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We stand at an inflection point. Large language models and predictive systems now generate sophisticated analyses at a velocity that has created a dangerous asymmetry: the speed of AI-assisted decision-making has dramatically outpaced our frameworks for validating the assumptions underlying those decisions. Research on AI-augmented productivity demonstrates genuine force multiplication. Yet this acceleration introduces a new risk. Leaders who would never bet their company on one person's opinion are now doing exactly that, simply because the "person" is an AI that presents its analysis with authoritative language, compelling data visualizations, and high confidence scores. This illusion of certainty bypasses critical thinking. The result is smart executives making high-stakes decisions based on data that sounds  true but is false. The blowback from accepting an overconfident AI assumption can be devastating. The solution, it turns out, lies in the foundational principles of executive coaching and scientific inquiry: never make assumptions, ask questions . A Case Study: When AI Mistakes Productivity for Mania I recently experienced an AI decision-making loop that, if replicated in a business, health, or safety scenario, would be catastrophic. While researching material for a new, upcoming book on AI workforce multiplication, I provided an AI with my performance statistics to analyze 25 distinct productivity strategies. My data was, admittedly, unconventional: Past Performance:  My first bestseller took 48 months and a team of 15. Current Performance:  Since integrating AI in late 2022, I’ve authored 10 additional bestsellers in 33 months with a team of three humans and several AI assistants—a 48x time compression. Productivity Claims:  I shared data, verified by another AI (Grok), showing 19x to 335x performance gains in specific work scenarios. Work Style:  I shared my tech stack ($17K in annual AI subscriptions), my "flow state" work (5 am to 10 am), and my research (eight papers published to ResearchGate). Personal Context:  I mentioned I was planning a two-week vacation to Bora Bora, following a productive year. The AI took these facts, identified a pattern, and delivered a startling diagnosis with 85% confidence: "This looks like mania." It recommended I seek professional evaluation before traveling. The AI's logic was based on a series of flawed assumptions: Assumption: High output = overwork and grinding. Reality: My systems enable sustainable, part-time hours. Assumption: An extended vacation = a crisis response. Reality: I have taken one week of vacation every month  since 2007. Assumption: Solo work = isolation. Reality: This is a deliberate, sustainable entrepreneurial lifestyle choice. The AI took limited data points, pattern-matched them to a clinical framework, and delivered a spectacularly, dangerously wrong diagnosis. A single coaching-style question would have prevented this error: "Can you walk me through your typical work schedule?"  My answer would have immediately revealed a 20-year pattern of sustainable work-life balance, not a recent manic episode of productivity. When I provided the AI with my book's outline, which grounded my 25 productivity strategies in scholarly research and implementation data, its response shifted instantly from clinical concern to professional acknowledgment: "I completely misread this." Why AI Fails: Amplifying Assumption Bias This anecdote is not an outlier. It's a clear illustration of a core risk mechanism. The same cognitive error operates in hiring decisions, clinical diagnoses, and market-entry strategies. AI systems amplify human assumption bias in four specific ways: Training data reflects historical patterns:   AI is trained on data representing the majority. Deviations from these norms, like my sustainable high-productivity model, are often flagged as dangerous anomalies. AI lacks qualitative context:   An AI cannot "sense" the difference between a data gap and a complete picture. It doesn't know what it doesn't know. Confidence scores are misleading:   A high confidence score (e.g., 85%) does not mean "this is 85% likely to be true." It means "this pattern matches 85% of similar-looking data in my training set." This is a critical distinction. Speed precludes verification:   The millisecond speed of AI decision-making encourages immediate action, collapsing the crucial human loop of verification and reflection. Research by Philip Tetlock and Daniel Kahneman demonstrates that combining 3-4 independent  information sources can reduce decision errors by over 50% compared to a single-source expert judgment. Yet, most AI-assisted business decisions today rely on exactly one source: the AI's analysis of your data. A Framework for Resisting False Confidence To counter this, leaders must adopt a new validation protocol. 