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However, artificial intelligence (AI) is now influencing the subtle norms of how employees think, communicate Artificial Intelligence and Its Role in Shaping Organizational Work Practices and Culture.

The AI revolution is transforming how we work, with a growing number of powerful large language models (LLMs) emerging, including Google's Gemini, Anthropic's Claude, and Perplexity AI's models. This article focuses specifically on two prominent LLM-powered tools designed for business use: OpenAI's ChatGPT and Microsoft Copilot. As a CEO, executive, or business user, understanding the strengths and weaknesses of these two is crucial for making strategic decisions about AI adoption. This article provides a clear comparison to help you navigate this landscape. Which AI tool will provide the biggest strategic advantage? Among the top contenders, ChatGPT (by OpenAI) with 63.3% of reported GenAI users, and Microsoft Copilot with a captive Microsoft Office and Azure user base, are leading the charge in their own ways, each offering distinct advantages. Whether you’re optimizing productivity within Microsoft 365 or seeking a powerful AI partner for deeper automation, making the right choice is critical for staying competitive. This article breaks down the strengths, limitations, and strategic use cases of ChatGPT vs. Microsoft Copilot to help business leaders make an informed decision. This article was originally published on LinkedIn by Severin Sorensen and has been approved for placement on Arete Coach. Scroll to continue reading or   click here  to read the original article. This article draws insights from business user feedback and analysis by ChatGPT (including Operator and Canvas capabilities), Microsoft Copilot (including insights on "Agents" and recent updates), Google's Gemini, and Grok. The article compares and contrasts ChatGPT and CoPilot, exploring these tools from a CEO and key executive business user perspective, focusing on practical applications and strategic decision-making. The author is not providing technical guidance or endorsements of specific platforms; users are encouraged to conduct their own thorough evaluations based on their individual needs. ChatGPT: Customization & Advanced AI Capabilities ChatGPT stands out as a powerful AI assistant with advanced reasoning, automation, and customization capabilities. Designed for flexible enterprise applications, it goes beyond just answering questions—it can generate content, analyze data, integrate with external systems, and even act as an autonomous agent. Why Businesses Choose ChatGPT: Customization & Extensibility: With features like Custom GPTs, OpenAI Operator, and Canvas, businesses can tailor ChatGPT to their exact needs. Advanced AI Agent Capabilities: Evolving towards true AI automation, capable of executing complex workflows. Broad Use Cases: Ideal for content creation, research, code generation, customer support, and workflow automation. Considerations: Integration Effort: May require developer resources for enterprise-level deployment. Learning Curve: More advanced capabilities require training to fully leverage. Scalability Costs: Usage-based pricing means costs can increase with higher volume. Microsoft Copilot: Seamless Integration for Workplace Productivity Microsoft Copilot is built for instant AI-enhanced productivity within Microsoft 365. If your team already relies on Word, Excel, PowerPoint, and Teams, Copilot provides AI assistance directly in your workflow—no extra setup required. Why Businesses Choose Copilot: Seamless Microsoft 365 Integration: Works immediately within Word, Excel, Teams, and Outlook. Automates Repetitive Tasks: Generates presentations, drafts emails, and simplifies spreadsheet analysis. Lower Initial Cost: Often bundled with Microsoft 365, reducing additional expenses. Considerations: Limited Functionality: Currently less powerful than ChatGPT for advanced use cases. Less Customization: Customization is mostly limited to Microsoft’s ecosystem. Basic AI Agent Capabilities” Copilot’s "Agents" are not yet fully autonomous AI assistants. Key Feature Comparison: ChatGPT vs. Microsoft Copilot Final Score: ChatGPT (4.39) vs. Microsoft Copilot (3.42) Which AI Assistant Is Right for Your Business? Choose ChatGPT if… You need advanced AI capabilities beyond Microsoft 365. Custom automation, integrations, and AI agents are a priority. You're prepared to invest in training and implementation. Choose Microsoft Copilot if… You want instant productivity enhancements within Microsoft 365. Your team relies on Word, Excel, Teams, or PowerPoint for daily tasks. You need a low-effort AI adoption strategy with minimal setup. Consider a Hybrid Approach:  Many organizations use both—leveraging Copilot for everyday tasks and ChatGPT for advanced AI solutions beyond Microsoft’s ecosystem. Final Thoughts: AI as a Competitive Advantage The real question isn’t whether to adopt AI, but how quickly you can integrate it to drive efficiency and innovation. AI-powered businesses will outperform their competitors—and choosing the right tools today will shape your future advantage. Copyright © 2025 by Arete Coach LLC. All rights reserved.

