans, including synthesizing multiple perspectives, challenging assumptions, and adapting dynamically as the landscape changes. Nurturing the conditions for reflective thinking, healthy debate, and reflective learning is paramount, as well as knowing when to prioritize human judgment over algorithmic outputs. A business example is navigating a major corporate crisis such as a sudden product recall or public relations disaster that requires executives to synthesize data feeds, stakeholder input, market signals, and sometimes AI opinion-mining. They must reframe their approach as new information emerges, requiring extensive expertise and adaptive thinking.
5. Leadership and Design Approach
I would frame this in four ways by focusing on conceptual frameworks that can serve as strategies for framing how to combine human and machine thinking.
How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?
I would start by identifying the small business’s most important value proposition and the problem it understands best. Then match the right mix of machine and human thinking to that issue. In other words, this is a leadership and design approach, not a technical choice. Start with one important workflow or decision where better speed, pattern recognition, or consistency would create value, but where business still understands the problem well enough to judge the output.
What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?
Figure out the nature of the problem under consideration first. Then, objectively assess what degree of AI can support that problem. Bring together people who have the three competencies I discussed previously: learning, framing and reframing, and bridging knowledge across organizational boundaries. Hesitation may be reasonable, but it should lead to disciplined inquiry instead of avoidance.
How will AI continue to shape the business world over the next 5–10 years? Are there any trends or emerging innovations you are particularly excited about?
I think there is a more useful version of this question. What I think is the better question is to ask how and what will leaders decide should be the nature of the human role in a mixed human-machine reality. This is ultimately about deciding what we are as humans and who do we want to be as workers, leaders, organizations, and societal members.
How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?
This is a very good question. I think this will depend on how well the technology is able to understand the outer boundaries of the context which will depend on having an exquisite ability to approximate the situational awareness of everything within that boundary. What I mean by “outer boundary of context” is the elements in time and space that shape the problem — the full set of human, organizational, social, technical, and environmental factors. Having such a comprehensive understanding of everything within that outer boundary involves separating external data from internal human knowledge. For customers, AI may personalize service but miss emotional context. For employees, AI may support work but create distrust if used without transparency. For the community, AI may improve responsiveness, but only if leaders understand broader social consequences. Reaching that level of understanding, what I would call the upper limit of situational awareness, will be a stretch for technology to achieve.
You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people through AI, what would that be? You never know what your idea can trigger. 🙂
A research and educational organization that helps leaders improve their ability to navigate the relationship between human and machine thinking. This could involve teaching leaders how to frame problems, preserve human judgment, use AI responsibly, and strengthen human capabilities rather than weaken them.
Another idea is to start a movement focused on strengthening human judgment in an AI-shaped world. The purpose would be to help leaders, organizations, educators, and communities learn how to combine human and machine thinking without weakening the human capacities that matter most: judgment, responsibility, curiosity, empathy, creativity, and moral awareness. I would want this movement to help people ask better questions before they adopt AI: What problem are we solving? What should remain human? What can the machine help us see? What might it cause us to miss? And who remains responsible for the outcome? The goal would not be to slow AI down, but to make sure that as AI becomes more capable, humans become more thoughtful, more responsible, and more adaptive.
How can our readers further follow you online?
Follow / contact me through LinkedIn profile, https://www.linkedin.com/in/adrianwolfberg/, and my website, https://www.oicllc.org/
Thank you so much for the time you spent on this. We wish you continued success!
About The Interviewer: Chad Silverstein is a seasoned entrepreneur with 25+ years of experience as a Founder and CEO. While attending Ohio State University, he launched his first company, Choice Recovery, Inc., a nationally recognized healthcare collection agency — twice ranked the #1 workplace in Ohio. In 2013, he founded [re]start, helping thousands of people find meaningful career opportunities. After selling both companies, Chad shifted his focus to his true passion — leadership. Today, he coaches founders and CEOs at Built to Lead, advises Authority Magazine’s Thought Leader Incubator. Learn more at www.chadsilverstein.com
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