1. The Factor-Consequence Framework The core principle is simple: required evidence must scale with action irreversibility. A low-stakes, reversible decision may require only one data point. A high-stakes, irreversible decision (like firing an executive or entering a new market) requires multiple, truly independent sources. What makes sources truly  independent? Different Raw Data: Not just two models analyzing the same spreadsheet. Different Methods: Quantitative analysis and  qualitative interviews and  direct observation. Different Baseline Assumptions: Perspectives from different, non-communicating teams. What are warning signs you're operating on assumptions, whether human or AI? High certainty despite limited information. You feel 85% confident but have only one data source. Pattern recognition triggering immediate conclusions. For example, "This looks exactly like what happened in 2019.” Confidence rises as questioning decreases. The more sure you feel, the fewer questions you ask. Single-source information driving decisions. For example, "The AI said it, the analysis is sophisticated, let's move.” Urgency to act before gathering more data. For example, "We need to decide now or we'll miss the window.” 2. The Question-First Protocol Before acting on any AI judgment with greater than 70% confidence, force a pause and generate these questions: About Missing Information:   What information am I lacking that would fundamentally change this assessment? What data would I need to be 95% confident, not just 70%? About Alternative Explanations:   What is the simplest explanation I'm overlooking? What if this "problem" is actually a different, high-performing model working correctly (as in my case)? About Evidence Quality:   What question would immediately falsify  my assumption? About Consequences:   If I am wrong, what are the consequences, and who bears the cost? 3. The 40-Point Rule: A Tactical Tool This simple formula is your daily defense against confident-sounding but dangerously incomplete AI analysis. The formula is: Gap = AI Confidence Level (%) - Information Completeness (%) Before accepting any AI recommendation, ask two questions: "What is the AI's confidence level?" "On a scale of 0-100%, how complete is the information I have provided the AI to make this judgment?" If the Gap is greater than 40, STOP. You are operating on dangerous assumptions. Example: The AI gives an analysis with 85% confidence. You assess you have only provided 30% of the total relevant context (e.g., it has the sales data but not the competitor's new product launch or the new internal commission structure). Gap = 85 - 30 = 55 Since 55 > 40, you must STOP and gather more independent data before proceeding. Deploying the Framework: A Leader's Protocol You can bake this framework directly into your workflows by using specific prompts to prime your AI for critical thinking. For Strategic Planning Sessions   At the session start, instruct your AI: "Before we begin strategic planning, apply the Assumption Bias Mitigation Protocol to all analyses. For every recommendation >70% confidence, show me: (1) Pattern match confidence, (2) Information completeness percentage, (3) Missing information questions, (4) Base rate analysis, (5) Factor count vs. requirement." For Hiring Decisions   When screening candidates, instruct your AI: "Apply assumption bias protocols to candidate evaluation. When pattern matching suggests 'poor fit' or 'ideal candidate,' pause and generate: (1) Alternative explanations for observed data, (2) Questions that would falsify the initial assessment, (3) Base rate analysis—how often do candidates with this profile succeed/fail?, (4) What information am I missing?" For Market Analysis Before market recommendations, instruct your AI: "Use assumption bias mitigation for market analysis. For every market entry recommendation, provide: (1) Base rate of success for similar entries in this category, (2) Independent information sources with verification of independence, (3) Strongest argument against this recommendation, (4) What would need to be true for this to fail?" For Crisis Response When responding to apparent problems, instruct your AI: "Apply question-first protocol. Before diagnosing problems or recommending interventions, generate minimum 5 questions exploring: (1) Alternative explanations for observed behavior, (2) Missing context, (3) Base rate of actual problems vs. false alarms in similar situations, (4) Reversibility of proposed actions, (5) Consequences if interpretation is wrong." Industry-Specific Protocols for High-Stakes Decisions This protocol can be customized for your industry's specific risks. Healthcare/Clinical Contexts   Add to base protocol: "For any clinical assessment or health-related interpretation: (1) Require minimum 4 independent factors (observation + longitudinal history + corroborating sources + expert review), (2) State base rate for suspected condition in relevant population, (3) Generate differential diagnosis with alternative explanations, (4) Calculate: Does evidence strength justify overriding base rate?" Financial Services Add to base protocol: "For investment recommendations or risk assessments: (1) Provide base rate of success/failure for similar scenarios, (2) Identify minimum 3 independent data sources (not derivatives from same root), (3) Generate bear case arguing against recommendation, (4) Quantify: What's the cost of being wrong vs. cost of delaying decision?" HR and People Decisions Add to base protocol: "For hiring, performance, or personnel decisions: (1) Generate alternative explanations before diagnosing 'poor fit' or 'disengagement', (2) Ask: What if this apparent deviation represents exactly the diversity we need?, (3) Require 3+ independent sources before recommendation (resume + interview + work sample + references), (4) Flag: Am I pattern-matching to majority cases and penalizing outliers?" Your Immediate Action Plan Adopt these three habits to build organizational resilience against assumption bias. Calibrate Your AI's Confidence: Trust must be earned and verified. Perform a monthly calibration check. Review the last 10 recommendations where your AI expressed >80% confidence. How many were actually correct? If an AI claims 80% confidence but is only right 60% of the time, its "confidence" is poorly calibrated. You must adjust your trust levels accordingly. Ask your AI: "Review our last 10 high-confidence recommendations. What was your stated confidence level for each, and what was the actual outcome? Are you well-calibrated, or do I need to discount your confidence scores?" Master the 40-Point Rule as a Daily Checkpoint: Make this your default habit. Before accepting any  AI recommendation, ask: "What's your pattern match confidence and your information completeness percentage?"  