“What If DeepSeek, Unmasked, Is Darth Sith?” That was my question in a recent (@SevSorensen) X post, alongside a surreal image of a whale-headed Sith Lord. Humor aside, there’s a serious concern here: DeepSeek is an incredibly powerful AI model with murky security risks. As an ethical AI evangelist, author of the Amazon-Best Selling "The AI Whisperer" Series, and a student of AI security (with a Certified Protection Professional (CPP) accreditation in physical security), I dove deep into DeepSeek's privacy, security, and operational risks. My conclusion? Proceed with extreme caution. While AI’s future is undeniably exciting, IT leaders, CIOs, and CISOs must examine these tools through the lens of cybersecurity and compliance. I encourage you to reach out to your CISSP and other information security professionals for tailored guidance. This article was originally published on LinkedIn by Severin Sorensen and has been approved for placement on Arete Coach. Scroll to continue reading or click here to read the original article. DeepSeek: The Honeyed AI With a Hidden Cost One of my recent posts called DeepSeek a “honeyed AI at a sweet price,” but warned that it could be a honeypot in disguise. Powerful potential, yes, but with deeply hidden costs. DeepSeek, while impressive, is shrouded in secrecy. Crucial details about its architecture and security protocols are scarce, raising red flags for data protection. When AI models run on external servers, privacy isn’t guaranteed. And when the model itself is subject to foreign cybersecurity laws, your data could be at risk in ways you might not anticipate, similar to the criticisms surrounding TikTok. So, what should IT managers be thinking about before deploying DeepSeek? DeepSeek’s Security Risks (and How to Mitigate Them) I dug into online news articles and even consulted AI models like ChatGPT-4o, Gemini, and Grok2, playing them off each other to glean insights. This culminated in a detailed table summarizing DeepSeek’s top security concerns and mitigation strategies. Here's a glimpse: Key questions IT Managers should ask their security teams: How does DeepSeek fit into our current security and compliance framework? Are there alternative AI solutions with stronger privacy and security controls? What additional security layers should we implement? Does DeepSeek’s data jurisdiction pose risks to our business? Can we self-host an open-source alternative to mitigate risks? The Hidden Risks: What CEOs Must Consider Security Vulnerabilities Independent security audits have revealed DeepSeek’s susceptibility to AI jailbreaks, prompt injections, and malware generation. These weaknesses could expose enterprise systems to cyber threats, putting proprietary data and customer trust at risk. Data Sovereignty & Privacy Even if hosted on U.S. or EU servers, DeepSeek remains a product of foreign AI governance laws. With rising geopolitical tensions and data-sharing concerns, enterprises must evaluate whether their sensitive data could be subject to foreign oversight. Compliance & Regulatory Exposure U.S. companies operate under strict data privacy laws (e.g., GDPR, CCPA, HIPAA). Deploying an AI model without clear data governance safeguards could lead to non-compliance, legal exposure, and reputational damage. Intellectual Property Risks DeepSeek’s open-source model fosters collaboration but raises concerns about proprietary data exposure. Enterprises must assess whether their AI-generated outputs could inadvertently contribute to an adversarial AI ecosystem. Final Thoughts: AI Is Here to Stay, AI Models Will Vary, But Security Is Non-Negotiable With public demand strong, many companies have reported an interest in hosting DeepSeek, and yet the security cautions still apply. Based on the information available, several U.S. companies have been identified as hosting or planning to host DeepSeek models on their servers for public or client use: Microsoft: They've integrated DeepSeek models into their offerings, making it available through platforms like Azure. Amazon Web Services (AWS): AWS clients have requested access to DeepSeek models, indicating AWS is hosting or considering hosting these models through services like Bedrock. Dell Technologies: Dell has announced that DeepSeek AI can now run on-premise with their technologies, in partnership with Hugging Face. Perplexity AI: They have added DeepSeek to their platforms, hosting the model in U.S. and EU data centers. Nvidia: Mentioned as part of the broader context of U.S. companies engaging with DeepSeek, though specifics on hosting are less clear. DeepSeek is a powerful, innovative open-source AI model—but with great power comes great responsibility. If you choose to integrate DeepSeek into your workflow, do so with caution, strong security measures, and clear compliance guidelines. And most importantly, lean on your cybersecurity professionals for guidance tailored to your organization’s needs. Strategic Considerations for CEOs For executives considering DeepSeek or similar AI models, I recommend the following: Due Diligence First: Before adoption, conduct rigorous security audits. Engage cybersecurity and legal experts to evaluate the AI model’s safety, compliance risks, and long-term viability. Strategic Sandbox Deployment: If exploring DeepSeek, limit initial usage to isolated, non-critical environments. Ensure that business-critical data remains untouched. Stay Informed & Adaptable: The AI regulatory landscape is evolving rapidly. CEOs must continuously monitor policy changes, security vulnerabilities, and geopolitical developments affecting AI adoption. Balance Innovation with Ethics & Security: AI should not only be powerful and cost-effective—it must be secure, ethical, and aligned with long-term business strategy. Responsible AI adoption requires transparency, governance, and proactive risk management. Engage in Thought Leadership & Collaboration: AI risk isn’t a company-specific challenge—it’s an industry-wide concern. CEOs should engage with industry leaders, policymakers, and security experts to shape best practices for secure and ethical AI deployment. Copyright © 2025 by Arete Coach LLC. All rights reserved.

