Executive Coach, Certified Organizational Development Coach, Certified Life Coach, and Certified Positive Intelligence Such technology includes: artificial intelligence and machine learning, quantum computing, renewable

singularity, a concept popularized by futurist Ray Kurzweil, refers to the point where AI surpasses human intelligence However, these are narrow applications, not the broad Artificial General Intelligence (AGI) that Kurzweil Uncertainties Remain The development of AGI (Artificial General Intelligence) is surrounded by uncertainties

As the realm of artificial intelligence (AI) continues to expand into various domains, the question of experience the benefits of coaching through AI, their exposure could foster demand for more advanced and intelligent “Systemic eclectic”: Coaches that can “exhibit a sensitive, intelligent approach to the client situation While AI coaching's mechanistic execution of goal theory compensates for its lack of nuanced intelligence Comparing artificial intelligence and human coaching goal attainment efficacy.

In recent years, artificial intelligence (AI) has emerged as a game-changer in this quest for efficiency

Most executives can now name the AI agent handling their expense reports, drafting their first-pass contracts, or triaging their inbox. Fewer can say what a “good quarter” looks like for that agent, or what happens when it quietly underperforms. While executive teams have gotten comfortable deploying AI colleagues, they have not yet gotten comfortable evaluating them. The scale of the shift explains the urgency. Gartner projects that by the end of 2026, 40% of enterprise applications will embed task-specific AI agents, up from under 5 percent in 2025 (Gartner, 2025). Field research from MIT’s Initiative on the Digital Economy is already documenting how teams that work alongside AI agents perform differently on real tasks, not hypothetical ones (Ju & Aral, 2025). Agents have moved from pilot projects to production, but what has not moved at the same pace is the discipline of reviewing their work the way a manager reviews a direct report’s. Providing AI Feedback Most leaders were trained, at some point, on a model for delivering feedback to a human being. The Situation-Behavior-Impact model, developed by the Center for Creative Leadership, is among the most widely used: name the situation, describe the specific behavior, and state its impact, keeping interpretation and personality out of the conversation entirely (Center for Creative Leadership, 2025). The model works because it forces precision. For example, “You were unreliable this month” invites an argument. Whereas, “In the March renewal cycle, you flagged four contracts as low-risk that later required legal review, which cost the team roughly a week of rework” invites a conversation about what to change. That same discipline transfers cleanly to an AI agent, with one addition. A human behavior usually has intent behind it worth exploring, which is why CCL’s extended model adds a fourth step: asking about intent to turn feedback into dialogue (Center for Creative Leadership, 2025). An AI agent has no intent in that sense, but it has something that plays a similar role: the configuration that produced the behavior. Call it Situation, Behavior, Impact, and Root Cause. Situation names the specific task and context, not “the chatbot” in general but the exact workflow, prompt chain, or trigger that ran. Behavior describes what the agent actually did, in terms as observable as a transcript or an output log allows, resisting the temptation to say the agent “decided” or “chose” when what happened was closer to “produced.” Impact states the downstream effect in the same terms an executive would use for a human report: hours saved or lost, revenue affected, risk introduced, trust gained or spent with a client or colleague. Root Cause is where the review does its real work, tracing the behavior back to its source, whether that is the underlying prompt, the tools the agent had access to, the data it was trained or grounded on, or the absence of a guardrail that should have caught the error before it reached a client. Root Cause is also the hardest step, and the one most organizations have not learned to do well, because it cannot be answered the way a human review answers it. With a person, you can simply ask why. An AI agent cannot answer that question in kind, so the root cause has to be reconstructed rather than asked for, which means actually having the underlying prompt, the tool logs, the data the agent was grounded on, and a clear sense of what guardrail should have caught the error before it reached a client. Most organizations running agents in production do not yet keep that record in a form anyone can review after the fact, which means the step of the model built to turn a finding into a fix is the one most reviews quietly skip. That gap compounds in a multi-agent workflow, where one agent's output becomes another agent's input before a human ever sees the full chain. An executive should be able to answer a simple question walking into any review: if this chain produces a bad outcome, whose name goes on the corrective action. Research on agentic governance suggests most organizations cannot answer that question with confidence today (Cloud Security Alliance, 2026), the same ambiguity a coach would flag immediately in a human org chart, but one that AI's speed and opacity make far easier to leave unresolved. The Quarterly Review The record-keeping problem points toward a solution: review more often, in smaller batches, while the trail is still traceable. An annual AI review, the cadence most organizations default to for talent, asks a reviewer to reconstruct root cause across a year of logs and prompt changes, which is close to impossible and explains why so many of these reviews never happen at all. A quarterly AI performance review, run with the same seriousness as a talent review and folded into the same calendar, keeps the sample small enough that a root cause is still findable and the config that produced it likely has not changed twice since. For each agent operating with meaningful autonomy, the executive team or a designated owner should walk through the Situation-Behavior-Impact-Root Cause structure against a sample of its highest-stakes work from that quarter, name what changed since the last cycle, and make one of three calls: continue as configured, retrain or reconfigure with a specific root cause attached, or retire the agent from that task entirely. The point is to bring the same rigor that prevents human underperformance from going unaddressed for a year to a category of colleague that can now do a year's worth of damage in a week. The Main Takeaway The organizations that get this right will be the ones whose leaders treat evaluating an AI agent’s work as seriously as they treat evaluating a person’s, because the coaching skill underneath both is the same: specific, evidence-based feedback that someone is accountable for acting on. That skill was never really about humans; it was about closing the loop between what happened and what happens next, and right now, for most AI agents in most organizations, that loop is still open. References Center for Creative Leadership. (2025). SBI feedback model & talent development conversations. https://www.ccl.org/articles/leading-effectively-articles/sbi-feedback-model-a-quick-win-to-improve-talent-conversations-development/ Cloud Security Alliance. (2026). NIST AI Risk Management Framework: Agentic profile. https://labs.cloudsecurityalliance.org/agentic/agentic-nist-ai-rmf-profile-v1/ Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026, Up from Less Than 5% in 2025. (2025). Gartner. https://www.gartner.com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026-up-from-less-than-5-percent-in-2025 Ju, H., & Aral, S. (2025). Collaborating with AI agents: Field experiments on teamwork, productivity, and performance. MIT Initiative on the Digital Economy. https://ide.mit.edu/ Copyright © 2026 by Severin Sorensen. All rights reserved.