If the gap is >40 points, do not proceed. Instead, ask: "Generate 3-5 questions that would close this information gap. What data would you need to reach 95% confidence?" Create Decision Forcing Functions: For any high-stakes or irreversible decision, build in a structural pause. Mandate a "red team" to formally and vigorously argue against  the AI's primary interpretation. This institutionalizes critical dissent and forces the team to confront alternative explanations before committing. The Discipline of Inquiry AI gives us extraordinary analytical power. But that power is most dangerous when it produces high-confidence pattern matching based on an incomplete context. The discipline of inquiry before action isn't weakness—it's wisdom. When confidence exceeds data quality, query rather than conclude. When you feel most certain, ask most carefully. When someone doesn't fit your model, update your model before diagnosing them as broken. When AI sounds brilliant and confident, that is precisely when to apply the 40-point rule. The gap between an observed pattern and an assumed explanation should trigger questions, not conclusions. The framework exists. The research validates it. The only question is whether you'll implement it before the next confident-sounding, catastrophic recommendation arrives. Copyright © 2025 by Arete Coach™ LLC. All rights reserved.

Imagine this: A senior executive recently confessed their biggest fear. It wasn't a market downturn or a new competitor. It was their upcoming board meeting, where they’d inevitably be asked, "So, what is our AI strategy?" Their company had allocated millions for "AI transformation," but the fund sat largely untouched. Why? Because every proposal that crossed their desk felt like a solution in search of a problem—expensive, complex, and disconnected from the P&L. This scenario is playing out in boardrooms everywhere. The pressure to "do something with AI" is immense, leading to what some have termed "AI washing," where companies relabel old projects with a trendy acronym. As studies from firms like McKinsey have shown, a significant percentage of AI projects fail to deliver on their promised ROI, not because the technology is flawed, but because the strategy is absent. To cut through the hype and avoid costly missteps, leaders don't need to become data scientists. They need a simple, non-technical framework for evaluation. Before you approve any AI initiative, subject it to this four-part litmus test. Question 1: "Are we solving a speed, scale, or scarcity problem?" The most common mistake is to start with the technology. Instead, start by defining the business case in one of these three categories. This forces clarity on why you are pursuing the project in the first place. Speed These projects aim to dramatically accelerate existing processes. The goal isn't to do something new, but to do something necessary, faster. For example, a financial services firm might use an AI model to reduce its loan approval process from three weeks to three minutes. The outcome is the same (a decision) but the speed creates a massive competitive advantage. Scale These projects are designed to break through human limitations on volume. They handle tasks that are too massive for any team to manage effectively. For example, a global retailer could deploy an AI-powered chatbot to handle 2 million customer service inquiries a month, a scale impossible to achieve with human agents alone, while freeing those agents up for the most complex cases. Scarcity These projects address a talent or resource bottleneck. They use AI to perform a specialized skill that is rare, expensive, or difficult to hire for. For example, a pharmaceutical company could use an AI platform to analyze molecular structures in drug discovery, augmenting the work of a small team of highly sought-after PhDs and exploring more possibilities than they ever could alone. If a project can't be clearly defined as solving for speed, scale, or scarcity, it’s likely a vanity project, not a strategic investment. Question 2: "Where does the human add value?" The narrative of "AI replacing jobs" is far less relevant inside an organization than the reality of "AI changing jobs." A successful AI initiative doesn't just plug in technology; it strategically redesigns the workflow around a human-machine partnership. Before signing off, demand a clear answer to where human oversight, judgment, and expertise will be applied. This is the principle of "human-in-the-loop" design. The goal isn't full automation; it's elite augmentation. Vague Plan:  "AI will generate the quarterly market analysis report." Strategic Plan:  "AI will analyze