Across industries, an obstacle to AI adoption is shifting from technology to mindset. When employees view AI as a threat instead of a tool, pilots stall, value is lost, and talent disengages. But when leaders reframe the narrative, employees lean in, and transformation gains momentum. What the Data Tells Us Employee sentiment is conflicted.  In a 2025 Pew Research Center survey, just over half of U.S. workers reported worry about the impact of AI on their careers, with nearly one-third anticipating fewer opportunities personally (Lin, 2025). Exposure is broad, but outcomes diverge.  The IMF estimates that 60% of jobs in advanced economies are exposed to AI. Roughly half of those jobs could benefit from productivity gains through human–AI complementarity, while the other half face potential erosion in wages or demand (Georgieva, 2024). The difference will depend largely on how organizations design roles and workflows. Frequent users still harbor concern.  A 2024 BCG survey found that employees in organizations moving more aggressively into AI-driven transformation report greater anxiety about their job security than those in companies progressing more slowly. Interestingly, the concern is not limited to frontline staff—leaders and managers are even more likely to worry about whether their roles will remain intact over the next decade (Beauchene, 2025). Adoption is uneven. Microsoft’s 2025 Work Trend Index Annual Report , covering 31,000 workers globally, shows experimentation with AI is widespread but value creation is concentrated among “frontier firms” that align culture and processes with technology (Microsoft, 2025). McKinsey’s 2024 survey echoes this: a subset of “high performers” capture outsized gains because they target clear use cases, mitigate risks, and invest in employee capability (Singla, 2024). Productivity gains are measurable.  In a large-scale field experiment with more than 5,000 customer support agents, access to a generative AI assistant increased productivity—measured as issues resolved per hour—by 14% on average. The effect was especially pronounced for novice and lower-skilled workers, who improved by 34%, while experienced agents saw minimal impact (Brynjolfsson, 2023). GitHub Copilot studies show developers completing tasks more than 50% faster (Kalliamvakou, 2024). These results confirm that AI can improve throughput, particularly when workflows are redesigned to integrate human oversight. The Managerial Imperative These findings highlight a central truth: employees aren’t resisting the technology itself, they’re resisting the uncertainty it creates—even though evidence shows AI is more likely to enhance their work than replace it. Leaders must reduce uncertainty by clarifying roles, workflows, and career paths. Persuasion alone is insufficient; the more effective approach is participation, inviting employees to shape how AI enters their work. Overcoming Fear with Reframing Questions Overcoming fear of AI adoption requires individual conversations. Employees need space to voice concerns and reimagine how AI could support, rather than threaten, their work. By asking thoughtful, reframing questions, leaders can address the specific sources of resistance—whether it’s anxiety about job security, doubts about reliability, reluctance to change, or a desire for recognition. These one-on-one discussions help employees see AI as a partner that frees time, strengthens judgment, and creates new opportunities for growth and influence. The following sets of questions provide a framework for guiding those conversations. Addressing Fear of Job Loss For employees worried about job security, frame the conversation differently. Ask questions that position automation as a way to free capacity for higher-value work, helping individuals view AI as a catalyst for professional growth rather than a threat to employment. For example: What parts of your role do you wish you could spend less time on because they’re repetitive or draining? If AI could take over 20% of your most repetitive tasks, how would you reinvest that time? What skills or creative work would you finally have the bandwidth to focus on if AI handled the busywork? Tackling Skepticism About Reliability For employees skeptical of AI’s reliability, frame the technology as a co-pilot rather than a replacement. Use questions that highlight how AI can support human judgment, strengthening trust and building confidence in augmentation instead of substitution. For example: When was the last time you had to double-check or redo a process because of human error? How might AI reduce those risks if it was paired with human oversight? What would it look like if AI acted