And the Award for AI Word of the Year Goes To… Tapestry

Lately, I’ve noticed something odd in branding. Every company is weaving its story into a “tapestry.” Yes, tapestry. It’s as if AI decided this was the magic word to convey elegance, complexity, and sophistication. Suddenly, every mission statement is a “tapestry of values” or a “tapestry of innovation.” If AI had a Word of the Year, tapestry would win by a landslide.

Streamlining or Stifling?

AI tools like ChatGPT and Bard are great at pulling in the perfect language to build cohesive narratives, but when every brand’s story uses the same polished script, we end up with a “tapestry” of sameness. It’s efficient, sure—but at what cost? When everyone’s leaning on the same digital playbook, we lose what makes each brand unique.

The Human Touch: More Than Algorithms

This is where true brand builders come in. AI can sift through data and suggest language, but real branding goes far beyond that. It’s not just about the words—it’s about talking to people at all levels, catching those offhand comments, reading body language in meetings, and understanding the CEO’s vision as well as the intern’s daily experience. That’s what brand builders like me do. We dive deep into the culture of a company, find its authentic character, and shape a narrative that reflects the people, not just the metrics.

Branding isn’t just plugging in keywords or relying on bots to tell you what a company stands for. It’s about connecting with the people behind the brand, capturing their energy, and understanding what drives them. That’s something no AI can fully replicate.

So yes, AI can help organize the threads, but it’s up to us to find the story that breaks the mold. Because in the end, nobody remembers a brand for the perfect “tapestry”—they remember the authentic story that resonates

"The Résumé Revolution: Standing Out When Everyone Sounds the Same"

As a consultant, I'm always on the lookout for the next project. And let me tell you, hunting for a job in the age of AI is like dating in the age of apps: everything looks polished, profiles are curated, and everyone’s swipe-right game is strong. But here's the twist—when everyone’s profile looks like an Instagram influencer’s feed, you start to wonder what happened to authenticity.

Lately, I've noticed an eerie similarity between my résumé and those of others in my network. Case in point: a colleague recently shared her professional bio on LinkedIn, and as I read it, I had a-ha moment. It was as if she had copy-pasted my résumé! (Of course, she didn’t, but the uncanny resemblance was there.) And it's not because we lack originality; it's because AI résumé tools are crafting everyone's stories from the same algorithmic recipe book.

AI Craftsmanship: Everyone’s a Perfect Fit

Thanks to tools like ChatGPT or Gemini, today’s résumés look like works of art. They’re clean, concise, and targeted with laser precision. AI takes the job description, extracts the keywords, and tailors our résumés to fit like a well-fitted suit. And that’s the point—sort of. We’re all trying to present our best selves and show recruiters that we can hit every requirement on their checklists. It’s like checking off the ingredients on a baking show: “You want leadership skills? Boom! You want a dash of team collaboration? Done. A sprinkle of strategic thinking? Let me toss that in.”

But there’s a catch. Everyone’s doing it. We’re all baking the same cake, and it’s getting hard to stand out. This reminds me of something I heard from a hiring manager friend: “It’s like scrolling through 200 identical LinkedIn profiles; I can’t tell who wrote these, who copied them, or who just hit ‘Generate.’”

The AI Interview: Playing the Game

And that’s just step one. After you’ve gotten your algorithm-approved résumé through the first screening, you get to the fun part: the AI-mediated interview. Picture this—You’re sitting in front of your laptop, staring at a countdown clock while an automated voice cheerfully instructs me to “describe a time you showed initiative under pressure.” No pressure, right?

Welcome to the age of gamified hiring. With AI assessing everything from my tone of voice to the tiny movements of my eyebrows, it feels more like trying to beat a video game boss than connecting with a future colleague. In one interview, the system rated me on a scale for cultural fit, asking me to rank statements like, “I find working with others inspiring” from “strongly disagree” to “strongly agree.” I remember staring at the screen and thinking, “This feels more like taking an online personality quiz to find out if I’m a ‘Creative Innovator’ or ‘Diligent Doer.’”