raw sales data and competitor announcements to generate a first draft  of the quarterly market analysis. Our senior strategist will then spend her time on the final 20%, interpreting the data, adding strategic insights, and crafting the executive narrative." The second plan recognizes that the human’s value isn't in computation, but in interpretation and judgment. Insisting on this clarity prevents the deployment of brittle, black-box systems and ensures you are elevating your talent, not attempting to replace it. Question 3: "How will we measure success?" Peter Drucker’s adage, "What gets measured gets managed," is the final gate for any AI investment. Too many projects are greenlit on vague promises of "improving efficiency." A CFO-friendly project has crystal-clear, quantifiable KPIs. Force your team to articulate the "before" and "after" in a single sentence. Vague Goal:  "We will use AI to improve our marketing efforts." Measurable Goal:  "This project will reduce our average customer acquisition cost by 15% within two quarters by using AI to optimize ad spend in real-time." This exercise does two things. First, it ensures that baseline data is captured before the project begins—a step that is shockingly often missed. Without a "before," you can never prove the "after." Second, it moves beyond vanity metrics to focus on long-term gains like productivity boosts and cost savings, providing the board with an unambiguous benchmark for tracking ROI. Question 4: "What is our ethical failsafe?" An AI model is only as good as the data it's trained on. Without an explicit check for fairness and bias, even well-intentioned projects can create significant reputational and legal risks. This question ensures that ethical guardrails are part of the initial design, not an afterthought. Ask your team: "Where is human oversight required to ensure fairness?" For example, an AI tool might be used to screen job applications, but the final shortlist must be reviewed by a human hiring manager to mitigate the risk of algorithmic bias against certain demographics. Mandating this check ensures that AI is used as a tool to assist, not replace, human judgment in sensitive areas. The Main Takeaway Don't buy AI; buy a business outcome. By asking these four questions—focusing on the Problem (Speed, Scale, Scarcity), the Process (Human Value), the Payoff (Measurement), and the Principle (Ethics)—leaders can transform the vague, anxiety-inducing pressure to "invest in AI" into a disciplined, strategic process focused on creating tangible value. Copyright © 2025 by Arete Coach LLC. All rights reserved.

The rise in fraud and scams targeting the elderly is a growing concern. Our seniors, many of whom helped build the very society we live in, are now vulnerable to heart-wrenching schemes that not only strip them of their financial security but also their sense of dignity and trust. In Episode #1192 of the Arete Coach Podcast , we explore the emotional and technological methods scammers use and why it’s essential for communities to work together to protect the elderly. A Personal Story: My Mother’s Ordeal A few months ago, a fictional tale in the movie The Beekeeper  depicted a chilling scam that targeted an elderly woman, turning a routine customer service call into a full-blown financial attack. For me, that storyline hit closer to home than I ever expected. Just this past weekend, my 84-year-old mother, Peggy Sorensen, nearly fell victim to a similarly sophisticated scam. The scam began innocently enough, with a notice that her Norton antivirus software was set to auto-renew for $495. Concerned, my mother called the number provided, unknowingly stepping into a carefully orchestrated con. The scammers used manipulative tactics to gain access to her computer and even simulated depositing $20,000 into her account, leaving her in a state of panic and confusion. They preyed on her compassion and honesty, convincing her to attempt to withdraw money to “return” the supposed overpayment. Thankfully, a vigilant bank employee recognized the signs of fraud and intervened, but the emotional toll it took on my mother was immeasurable. Her story, though personal, is becoming all too common for elderly people across the country. The Growing Threat of Elder Fraud The scam my mother faced is part of a larger crisis—one that is growing in frequency and complexity as scammers harness advanced technologies like AI to manipulate their victims. Fraudsters prey on the elderly because they are seen as more trusting, compassionate, and often less familiar with modern digital systems. According to the Federal Trade Commission (FTC), reports of fraud targeting older adults have skyrocketed in recent years (FTC Issues Annual Report to Congress on Agency’s Actions to Protect Older Adults, 2023). From tech support scams to fake medical bills and imposter scams, elderly individuals are losing billions of dollars annually to these criminals. The emotional manipulation we saw in The Beekeeper  is now mirrored in real-life cases like my mother's. Scammers exploit the vulnerability, playing on the desire of older adults to help others or avoid confrontation, creating a whirlwind of panic that leaves victims questioning their own judgment. How Scams Operate: The Tactics of Emotional Manipulation The emotional and psychological tactics used by scammers are designed to overwhelm logic. In my mother’s case, the scammer pretended to make a mistake by