more like a second set of eyes or a co-pilot rather than a replacement? Where in your workflow would faster access to reliable data improve decision-making? Overcoming Reluctance to Change Workflows For employees who resist change because they feel excluded from the process, involve them directly in co-designing new workflows. Participation transforms resistance into agency and builds genuine buy-in. Example questions include: If we could redesign your current workflow from scratch, without legacy frustrations, what would it look like? Which part of your process feels outdated, clunky, or manual today? How could AI act as an assistant that adapts to your style, rather than forcing you to adapt to it? Building a Sense of Ownership For employees motivated by recognition, ask questions that highlight opportunities for status and influence. Recognition is a powerful driver, and positioning individuals as “AI champions” can accelerate peer-to-peer adoption. Consider: If you were the one designing how AI fits into your team’s work, what would you prioritize first? What would make you proud to say, “We were one of the first teams to figure out how to use AI well”? How could you imagine mentoring others as an “AI champion” once you’ve mastered it? A Practical 90-Day Playbook Asking the right questions is the first step; it surfaces employee concerns, builds trust, and uncovers opportunities for meaningful change. To translate dialogue into action, leaders need a structured approach that turns insights into tangible progress. The following 90-day playbook outlines how to move from individual conversations to organization-wide adoption, ensuring that reframing leads to buy-in and measurable results. Weeks 1–3: Listening and Selection Conduct structured listening sessions using the reframing questions. Segment findings by role seniority and experience, and identify three promising use cases. Weeks 4–6: Workflow Design Work with employees to map current workflows, define new “human-in-the-loop” checkpoints, and clarify guardrails for data use and quality control. Weeks 7–10: Pilot Execution Recruit a small, diverse champion group to test new workflows. Provide training on prompts, scenarios, and failure modes. Collect baseline and pilot performance metrics. Weeks 11–12: Decision and Scale Evaluate pilot results against agreed-upon KPIs (time savings, error rates, satisfaction). Publish outcomes, refine workflows, and extend adoption through structured playbooks and recognition. The Main Takeaway The real challenge for organizations is no longer proving that AI can drive productivity—that is already well established. The challenge is helping employees see AI as a pathway to growth rather than a threat to their role. Achieving this requires participation. When leaders listen, reframe, and co-design with their people, resistance turns into ownership. And when employees take ownership, AI evolves from a source of anxiety into a driver of momentum. References Beauchene, V., Sylvain Duranton, Kalra, N., & Martin, D. (2025, June 26). AI at Work: Momentum Builds, but Gaps Remain. BCG Global. https://www.bcg.com/publications/2025/ai-at-work-momentum-builds-but-gaps-remain Brynjolfsson, E., Li, D., & Raymond, L. R. (2023, April 1). Generative AI at Work. National Bureau of Economic Research. https://www.nber.org/papers/w31161 Georgieva, K. (2024, January 14). AI will transform the global economy. let’s make sure it benefits humanity. International Monetary Fund. https://www.imf.org/en/Blogs/Articles/2024/01/14/ai-will-transform-the-global-economy-lets-make-sure-it-benefits-humanity Kalliamvakou, E. (2024, May 21). Research: quantifying GitHub Copilot’s impact on developer productivity and happiness. The GitHub Blog. https://github.blog/news-insights/research/research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/ Lin, L., & Parker, K. (2025, February 25). U.S. workers are more worried than hopeful about future AI use in the workplace. Pew Research Center. https://www.pewresearch.org/social-trends/2025/02/25/u-s-workers-are-more-worried-than-hopeful-about-future-ai-use-in-the-workplace/ Microsoft (2025, April 23). 2025 Work Trend Index Annual Report Work Trend Index Annual Report 2025: The Year the Frontier Firm Is Born. https://www.microsoft.com/en-us/worklab/work-trend-index/2025-the-year-the-frontier-firm-is-born Singla, A., Sukharevsky, A., Yee, L., & Chui, M. (2024, May 30). The state of AI in early 2024: Gen AI adoption spikes and starts to generate value. McKinsey & Company. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-2024 Copyright © 2025 by Arete Coach LLC. All rights reserved.

Artificial intelligence promises efficiency.