The whole process is so thoroughly optimized that it’s become a strategy game. And like in any game, those who know the rules best are the ones who succeed. This new hiring landscape rewards those who can master AI tools or afford to pay experts to craft their applications. If you’re tech-savvy, that’s great. But if you’re not, or if your strengths lie outside these parameters, good luck making it past the algorithmic gatekeepers.

The LinkedIn Algorithm and Its Consequences

This is where LinkedIn comes into play. It’s no longer just a job board—it’s a 24/7 networking event, professional knowledge hub, and, let’s be honest, a annoying birthday reminder app, I thought that was Facebooks’s job:) . Thanks to LinkedIn’s matching algorithm, roles are pushed based on keywords and past activity. The platform knows more about our professional preferences than ourselves. But despite the sophisticated matching, something gets lost in translation..

Is Authenticity the Price of Efficiency?

Now, don’t get me wrong, AI in hiring isn’t inherently bad. In fact, it’s the only thing standing between recruiters and death by résumé avalanche. But there’s a cost to efficiency. The more we optimize for key phrases and algorithmic approval, the more we risk losing the things that make us interesting, unique, and frankly, human.

So where does that leave us? At the end of the day, the most consistent way to get hired hasn’t changed in decades: it’s through connections and referrals. Despite all the advancements in AI, a personal recommendation from a colleague still outweighs the most pristine algorithmically-optimized résumé. It’s the professional equivalent of “knowing the bouncer at the club,” and let’s be honest, that still gets you in quicker than waiting in line.

What’s Next?

As AI takes over more of the hiring process, we need to be cautious about over-automation. Companies, in their eagerness to cut down on human error and unconscious bias, could be inadvertently introducing new biases through the algorithms they employ. Remember, an AI isn’t a neutral entity—it’s trained on data that reflects human decisions and behaviors, for better or worse.

So, what can job seekers do? Be authentic, be strategic, and—this is important—keep networking. AI might be evaluating your résumé, but humans are still doing the final interview. And humans appreciate a little bit of personality, a real story, and a good laugh. Just last week, a recruiter told me that what stood out most in an interview wasn’t a candidate’s bullet-pointed résumé but their genuine response to a simple question: “What’s the hardest thing you’ve ever failed at?”

In the age of AI, maybe that’s the trick: understanding the rules of the game, but not being afraid to break them a little. Because no matter how sophisticated AI becomes, there’s still one thing it can’t replicate: the complexity, creativity, and imperfect beauty of being human.

So keep customizing those résumés, practice for those AI-mediated interviews, but we can’t lose sight of what makes us, well, us. Because at the end of the day, no one ever got hired for being perfectly predictable.

AI Stands for Artificial Intelligence, But What’s a GPT or an LLM??

Let’s be honest—how many AI buzzwords and acronyms do you really get beyond AI meaning Artificial Intelligence? Maybe you’ve heard of LLMs (Large Language Models) or know that a Neural Network has something to do with how machines "learn," but for most people, these terms fly right over our heads. Just like when www became part of our vocabulary or when the language of the .com internet exploded into the world, we had to learn a whole new language: megabytes, pixels, bandwidth. Sound familiar?

Fast-forward to today’s AI era. Once again, it’s time to pick up the lingo. But it’s not just about knowing the words—understanding what they mean is key.

Here are the top 15 AI buzzwords and acronyms, ranked by how familiar they may be, and why they matter to you—even if you’re not an AI expert. Pay close attention to number 11 as that is critical for building trust.

1. AI (Artificial Intelligence)

• Definition: Machines performing tasks that usually require human intelligence, like recognizing faces or driving cars.

• Companies: Google, Amazon, Tesla

• Analogy: Think of AI as a robot brain that can make decisions, recognize faces, or recommend songs, much like a human.

• Why It’s Important: AI is everywhere—from voice assistants (like Siri) to personalized shopping recommendations. It's the backbone of modern technology.

2. NLP (Natural Language Processing)

• Definition: AI systems designed to understand, interpret, and respond to human language.

• Companies: Google (Assistant), Amazon (Alexa)

• Analogy: It’s like teaching a machine to read, listen, and speak like a human, so it can understand you and respond to your requests.