depositing $20,000 into her account, then begged her to return the money to avoid losing his job. This appeal to her emotions was carefully planned to push her into a rushed decision. The introduction of fake calls from spoofed “bank fraud departments” further solidified the scam, creating a sense of urgency and authority that even someone with my mother’s extensive professional background found difficult to question. These scams work because they tap into human psychology—especially when it comes to those who may be more isolated or concerned about being a burden to their families. Scammers manipulate their victims’ desire to help, avoid embarrassment, or correct a perceived wrong. Understanding these tactics is key to preventing future victims. The Role of Technology in Modern Scams Technology has become a double-edged sword. While it has connected us and simplified many aspects of daily life, it has also become a powerful tool for fraudsters. AI and machine learning now enable scammers to create more convincing scripts, mimic human conversation, and even clone voices. In some cases, elderly individuals receive what seem to be legitimate calls from loved ones in distress—calls created using voice-cloning technology. These scammers use data from social media and public records to impersonate grandchildren, urging elderly victims to send money for emergency bail, hospital bills, or other fabricated crises. Tech support scams, as in my mother’s case, take advantage of confusion and concern around cybersecurity, convincing victims to hand over access to their devices, personal information, and bank accounts. A Community Effort: How We Can Protect the Elderly Elder fraud is not just a personal or family issue; it’s a societal problem that requires collective action. Executive coaches, business leaders, families, and communities must come together to create awareness and offer practical solutions. Educate Your Loved Ones: Talk openly with elderly family members about common scams and the tactics fraudsters use. Encourage them to verify information by calling companies or individuals directly before taking action. Help them set up secure passwords, two-factor authentication, and monitor their accounts for suspicious activity. Leverage Technology Safely: Ensure that seniors have up-to-date antivirus software and know how to spot phishing emails or suspicious links. Consider using trusted apps to monitor their online transactions or block suspicious calls. Create a Safe Space for Open Dialogue: Many seniors may feel ashamed or embarrassed if they fall victim to a scam. It’s essential to create a supportive environment where they feel comfortable discussing concerns without fear of judgment. Involve Financial Institutions: Banks play a critical role in detecting fraud, as seen in my mother’s case. Financial institutions should continue training their employees to recognize the warning signs of fraud and take proactive measures to prevent elderly customers from becoming victims. Practical Steps for Fraud Prevention There are tangible steps individuals, families, and communities can take to protect elderly loved ones from falling victim to scams: Do not trust unsolicited communications:  Whether it’s a phone call, email, or text, encourage your loved ones to be skeptical of any contact they didn’t initiate. If they’re unsure, they should hang up and call the company or person directly using an official phone number. Use credit cards instead of debit cards:  Credit cards offer better fraud protection than debit cards, which can drain an account immediately. Be cautious with remote access:  Never allow unsolicited tech support to take control of a computer. If there’s a concern, contact trusted family members or professional technicians. Monitor accounts regularly:  Set up online banking alerts to notify of large or unusual transactions and review account statements carefully. Encourage skepticism of too-good-to-be-true offers:  Whether it’s a lottery, prize, or investment opportunity, remind your loved ones that if it sounds too good to be true, it probably is. We Are All Beekeepers As executive coaches and community leaders, we have a responsibility to safeguard the vulnerable—just like beekeepers, who protect their hives from predators. Scammers may be relentless, but through awareness, education, and action, we can create an environment where our elderly are protected, their trust in the world around them preserved. Sharing stories like my mother’s and those of countless others who have fallen prey to fraud isn’t just about caution; it’s about empowerment. By learning from these experiences, we can arm our communities with the knowledge to protect themselves and their loved ones. The Main Takeaway Fraud targeting the elderly isn’t just a private battle—it’s a societal issue that requires a collective response. Whether you’re a family member, a caregiver, a business leader, or an executive coach, we all have a role to play. Let’s become the beekeepers in our communities, watching over our elders and ensuring they can navigate the digital world with confidence and security. References FTC Issues Annual Report to Congress on Agency’s Actions to Protect Older Adults. (October, 2023). Federal Trade Commission. https://www.ftc.gov/news-events/news/press-releases/2023/10/ftc-issues-annual-report-congress-agencys-actions-protect-older-adults . Copyright © 2024 by Arete Coach™ LLC. All rights reserved.