This article was created by Severin Sorensen, drawing insights from the U.S. Copyright Office Report on AI and Copyright, analysis by Google Notebook LM (including summary, Q&A, and podcast summary), and ChatGPT-4o analysis of the material. The article explores the issues from a creator's perspective. The author is not providing legal advice; creators are encouraged to consult with their IP attorney for additional questions. The rise of AI-generated content has sparked one of the most pressing legal debates of our time: Who owns AI-created work? The U.S. Copyright Office has now provided a clear answer—only humans can be authors. This decision carries major implications for businesses, creators, and innovators using AI in their work. Whether you’re a writer, artist, entrepreneur, or corporate leader leveraging AI tools, understanding this ruling is critical. What Did the U.S. Copyright Office Decide? The U.S. Copyright Office has reaffirmed that AI-generated material cannot be copyrighted because copyright law protects only human creativity. The term "author," as defined in both the U.S. Constitution and the Copyright Act, refers exclusively to human beings. However, works that contain both AI-generated and human-created content may be eligible for copyright—but only the human contributions will be protected. Key Takeaways from the Ruling AI-Generated Works Are Not Copyrightable: If an AI creates content autonomously—whether it’s text, images, or music—it cannot be copyrighted. The logic is simple: copyright law protects human expression, and AI lacks the ability to be legally recognized as an "author." Hybrid Works (Human + AI) Can Be Copyrighted: If a human meaningfully contributes to AI-generated content—by arranging, modifying, or creatively integrating it—those human-authored portions may be eligible for copyright protection. A Simple Prompt Is Not Enough: Merely inputting a text prompt into an AI tool like ChatGPT or Midjourney does not make the result copyrightable. The Copyright Office considers this a case of "mechanical reproduction" rather than human authorship. How to Protect Your Work If You Use AI The Copyright Office now requires creators to explicitly disclose the use of AI in their copyright applications. Here’s how to ensure your work is properly protected: Identify What You Created: When applying for copyright, specify which parts were created by a human. Do not list the AI as an author. Use the ‘Limitation of the Claim’ Section: In your copyright application, disclose AI-generated portions under the “Material Excluded” field. Clarify your human contribution under “New Material Added.” Update Existing Copyright Applications: If you've already applied for copyright without disclosing AI use, you must file a supplementary registration to correct the record. Failure to disclose AI use could result in cancellation of your copyright registration or legal challenges if discovered in a lawsuit. What This Means for Businesses & Creators This decision significantly impacts companies, marketers, designers, authors, musicians, and legal teams using AI tools. For Writers & Content Creators: If you use AI for brainstorming or drafting, ensure you edit and refine the content significantly—mere AI-generated text won’t qualify for copyright. For Designers & Artists: If AI-generated images are part of your work, focus on adding human creativity—modifying, arranging, or integrating them into a unique composition. For Businesses & Brands: Companies using AI-generated content in branding, marketing, or product development should be mindful of copyright limitations—especially if they plan to commercialize AI-created work. The Future of AI and Copyright Law The U.S. Copyright Office acknowledges that AI is evolving rapidly and has launched an agency-wide initiative to investigate: The use of copyrighted material in AI training datasets The legal and ethical implications of AI-generated content Public comments on AI’s role in creativity and authorship As legal frameworks continue to develop, businesses and creators must stay informed and proactive in understanding the intersection of AI and copyright. Final Thoughts: The Human Touch Matters The Copyright Office’s decision reinforces an essential truth: AI is a powerful tool, but it is not a creator. Creativity, judgment, and authorship remain uniquely human qualities. For businesses and creators using AI, the path forward is clear—AI should augment, not replace, human creativity. The best way to protect your work is to ensure that your own originality, intent, and unique perspective remain at the heart of what you create. For the full Copyright Office guidance, read here: https://www.copyright.gov/ai/ . Copyright © 2025 by Arete Coach LLC. All rights reserved.