• Why It’s Important: NLP powers the voice assistants on your phone, smart speakers, and customer service bots—basically, it helps machines understand what you’re saying.

3. LLM (Large Language Model)

• Definition: A type of AI model that processes and generates human-like text by understanding patterns in large amounts of data.

• Companies: OpenAI (GPT), Google (Bard)

• Analogy: Like a very advanced chatbot that can hold conversations, write essays, or answer questions with detailed responses.

• Why It’s Important: LLMs power virtual assistants, customer service bots, and content creation tools, making communication with machines more natural.

4. GPT (Generative Pre-trained Transformer)

• Definition: A pre-trained language model that generates human-like text responses.

• Companies: OpenAI (ChatGPT)

• Analogy: It’s like a really knowledgeable friend who’s read everything and can answer almost any question you ask.

• Why It’s Important: GPT models power many of today’s most popular AI tools, from chatbots to content creation systems, making them some of the most influential technologies in AI today.

5. RAG (Retrieval-Augmented Generation)

• Definition: Combines retrieving information from databases with generating text to improve responses.

• Analogy: Like a student answering a question using both memory and research.

• Companies: Google Bard, OpenAI

• Why It’s Important: It makes AI smarter by grounding its responses in real-world knowledge, which is key for search engines and chatbots.

6. GAN (Generative Adversarial Network)

• Definition: Two competing AI networks (a generator and a discriminator) create and evaluate realistic data, like images or videos.

• Analogy: Two artists, one paints, the other critiques, until the art looks real.

• Companies: NVIDIA, OpenAI (DALL·E)

• Why It’s Important: GANs are used for creating deepfakes, AI art, and enhancing visual creativity in industries like design and entertainment.

7. NLG (Natural Language Generation)

• Definition: The process of AI generating human-like text from input data.

• Analogy: It’s like giving AI a couple of ideas, and it spins them into a whole story.

• Companies: OpenAI, Narrative Science

• Why It’s Important: NLG powers applications that automatically write reports, generate text responses, or create articles, improving how businesses communicate with customers.

8. Transformer

• Definition: A neural network designed for processing sequences of data, used heavily in language tasks.

• Analogy: Imagine a super-powered translator that can read and understand a sentence both forward and backward to get the full meaning.

• Companies: OpenAI (GPT), Google (BERT)

• Why It’s Important: Transformers are the backbone of most major advancements in NLP, allowing AI to generate and understand text in a much more sophisticated way.

9. Edge AI

• Definition: Running AI directly on devices (like your phone or smart home gadgets) without sending data to the cloud.

• Analogy: Instead of asking a friend for help with a math problem, you just solve it yourself, on the spot.

• Companies: Qualcomm, NVIDIA

• Why It’s Important: It enables faster response times, lower latency, and better privacy, especially in smart home devices and real-time applications.

10. Federated Learning

• Definition: A method where AI learns from data on multiple devices without needing to centralize it.

• Analogy: Imagine teaching students in their own homes without bringing them to a central classroom—everyone learns, but privacy is maintained.

• Companies: Apple (Siri), Google

• Why It’s Important: This allows AI to get smarter without compromising your privacy—crucial for health apps or mobile services.

11. XAI (Explainable AI)

• Definition: AI systems that explain their decisions, making them more transparent and understandable.

• Analogy: It’s like having a teacher who doesn’t just give you the right answer but explains how they got there.

• Companies: IBM Watson, DARPA

• Why It’s Important: With AI influencing decisions in healthcare, finance, and hiring, knowing why an AI made a choice helps build trust and accountability.

12. Reinforcement Learning (RL)

• Definition: AI learns by trial and error, receiving rewards for good actions and penalties for bad ones.

• Analogy: Training a dog with treats—when the AI does something right, it gets a reward, encouraging it to repeat the behavior.

• Companies: DeepMind (AlphaGo), Tesla (Autonomous Driving)

• Why It’s Important: RL is critical for AI in dynamic environments like gaming (e.g., AlphaGo) and autonomous systems (like self-driving cars), where constant learning and improvement are required.