of prompting is not just about getting answers; it's about engaging with AI to generate meaningful, intelligent

In July 2026, at two of the most capable artificial-intelligence laboratories in the world, a boundary Why one event deserves ten lenses For the hundredth issue of the AI Daily Intelligencer, I spent the edition, with sources and the verified numbers behind each figure above, is Issue 100 of the AI Daily Intelligencer

Almost everyone arguing about artificial intelligence and jobs is arguing about a different thing, and

The most powerful advances in artificial intelligence do not discard history. built, which is why the oldest interface in computing has become the preferred hands of the newest intelligence A century-old finding from animal psychology has become the finishing school of artificial minds. 5.

Most executives now accept that artificial intelligence will reshape how their organizations operate. Claude 101 Claude 101 covers the core features most knowledge workers actually need: projects, artifacts

For most executives, artificial intelligence has already proven its worth as an analyst, a drafter, and

Nearly seven in ten C-suite executives report being seriously close to leaving their roles for reasons tied to their well-being (Silverglate & Fisher, 2022). That survey predates the current wave of AI adoption, but a more recent study gives the pattern a name and a mechanism. Researchers at Boston Consulting Group surveyed nearly 1,500 full-time workers and found that intensive AI oversight, rather than AI use itself, is driving a distinct form of mental fatigue they call "AI brain fry," marked by difficulty focusing, slower decision-making, and higher turnover intentions (Bedard et al., 2026). In other words, the exhaustion isn't coming from delegating more to AI. It's coming from what AI adoption asks leaders to personally hold, verify, and reconcile in real time. Workload vs. Cognitive Load Workload is a volume problem. You address it by delegating, automating, or saying no. Cognitive load is a coordination problem. It comes from holding multiple unresolved, often contradictory demands in mind at once and having to keep making sound decisions anyway. In 2026, AI adoption has become one of the primary sources of that load for the leaders responsible for it, which creates a strange irony: the technology meant to relieve pressure on the organization is intensifying pressure on the people steering its adoption. Consider what a typical executive is now expected to hold simultaneously. The board wants a credible AI strategy and a return on the capital already committed to it. Employees want reassurance that adoption will not cost them their jobs, delivered in language specific enough to be believed. Customers expect the polish AI promises without any visible seams. Regulators are still writing the rules the leader is meant to already be following. None of these demands resolves on its own timeline, and a leader cannot simply postpone one to focus on another. They coexist, and the executive is the only place where they all have to be reconciled at once. This is decision fatigue in its more dangerous form. It is not the familiar tiredness of a long day of meetings. It is the erosion of judgment that comes from constant context-switching between fundamentally different value systems, financial return, workforce trust, technical feasibility, and public accountability, all in the same afternoon, often in the same conversation. The BCG/HBR research on AI brain fry found this pattern is strongest in exactly this kind of high-oversight role, where marketing and HR functions reported it most acutely (Bedard et al., 2026). In coaching conversations, these shifts tend to show up in behavior well before they show up in a performance review. A leader with a strong instinct for people starts making calls that seem uncharacteristically flat. Meetings get shorter, not because the leader has grown efficient, but because their bandwidth for nuance has quietly narrowed. A Design Problem, Not a Resilience One Most organizations misdiagnose this as a personal wellness problem and respond accordingly, with a meditation app, an executive health screening, or a well-intentioned reminder to take a vacation. Those responses are not wrong so much as they are aimed at the wrong layer. They treat the leader as the site of the problem, when the leader is