how we manage technology, but how we define collaboration, leadership, and personhood in the age of intelligent Anthropomorphism bias: Human tendency to attribute consciousness to sophisticated responses Temporal artifacts Implications Positive Results (Consciousness Evidence) Scientific implications: First measurable evidence of artificial reproducibility standards, we can move beyond subjective impressions toward scientific assessment of artificial results support or refute consciousness hypotheses, this methodology advances our understanding of intelligence

For nearly two decades, the rules of digital engagement were clear: design mobile-friendly sites, generate authoritative backlinks, and publish keyword-rich content. Search algorithms decided who won, and those rankings drove growth, brand awareness, and trillions in commerce. But a profound, structural shift is underway. Stakeholders—from customers and partners to investors—are no longer just typing queries into a search bar. They are posing complex, conversational questions to AI-powered platforms and receiving synthesized, single-answer responses. Recent research from Gartner projects that by 2026, traditional search engine volume will drop by 25%, with AI-powered search bots and virtual agents eating into the market (Gartner, 2024). In this new environment, leaders must prepare for AI Engine Optimization (AEO): the practice of strategically shaping how generative AI platforms find, interpret, validate, and present your company’s content in their outputs. The core difference is one of intent and outcome: SEO:  The goal is to rank a web page in response to a keyword-based query, driving a user to click a link. AEO:  The goal is to become a trusted, citable source that an AI engine incorporates into its synthesized answer, often without a click. Comparing SEO and AEO Executives must view the transition from SEO to AEO not as an incremental evolution, but as a paradigm shift. The strategic dimensions are starkly different: Traditional SEO Primary Goal:  To drive website traffic by achieving high rankings on a Search Engine Results Page (SERP). User Interaction:  Users enter keywords, scan a list of blue links, and click through to various websites to find their answer. Visibility Signals:  Relies on traditional ranking factors like backlinks, keyword density, and domain authority. Success Metrics: Measured by impressions, clicks, session duration, bounce rates, and keyword rankings. Competitive Risk: Being outranked by a competitor, leading to reduced traffic but not complete invisibility. AI Engine Optimization (AEO) Primary Goal:  To become an authoritative source by directly embedding your data into AI-generated answers. User Interaction: Users ask a conversational question and receive a single, synthesized response compiled from various sources. Visibility Signals:  Depends on machine-readable signals like structured data (schema), E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness), and content clarity. Success Metrics: Measured by frequency of citation in AI responses, sentiment analysis of those citations, and share of voice within key conversational queries. Competitive Risk: Digital invisibility—if your brand is absent from AI outputs, it effectively ceases to exist in that user's discovery journey. The AEO Playbook: 5 Strategies for the AI-First Era Shifting to AEO requires a disciplined, C-suite-led approach. CEOs must ensure their organizations adopt the following practices to build a durable competitive advantage. Structure Your Content for Machines, Not Just Humans AI engines are voracious but literal readers. They thrive on structured, machine-readable data that removes ambiguity. Aggressively Implement Schema Markup:  Go beyond basic schema. Mark up your products, services, executives (with their expertise), articles, and FAQs. This structured language tells AI engines exactly what your content is about, who wrote it, and why it’s credible. Build a Centralized Knowledge Base:  Create a "single source of truth" with clearly tagged, up-to-date information. This becomes the well from which AI engines can draw clean, reliable data about your company. Ensure Consistent Metadata:  Use uniform metadata across all platforms and content types so AI engines can parse context accurately and connect the dots between your different digital assets. Weaponize Your Expertise with Verifiable Authority (E-E-A-T) In an environment flooded with AI-generated content, verifiable human expertise is a premium. AI platforms are being fine-tuned to prioritize it. E-E-A-T—Experience, Expertise, Authoritativeness, and Trustworthiness—is the framework. Attribute Everything:  Clearly attribute authorship to qualified experts with detailed bios, credentials, and links to their professional profiles. Cite Credible, External Sources:  Back up claims with data from academic studies, peer-reviewed journals, and reputable industry reports. This signals to AI that your content is part of a broader, credible conversation. Display Freshness Signals:  Prominently display publication and update dates to show that your information is current and relevant. Shift from Keywords to Conversational Queries Keyword-stuffing is over. Your stakeholders are asking complex, multi-faceted questions. Reframe Content Around Problems:  Instead of optimizing for "AI adoption consulting," frame content to answer: “What are the top five risks a CEO must consider before deploying enterprise-wide AI?” Deliver Concise, Authoritative Answers:  Structure your content to provide direct, clear answers early on—mirroring how AI engines synthesize and present information. Think of your content as a series of "briefing notes" for an AI. Forge Direct Data Partnerships and API Pipelines Forward-looking companies are not waiting for AI engines to find them; they are creating direct pathways for their data. Explore Syndication and APIs:  Investigate partnerships with AI platforms to ensure your data is pulled directly via an API. For example, a financial services firm could build