13. Few-Shot Learning (FSL)

• Definition: A method where AI learns tasks from just a few examples.

• Analogy: Like learning a new card game after watching just one or two rounds.

• Companies: OpenAI (GPT), Google Research

• Why It’s Important: It allows AI to adapt quickly even when there’s not much data to learn from, which is critical in areas like healthcare or rare languages, where data is often scarce.

14. Multimodal Learning

• Definition: AI that can process and understand multiple types of data (like text, images, and audio) simultaneously.

• Analogy: Like watching a movie while reading the subtitles and listening to the soundtrack—AI takes in all these different types of input at once to understand the full picture.

• Companies: Microsoft Azure AI, Google

• Why It’s Important: This enables AI to handle complex tasks like interpreting video content or assisting in medical diagnostics by analyzing different types of data together.

15. AGI (Artificial General Intelligence)

• Definition: A type of AI that aims to perform any intellectual task a human can do, not just specific tasks.

• Analogy: Like watching a movie while reading the subtitles and listening to the soundtrack—AI takes in all these different types of input at once to understand the full picture.

• Companies: OpenAI, DeepMind

• Why It’s Important: AGI represents the ultimate goal for AI research, promising machines that can perform a wide range of tasks autonomously, which could revolutionize industries and society as a whole.

So, there you have it—the top 15 AI buzzwords and acronyms that everyone should know.

What ones did we miss?

What Does a Fully AI-Integrated Business Look Like?

Imagine a world where AI doesn’t just assist—it’s the backbone of every operation. In this future company, AI anticipates customer needs and designs products faster than any human could. The supply chain? Flawlessly optimized in real-time, with AI predicting demand spikes and adjusting logistics instantly. Marketing campaigns are hyper-personalized, delivered to the right audience at the perfect moment, and customer service is handled by AI that knows your problem before you do. Employees no longer drown in repetitive tasks; they’re empowered by AI to focus on strategy, creativity, and innovation. Brand management becomes a living, breathing entity, with AI constantly monitoring and shaping the company's reputation. In this AI-powered utopia, businesses run like clockwork—decisions aren’t made on intuition or gut feelings but driven by data, precision, and efficiency at every level. It’s a future where human potential and AI work in harmony to create a seamless, adaptive, and incredibly efficient organization.

Welcome to the AI-powered business utopia. But what must we do to get there?

The Challenge of Comprehensive AI Adoption

Despite AI's potential to transform industries, many companies still struggle to implement it successfully across their entire business. Gartner reports that only 15% of enterprises have integrated AI into multiple processes, with many companies still stuck in the pilot phase. Challenges range from outdated infrastructure to a fundamental lack of AI expertise. But the real barrier often boils down to one thing: trust.

"AI is a tool, not a replacement for human ingenuity," says Arvind Krishna, CEO of IBM. "To build trust in AI, businesses need to prioritize transparency, ethics, and education."

Trust is key—without it, both employees and customers remain skeptical. Data bias, security risks, and the notorious “black box” effect (where even developers can’t explain why an AI model made a particular decision) are serious concerns. As companies grapple with these issues, they risk falling behind.

AI Trust: The Biggest Hurdle

For a company to fully integrate AI, it needs to tackle trust at its core. According to Accenture’s AI Trust Survey, 60% of employees are hesitant to trust AI-driven decisions, fearing biases or lack of transparency.

Here are five ways companies can build AI systems employees and customers will trust:

1. Explainable AI (XAI): Simply put, make AI explainable. Businesses like Salesforce are already leading the charge with their “Einstein GPT,” an AI tool capable of explaining the reasons behind its recommendations. "If the AI suggests something, I want to know why," says John Ball, GM of Salesforce AI.

2. Human-AI Collaboration: Rather than pitching AI as a replacement, businesses must emphasize it as a partner. In McDonald's pilot restaurants, AI handles the repetitive tasks of ordering and inventory, while employees focus on customer interaction and creativity.