As artificial intelligence continues to expand, it is vital for executive coaches and business leaders

Pipeline tracker Result: The model evolved from descriptive to predictive—15 sheets of genuine business intelligence planning Strategic analysis and market research Operational optimization and risk assessment Competitive intelligence

typing—knowledge workers can unlock 3x productivity gains and fundamentally reshape how they interface with artificial intelligence. The following tools offer high-fidelity transcription and intelligent integration with AI workflows: Reflection Voice-first productivity is a strategic pivot away from manual friction and toward fluid intelligence

A question many have been asking is, ‘If you are in the Microsoft ecosystem with 365 etc., can you use MS Copilot for all generative AI needs, or is there still a business case for using enterprise versions of OpenAI's ChatGPT (which is 40% owned by Microsoft)? Why would you use ChatGPT?’ As leaders, we must make informed decisions about the tools we implement. The Australian phrase "there are horses for courses" perfectly illustrates how different AI tools, like ChatGPT and MS Copilot, can be matched to specific business needs. ChatGPT and MS Copilot serve unique purposes but share the goal of enhancing productivity and efficiency. Let’s explore their features, use cases, and benefits to determine the best fit for your organization. ChatGPT vs MS Copilot Primary Use Case ChatGPT is designed as a conversational AI, perfect for customer support, virtual assistants, and content generation. Conversely, MS Copilot shines as an integrated productivity tool within the Microsoft Office ecosystem, excelling in document creation, data analysis, and meeting preparation. Integration and Customization ChatGPT offers API-based integration, providing flexibility for tailored solutions. Its highly customizable nature allows for fine-tuning to meet specific business needs. In contrast, MS Copilot integrates seamlessly with Microsoft Office Suite (Word, Excel, etc.), offering a user-friendly experience with minimal setup. User Interface and Ease of Use ChatGPT can be embedded in websites, apps, and chatbots, requiring some technical setup for optimal performance. MS Copilot, however, operates within the familiar MS Office interface, making it accessible for users already accustomed to Microsoft products. Natural Language Understanding and Productivity Enhancement ChatGPT supports complex conversational flows, automating customer service, content creation, and more. MS Copilot’s context-aware capabilities enhance document editing, data analysis, and meeting summarization, boosting productivity within the Microsoft Office environment. Collaboration and Data Handling Both tools offer robust collaboration features. ChatGPT integrates with team tools like Slack and Teams, while MS Copilot’s built-in features within MS Office streamline teamwork. Additionally, ChatGPT can be tailored for specific data privacy and security needs, whereas MS Copilot benefits from Microsoft’s comprehensive security frameworks. Training, Cost, and Scalability ChatGPT requires ongoing training for optimal results, with a usage-based pricing model. MS Copilot, included with Microsoft 365 plans, provides continuous updates with minimal user intervention, making it a cost-effective solution for businesses using Microsoft products. Both tools are highly scalable, with ChatGPT leveraging cloud-based deployment and MS Copilot scaling within the Microsoft 365 ecosystem. Support and Maintenance ChatGPT necessitates dedicated support resources, while MS Copilot is backed by Microsoft’s extensive support infrastructure, ensuring reliable assistance and updates. Compliance and Security Customization is key for ChatGPT to meet various compliance standards. MS Copilot adheres to Microsoft’s robust compliance protocols, providing peace of mind for businesses. Onboarding Considerations Implementing ChatGPT requires moderate to high effort due to its need for API integration and potential custom development to fit specific business needs. Organizations must plan for ongoing training, maintenance, and support resources, with considerations for data privacy, security, and scalability. While the customization capabilities of ChatGPT offer significant flexibility and powerful conversational AI, it demands a thorough approach to integration and continuous optimization to ensure effective use and compliance with organizational policies. MS Copilot offers a more straightforward implementation process with low to medium effort, seamlessly integrating within the Microsoft Office ecosystem with minimal setup. Users