an API that delivers its latest market analysis directly into the AI models used by investors; a homebuilder could feed real-time inventory, pricing, and community data into AI models used by prospective buyers; and a healthcare system could provide appointment availability, specialty services, and accreditation data to AI models guiding patients in their care decisions. Engage with Emerging Platforms:  Don't just focus on the giants. Platforms like Perplexity are building new models for content discovery. Engaging with them early can secure a first-mover advantage. Build a Continuous Learning Loop AEO is not a "set it and forget it" initiative. The algorithms will evolve continuously. Invest in AEO Analytics:  A new category of analytics tools is emerging to track brand citations, sentiment, and visibility within AI responses. This is your new dashboard for digital relevance. Establish a Cross-Functional AEO Team:  Assign a team—led by a senior executive—to constantly monitor the landscape, experiment with new content formats, and refine your AEO strategy. Leading the Transition from SEO to AEO AI Engine Optimization should not be a delegated task for the marketing department; it is a fundamental strategic concern that requires C-suite oversight and orchestration. Make it a Boardroom-Level Priority:  AEO directly impacts brand visibility, corporate reputation, and competitive positioning. It must be integrated into your digital transformation roadmap and discussed at the highest levels. Orchestrate Cross-Functional Collaboration:  The CEO must ensure the CIO, CMO, and Chief Data Officer are aligned. The CIO prepares the technical infrastructure (like APIs), the CMO guides the content and expertise strategy, and the CDO governs the data pipelines that feed the AI ecosystem. Redefine KPIs and Demand Accountability:  Just as executives once tracked keyword rankings, they must now define and monitor AEO metrics: frequency of citation in AI responses, sentiment analysis of those citations, and share of voice within key conversational queries. The rules of digital visibility are being rewritten in real time. For decades, the game was about climbing a list of links. Now, it is about becoming the answer itself. Those who master AEO will be trusted and shape the narratives that drive the next era of business. References Gartner. (2024, February 19). Gartner Predicts Search Engine Volume Will Drop 25% by 2026, Due to AI Chatbots and Other Virtual Agents . Gartner. Retrieved from   https://www.gartner.com/en/newsroom/press-releases/2024-02-19-gartner-predicts-search-engine-volume-will-drop-25-percent-by-2026-due-to-ai-chatbots-and-other-virtual-agents Copyright © 2025 by Arete Coach™ LLC. All rights reserved.

In recent weeks, Agentic AI has taken center stage in our AI-related discussions. This concept has gained even more relevance with OpenAI's introduction of its first Agentic AI, called “Operator.” Operator is a groundbreaking agent capable of performing tasks on the web independently. Using its built-in browser, it can navigate websites, interact with content by typing, clicking, and scrolling, executing tasks as directed. Currently available as a research preview, Operator has some limitations and is designed to evolve through user feedback. It represents an exciting step forward in AI capabilities, enabling systems to perform tasks autonomously with minimal input—marking a significant milestone in the development of Agentic AI. How It Works OpenAI reports Operator’s capabilities as being designed to “handle a wide variety of repetitive browser tasks such as filling out forms, ordering groceries, and even creating memes. ” (OpenAI, 2024). By utilizing the same interfaces and tools that humans interact with daily, such as your internet browser, Operator expands upon Chat GPT’s prior applications of AI.  Unlike other iterations of ChatGPT and OpenAI products, which were solely generative and required users to guide every action, Operator introduces the potential for AI to complete tasks within a framework rather than relying on prompt-by-prompt instructions. What sets Operator apart from traditional Agentic AI solutions—which often require programmers to code within rigid frameworks—is its ability to “‘see’ (through screenshots) and ‘interact’ (using all the actions a mouse and keyboard allow) with a browser, enabling it to take action on the web without requiring custom API integrations.” (OpenAI, 2024).  Its decision-making process is fully transparent, with a detailed step-by-step rationale displayed to the user in real-time. When Operator encounters challenges or makes errors, it utilizes its advanced reasoning capabilities to identify and resolve the issue on its own. If it reaches a point where assistance is required, it smoothly transitions control back to the user, fostering a seamless and collaborative interaction that prioritizes ease of use. Current Use Cases Operator’s current use cases are primarily focused on personal tasks such as grocery shopping, making reservations, staying updated with news, and more. Users can select a website from a list provided by OpenAI and begin engaging with it seamlessly. For example, OpenAI demonstrates how the Agent can use Instacart to perform a series of tasks: (i) find a recipe on AllRecipes.com , (ii) purchase the required ingredients on Instacart, and (iii) exclude ingredients the user has already specified they own. Moreover, for each website integrated with the tool, users can provide personalized instructions to tailor its actions. For instance, with Priceline.com , users can set preferences such as booking only hotels that offer “free breakfast and fully refundable rooms.” With this customization, the Agent ensures all recommendations align with user preferences, making tasks like trip planning significantly easier and more efficient. What It Means Currently available exclusively to Pro users during its initial Research Preview phase, Operator introduces an exciting vision for the future of Agentic AI. By eliminating barriers such as