3. Ethical AI Use: More companies are adopting ethical frameworks. Microsoft’s AI ethics committee is tasked with overseeing all AI development to ensure fairness and accountability in decision-making.

4. Continuous Learning: Employees need to be AI-literate. Businesses like Amazon now offer free courses on AI for non-technical employees, ensuring everyone understands how the technology works and its limitations.

5. Customer-Centric AI: Involving customers in the AI process can build confidence. Google invites users to experiment with AI tools in beta phases, gathering feedback before full-scale launches.

AI and Humans: Roles at Every Level

In the AI-powered company of the future, humans and machines don’t compete—they collaborate. Here’s how this balance could play out at every level of the organization:

1. Product Design:

   • AI Role: AI tools would use predictive analytics to analyze customer feedback and market trends, generating multiple prototypes based on real-time data. Generative AI could create product variations based on specific user preferences, iterating design cycles at speeds impossible for human designers alone.

   • Human Role: Designers would interpret AI-generated prototypes, infusing them with creativity, emotional intelligence, and aesthetic sensibility. Humans would oversee final decisions, ensuring products not only meet functional needs but also align with the company's brand and values.

   • People Needed: Product managers with AI expertise, UX/UI designers familiar with AI-assisted tools, and human-centered design specialists.

2. Manufacturing:

   • AI Role: AI-powered robots and machine learning algorithms would optimize production lines, making real-time adjustments to reduce waste, increase output, and predict equipment failures before they happen. AI would also manage inventory, ensuring raw materials are always available exactly when needed.

   • Human Role: Human workers would manage more strategic tasks, such as refining operational processes, solving unforeseen issues that AI cannot handle, and maintaining the systems. Their role would shift from manual labor to machine supervision, decision-making, and innovation.

   • People Needed: Automation engineers, AI maintenance specialists, and industrial engineers with AI and robotics experience.

3. Supply Chain:

   • AI Role: AI would monitor and optimize the entire supply chain, using real-time data to adjust sourcing, manufacturing, and shipping processes dynamically. It would forecast demand fluctuations, adapt to geopolitical disruptions, and optimize routes for logistics.

   • Human Role: Supply chain managers would use AI insights to make high-level decisions, ensuring ethical sourcing and adjusting long-term strategies. Humans would still be responsible for negotiating contracts, managing relationships, and setting broader business goals.

   • People Needed: Supply chain analysts, AI-integrated logistics managers, and data scientists specializing in demand forecasting.

4. Marketing:

   • AI Role: AI would drive personalized marketing, using customer data to segment audiences, predict behaviors, and tailor campaigns to individual preferences. Tools like Adobe Sensei are already using AI to optimize content in real time.

   • Human Role: Human marketers would develop creative concepts and brand strategies, overseeing AI's work to ensure it resonates emotionally with the audience. They would also handle public relations, high-level storytelling, and engagement with influencers and partners.

   • People Needed: AI marketing strategists, content creators proficient in data analytics, and brand managers with a focus on digital platforms.

5. Customer Support:

   • AI Role: AI chatbots would handle routine customer inquiries, resolve issues in real time, and even predict customer needs. Tools like IBM’s Watson are already performing this role, resolving up to 80% of customer service queries without human intervention.

   • Human Role: Customer support agents would step in for complex or high-value interactions, where empathy, negotiation, and deep product knowledge are required. They would use AI-generated insights to deliver more personalized, meaningful experiences.

   • People Needed: AI-integrated customer service managers, customer experience specialists, and support agents with expertise in human-AI collaboration.

The Role of AI in Brand Management

One of the most exciting frontiers for AI is in brand management, an area traditionally dominated by human creativity and perception. But AI is increasingly finding its place here, offering unique capabilities to bolster brand identity and protect reputation.

• AI Role: AI can analyze social media and online sentiment in real time, identifying trends, detecting potential PR crises, and even suggesting strategic responses. Tools like Brandwatch and Sprinklr are already using AI to track brand mentions and analyze consumer sentiment across millions of online sources. AI also optimizes branding campaigns by analyzing which elements (color schemes, wording, etc.) drive the most engagement, helping refine messaging at scale.