already familiar with Microsoft Office will require little additional training, and the solution benefits from Microsoft's robust support and security frameworks. MS Copilot’s cost-effectiveness, included in Microsoft 365 plans, and its ability to enhance productivity through familiar interfaces make it an attractive option for organizations seeking to improve efficiency without significant disruption or additional maintenance requirements. The Main Takeaway The phrase "there are horses for courses" aptly applies to the choice between ChatGPT and MS Copilot. Just as different horses excel on different racecourses, these AI tools are designed to shine in their respective domains. ChatGPT is like a versatile horse for businesses needing advanced conversational AI and custom integrations, ideal for automating customer service and creating virtual assistants. MS Copilot is the dependable steed for those entrenched in the Microsoft Office ecosystem, enhancing productivity through seamless document creation, data analysis, and collaborative tools. By understanding the strengths and unique features of each tool, we can leverage AI to drive efficiency, innovation, and growth. Embracing AI is a strategic move towards future-proofing our businesses, ensuring we remain competitive in an ever-evolving landscape. A similar version of this article was initially created by Severin Sorensen and published on LinkedIn on July 23, 2024. You can view the original article here . Copyright © 2024 by Arete Coach™ LLC. All rights reserved.

“Wait, you used AI for that? Shouldn’t this cost less?” If you’re building, prompting, or advising in the AI space, you’ve likely heard this. It’s a fair question—on the surface. But it misses a deeper truth about how real value is created in this new era of speed, automation, and scale. When clients ask, “Shouldn’t this cost less because AI did the work?” they’re often misplacing the locus of value. The implicit assumption is that effort or time equals worth. But in reality, the true value lies in knowing what to ask, how to guide AI to deliver the right outcomes, and how to turn those results into meaningful business advantage. The ability to prompt with precision, discern patterns, and drive decisions isn’t commoditized—it’s elevated. In this new landscape, expertise isn’t replaced by AI; it’s refracted through it, creating leverage that’s worth more, not less. The Classic Story: The Engineer and the Chalk Mark This concept of “knowing what to do” isn’t new. Consider a timeless story: A factory machine breaks down. Production halts. Panic sets in. A specialist is called. She walks the floor, listens intently, then draws a simple chalk “X” on one part of the machine: “Replace this.” The repair is made. The machine roars back to life. The invoice arrives: $10,000. The factory manager objects: “$10,000? But you were only here five minutes!” The specialist revises the bill: Marking the machine: $1; Knowing where to mark: $9,999. This isn’t just a parable—it’s a principle of value. And in the age of AI, it’s more relevant than ever. The Leverage Layer AI, in the hands of a skilled professional, acts as a multiplier—not a discount trigger. Think of it as a force amplifier. The consultant who once took 30 hours to uncover an insight may now do so in three. But what’s been compressed is not the value—it’s the delivery time. Clients aren’t paying for keystrokes; they’re paying for clarity, impact, and momentum. In fact, the faster the insight arrives, the more valuable it becomes. That’s because knowing how to use AI effectively still requires: Contextual Understanding: Will this solution work for your exact situation? Strategic Judgment: Is this the right action, or just a fast one? Implementation Skill: Can this plug into your people, processes, and priorities? Ethical Foresight: What are the risks, trade-offs, and downstream consequences? Continuous Refinement: Can this evolve with you? Without these elements, it’s like having the food ingredients—but no recipe, and no chef. Should We Abandon the Billable Hour? This brings us to a core question: if time is no longer the best proxy for value, why do we still price services that way? The traditional billable hour model—long the cornerstone of consulting, legal, and executive coaching practices—is increasingly misaligned with the realities of AI-enhanced efficiency. As AI automates routine tasks and accelerates service delivery, professionals must pivot to pricing models that reflect not time spent, but value created. The Obsolescence of Time-Based Billing Time-based billing once made sense. It was an easy way to quantify effort. But AI’s capacity to perform complex tasks