the need for programming expertise and the cost of API integrations, it opens up new possibilities for how individuals and businesses can harness AI to streamline tasks and enhance productivity. As Operator evolves based on user feedback, we anticipate its integration with a broader range of websites will expand. This growth could empower executive coaches, business leaders, and CEOs to leverage Operator as a powerful "back-pocket assistant," helping them operate more efficiently and strategically. By extending its utility beyond personal use, Operator has the potential to transform business workflows by accelerating task completion, simplifying adoption through seamless compatibility with existing systems, and offering early adopters a distinct advantage as industry pioneers. As Operator advances, we aim to harness its potential to benefit both executive coaches and the industry as a whole. By applying Operator to key foundational use cases, we can enhance our ability to support clients more effectively than ever before. Foundational examples of leveraging Operator we hope to see in the future could include: Market Monitoring: Track competitor activities or industry trends by scanning relevant websites and news platforms, as well as identify growth opportunities by analyzing customer feedback or market data. Research Assistance: Gather and summarize information on clients’ industries, competitors, or market trends, and prepare detailed client insights and performance metrics for coaching sessions. Strategic Research: Use Operator to analyze new markets, potential partnerships, or acquisitions, and collect and summarize key reports on industry trends, policy changes, or regulatory updates. Operational Efficiency: Delegate routine decision-making tasks like reordering supplies, renewing subscriptions, or managing admin-related communications. Furthermore, leverage Operator to review performance dashboards and flag anomalies or trends for further analysis. Streamlined Administrative Tasks: Automate routine tasks like scheduling appointments, completing forms, or managing CRM data, allowing coaches to focus more on delivering value to clients rather than spending time on operational logistics. Decision Support: With its reasoning capabilities, Operator could assist leaders in analyzing data, generating insights, and even drafting communications or proposals, enabling faster and more informed decisions, especially for leaders managing dynamic and complex environments. The Main Takeaway Although Operator's current applications focus primarily on personal tasks, it's essential for executive coaches to stay informed about tools like this as they could significantly impact professional contexts in the future. By understanding and anticipating the potential of Agentic AI, coaches can position themselves as early adopters and innovators, ready to leverage these technologies as they evolve toward more professional and business-focused use cases. Operator’s ability to automate repetitive tasks, perform complex decision-making, and integrate seamlessly into existing workflows has clear implications for the coaching industry. For executive coaches, staying abreast of these developments means being prepared to harness similar tools to enhance client engagement, improve operational efficiency, and provide data-driven insights. In essence, remaining informed about advancements like Operator ensures that executive coaches can proactively adapt to technological shifts, unlocking new opportunities to drive growth for themselves, their clients, and the broader coaching industry. DeepSeek-R1: Advancing NLP and Disrupting AI Innovation DeepSeek is an AI research initiative dedicated to redefining natural language processing (NLP) to elevate AI's ability to interpret and generate text with human-like depth and precision. By delving into the intricacies of context, nuance, and subtlety in communication, DeepSeek seeks to transform applications such as conversational AI, automated content generation, and tailored user experiences. Its mission is to push the limits of AI's capacity to emulate and complement human cognition, fostering more intuitive and accessible AI systems. This week, DeepSeek introduced DeepSeek-R1, a groundbreaking reasoning-focused AI model that has made waves across the AI community. Delivering exceptional performance on multiple benchmarks, DeepSeek-R1 was developed at a fraction of the usual cost and resource expenditure. Reports highlight that “DeepSeek’s API costs are over 90% lower than the comparable o1 model from OpenAI” (Franzen, 2025). This achievement not only challenges industry leaders such as OpenAI and Google but also underscores the feasibility of producing advanced AI under tight resource constraints, raising questions about the effectiveness of U.S. semiconductor export restrictions. Renowned for its commitment to advancing NLP, DeepSeek is revolutionizing AI's ability to grasp the complexities of human communication. These innovations open doors to transformative developments in conversational AI, automated content creation, and personalized experiences, advancing AI's broader potential to enhance and replicate human cognitive processes. References Franzen, C. (2025, January 24). Why everyone in AI is freaking out about DeepSeek. VentureBeat. https://venturebeat.com/ai/why-everyone-in-ai-is-freaking-out-about-deepseek/ Copyright © 2025 by Arete Coach LLC. All rights reserved.

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Intelligent Workflow Design AI can identify patterns in workflow logs, highlight bottlenecks, and suggest automation, documentation, and AI-driven process design—each with unique strengths for building a modern, intelligent Confluence + Atlassian Intelligence Strength: Robust SOP documentation with enterprise search and permissions AI-Powered Process Insights & Automation (Beyond Manual Triggers) Leverage intelligent systems that observe handling Use It For: Back-office finance, HR, regulated workflows Strength: AI bots + domain-specific intelligence