• Human Role: Brand managers would still oversee high-level messaging, crafting the brand’s voice, values, and personality. They would use AI insights to inform strategy, but the human touch would remain essential in forming emotional connections with consumers, especially in crisis situations where authenticity and empathy matter most.

• People Needed: Social media analysts using AI tools, PR professionals with AI-enhanced media monitoring capabilities, and creative brand managers.

Cost Savings in an AI-Driven Enterprise

The promise of AI integration extends beyond efficiency—it’s about dramatic cost savings, too. McKinsey estimates that companies could save up to $3.5 trillion annually by 2030 through AI-driven cost efficiencies, especially in labor-intensive sectors like manufacturing, logistics, and customer service.

Here’s how the savings could stack up across different areas:

1. Labor Costs: AI can reduce human labor hours required for repetitive tasks. Accenture found that automation and AI integration can cut operational costs by up to 30%, as AI-driven systems require less human intervention for tasks like manufacturing, customer support, and supply chain management.

2. Operational Efficiency: AI-driven optimization of supply chains and production lines could reduce waste and downtime. For example, predictive maintenance driven by AI can reduce maintenance costs by up to 20% and cut unexpected downtime by up to 50%, according to PwC.

3. Marketing and Advertising Spend: AI-powered personalized marketing can significantly reduce advertising costs. Gartner found that companies that use AI for marketing can reduce digital advertising costs by 15-20% through better-targeted campaigns, leading to higher ROI.

4. Inventory Management: AI’s real-time monitoring can prevent overproduction or stock shortages, optimizing warehouse and production processes, and reducing excess inventory costs. Retailers using AI-driven demand forecasting have seen reductions in excess inventory by up to 30%, according to Deloitte.

The Future is Here

While this vision might sound futuristic, elements of it are already in play. The key to success lies in combining AI's efficiency with human oversight. By 2030, McKinsey predicts that AI could deliver $13 trillion in economic value across industries, transforming not just how companies work but also the experiences they provide. From reducing labor costs and operational inefficiencies to driving personalized marketing and optimizing supply chains, AI’s impact on cost savings and growth potential is immense.

However, to fully realize this AI-powered utopia, businesses must invest in the right talent—automation engineers, AI strategists, brand managers with AI expertise, and supply chain analysts capable of leveraging machine learning insights. The human touch remains essential in interpreting data, ensuring ethical use, and maintaining brand authenticity in an increasingly automated world.

As companies continue to grapple with trust, ethics, and execution, the winners will be those that strike the right balance between human creativity and machine efficiency. The AI-powered company of the future won't just be faster or more efficient—it will be smarter, more ethical, and infinitely adaptable. Welcome to the AI utopia, where success is measured not just in profits but in the seamless integration of human and machine intelligence.

Immediate Action: A Podcast on this article

If you’re interested in diving deeper into this topic, here’s something fascinating: Google’s new Notebook.LM platform can turn any subject into a back-and-forth, two-person podcast. I tried it, and the result was incredibly realistic and a startlingly lifelike example of where this technology is heading.

Have a listen!

AI's Transformation of Comms and Consulting Agencies: Tools, Billing, and Strategy Evolution

The communication agency and consulting firm landscape is undergoing a profound transformation, driven by the rapid evolution of artificial intelligence (AI). It's not just changing how agencies work; it's reshaping the very foundation of client engagement, service delivery, and business models within these sectors. As someone deeply involved in leading the integration of AI across content, branding, and digital interaction, I've seen firsthand how agencies and consulting firms can unlock the power of AI to drive innovation. This article dives into the key impacts of AI, exploring its influence on tools, billing models, and future strategies for agencies and the brands they serve.

AI in Communication Firms: Leading Innovation

Communication firms are among the most proactive adopters of AI, leveraging its capabilities to streamline client interactions, deliver hyper-personalized experiences, uncover the top influencers and optimize campaign performance. In many ways, this mirrors the shift to digital and social media in previous decades, but the potential of AI to improve efficiency, insights, and outcomes is unparalleled.