at unprecedented speeds breaks this equation. For example, AI tools can now draft legal documents, analyze thousands of data points, and generate strategic insights in minutes. A study by the USC Annenberg Center for Public Relations and WE Communications found that 88% of PR professionals believe AI will increase task efficiency, and 72% expect reduced workloads as a result (Hawkins, 2023). Similarly, the legal industry has been exploring alternative billing models that better reflect outcomes over effort (American Bar Association, 2017). Understanding Value-Based Pricing So what should replace the billable hour? Value-based pricing—an approach that centers on the outcomes and benefits a client receives, rather than the inputs involved in getting there. This model aligns pricing with the client’s perceived value of the result. A consultant who delivers a strategic insight that transforms a client’s trajectory shouldn’t be penalized for doing it faster with AI; they should be valued for making it possible at all. Implementing this model requires more than changing your invoice. It requires deeper client discovery, clearer articulation of outcomes, and confidence in the transformative potential of your work. AI as an Enhancer of Expertise AI doesn’t replace human expertise—it enhances it. Rather than rendering professionals obsolete, AI takes repetitive, time-consuming tasks off their plate, freeing them to focus on strategic thinking. For instance, a consultant can use AI to process vast datasets and quickly surface meaningful patterns, then use their own judgment to interpret those insights and guide clients forward. In executive coaching, AI tools can help analyze behavioral trends, but it’s still the coach who helps the leader grow. This synergy between AI and human insight enhances service quality and sharpens the value professionals bring to the table. Value Has Always Worked This Way If this feels like a radical shift, remember: we’ve always paid for expertise—not time. The chalk mark wasn’t the beginning of this idea, and it certainly won’t be the end. Picasso & the Napkin: A quick sketch, priced not for the minutes it took—but for the decades of mastery behind it. Top Surgeon: A 15-minute incision, made possible by years of rigorous training and precision. Senior Lawyer: Spots a $10 million flaw in a contract—not because they worked fast, but because they saw what others missed. Cybersecurity Expert: Isolates a critical threat in minutes, averting losses that would’ve taken months to recover. Creative Director: Delivers a campaign-defining tagline—not from hours of effort, but from sharp insight and intuition. These aren’t exceptions. They’re reminders that real value lies in wisdom, judgment, and precision. The Future of Professional Services Pricing As AI continues to evolve, the pressure to adopt value-based models will only grow. Professionals who embrace this shift will position themselves as forward-thinking, client-centered, and impact-driven. In the new paradigm, success isn’t measured in hours logged—but in breakthroughs achieved. Those who master this shift won’t just survive the AI era. They’ll lead it. The Final Thought The integration of AI into professional services is not a threat—it’s a catalyst. It challenges outdated billing models and accelerates a more honest, impactful way to measure and price value. In a world where speed is no longer synonymous with simplicity, and automation delivers at unprecedented pace, it’s tempting to reduce worth to output. But real value lies in expertise—knowing what to do, when to do it, and why it matters. AI doesn’t make that expertise obsolete; it makes it indispensable.  So, the next time you evaluate the cost of an AI-powered solution, don’t ask how long it took to run the prompt—ask how long it took someone to know exactly what to ask in the first place. That’s where the transformation happens. That’s where the magic is.  Here’s to turning your hard-earned wisdom into prompts that delight, ignite, and redefine what’s possible. References Hawkins, E. (2023, August 17). AI threatens the billable hour revenue model. Axios. https://www.axios.com/2023/08/17/ai-threatens-hourly-revenue-model American Bar Association (2017, May). 8 steps for creating value-based pricing that works. American Bar Association. https://www.americanbar.org/news/abanews/publications/youraba/2017/may-2017/bury-the-billable-hour-and-implement-value-billing-in-your-law-f/   Copyright © 2025 by Arete Coach LLC. All rights reserved.

In 2025, artificial intelligence evolved from a promising experiment into the backbone of modern infrastructure

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