more often the last stop for a problem the system generated upstream. A wellness app cannot resolve the fact that a CEO is being asked to have a defensible point of view on AI governance, workforce redesign, vendor selection, and competitive strategy, all before most of the relevant standards and best practices have had time to mature. The more useful frame, and the one that should inform how coaches and organizations approach this moment, is that AI-era burnout at the top is substantially a design problem before it is a resilience problem. Leaders are not failing to cope. They are being asked to serve as the integration layer for decisions that no single person, however capable, is well positioned to make alone and in real time. The fix starts with reducing how much of that integration work has to happen inside one person's head. Three Shifts to Reduce Cognitive Load Three shifts help, and none of them require a leader to become more resilient in the abstract sense the word usually implies. Sequence Decisions The first is sequencing decisions rather than holding them open simultaneously. Many executives treat every AI-related question as urgent and current, which means they are perpetually revisiting the same four or five unresolved threads without ever closing one. A structured decision calendar, where governance questions, workforce questions, and vendor questions each get a dedicated window rather than competing for attention in every conversation, does more to reduce cognitive load than any amount of stress management technique. It converts an undifferentiated pile of pressure into a sequence, and sequences are something the mind can actually process. Distribute Judgement The second is distributing judgment rather than centralizing it further. A recurring pattern among executives navigating AI adoption is a reflexive instinct to personally validate more decisions, not fewer, because the stakes feel higher and the technology is unfamiliar. This instinct is understandable and almost always counterproductive. It concentrates load exactly where it should be relieved. Coaches working with these leaders should be asking directly which decisions genuinely require the executive's judgment and which have simply defaulted there out of habit or anxiety. Reduce Coordination Burden The third is using AI itself to reduce the leader's coordination burden rather than adding to it. Much of what currently lands on an executive's desk, synthesizing conflicting stakeholder input, tracking the status of parallel workstreams, drafting the fifth version of a communication to employees, is exactly the kind of structured, well-bounded work that AI tools handle competently. Used this way, AI functions as a release valve on cognitive load rather than another demand on it. The distinction is not about which tools a leader adopts. It is about whether adoption is designed to lighten the leader's coordination burden or simply adds a new category of decision to the pile they are already carrying. The Main Takeaway None of this argues that leaders don't need genuine rest, support, or attention to their own wellbeing. They do, and that need is real. But treating burnout purely as a personal deficit lets organizations off the hook for the structural conditions actually producing it, and it leaves leaders trying to out-willpower a problem that was never about willpower to begin with. The leaders who come through this period well will not be the ones who found a way to tolerate more. They will be the ones who, with the right support, restructured how much they were being asked to hold at once. That is a coaching conversation, a governance conversation, and increasingly, a conversation about how AI itself gets deployed inside the leader's own workflow, not just the organization's. References Bedard, J., Kropp, M., Hsu, M., Karaman, O. T., Hawes, J., & Kellerman, G. R. (2026, March 5). When using AI leads to "brain fry." Harvard Business Review. https://hbr.org/2026/03/when-using-ai-leads-to-brain-fry Silverglate, P. H., & Fisher, J. (2022, June 22). The C-suite's role in well-being: How health-savvy executives can go beyond workplace wellness to workplace well-being—for themselves and their people. Deloitte Insights. https://www.deloitte.com/us/en/insights/topics/leadership/employee-wellness-in-the-corporate-workplace.html Copyright © 2026 by Severin Sorensen. All rights reserved.