 Case Study: GroupM’s AI-Powered Media Planning

GroupM has integrated AI into its media planning process, demonstrating how AI tools can outperform traditional human teams. In a "human vs. machine" test, AI successfully optimized reach across multiple audience segments in under two minutes, a task human planners struggled to complete efficiently. AI-driven media planning now allows GroupM to maximize client budgets while delivering better-targeted campaigns​. Reed Smith LLP BCG Global

 "AI allows us to deliver highly effective media training at a fraction of the time and cost," says Greg Matusky, Founder and President of Gregory FCA. "This not only benefits our clients but also allows our team to focus on higher-value strategic initiatives."

 Case Study: Publicis’ Marcel AI Platform

Publicis introduced Marcel, an AI-powered platform, to boost collaboration and streamline operations within its global network. The platform connects employees with relevant data, experts, and real-time insights, allowing faster and more strategic decision-making. This shows how AI can optimize agency operations beyond simple automation​.Reed Smith LLP

 Case Study: WPP’s Satalia Acquisition

To stay competitive, WPP acquired AI technology firm Satalia to integrate advanced AI capabilities across its operations. From automating complex workflows to enhancing creative outputs, WPP is investing heavily in AI to transform its business model, aiming to shift from traditional media strategies to AI-driven ones​. BCG Global

 These examples show how major agencies are leading the charge by integrating AI to optimize not just efficiency but also creativity and strategic execution.

AI's Impact on Billing Models: From Hours to Outcomes

With AI’s efficiency, agencies are rethinking traditional billing models. Clients, more aware of AI’s role in delivering services, are pushing for more transparent pricing that reflects value and outcomes rather than hours worked.

 Case Study: Dentsu’s M1 Platform

Dentsu's M1 AI platform targets audience segments in real time, allowing for highly efficient media buys. This transition has prompted a shift from hourly billing to performance-based pricing models, where clients are charged for the results AI enables, such as improved engagement and conversions​. Reed Smith LLP

 Case Study: Accenture’s Move to Value-Based Pricing

Accenture, too, has transitioned to value-based pricing as AI is embedded in projects like AI-powered data warehouses. Instead of charging for time spent, Accenture focuses on the value delivered through AI-powered insights, predictive analytics, and personalized services​.  BCG Global

 As AI becomes more integrated into everyday operations, agencies need to rethink how they structure fees. Value-based models are becoming more prevalent, aligning pricing with the tangible results clients expect from AI-driven insights.

AI-Powered Platforms Revolutionizing Agency Operations

AI is no longer just about efficiency—it’s about innovation. A range of AI-powered platforms is emerging to help agencies streamline processes, deliver actionable insights, and collaborate more effectively with clients.

 Case Study: Aily Labs and Democratizing AI

Aily Labs has made it possible for smaller agencies and businesses to harness the power of AI through its user-friendly decision intelligence app. The platform empowers agencies to use AI across various functions, including data-driven decision-making and client collaboration. Aily Labs is democratizing AI, enabling businesses of all sizes to benefit from its advanced capabilities without needing massive resources​. HubSpot Blog

 Case Study: Crayon’s Competitive Intelligence

Crayon provides AI-powered competitive intelligence, helping agencies stay ahead of market trends by tracking competitor activity. This allows agencies to deliver better-informed strategies to their clients and adjust campaigns in real time​. ContentBot.ai

 Case Study: BCG’s Fabriq Platform

Boston Consulting Group's Fabriq is an AI-powered personalization tool that helps agencies deliver tailored customer experiences. By analyzing complex datasets, Fabriq provides insights into customer preferences and optimizes product recommendations and pricing strategies​. BCG Global

Test-and-Learn Projects: Exploring AI’s Capabilities

Agencies are adopting AI cautiously but strategically through test-and-learn projects. These smaller-scale experiments allow them to understand AI’s potential while managing risks and costs.

Case Study: MikeWorldWide’s AI Tools

MikeWorldWide (MWW) has incorporated AI-driven tools like Perspectives and ProfileLift, allowing the agency to forecast content performance more accurately and adjust media strategies in real time. These tools are proving essential in helping MWW’s teams make data-driven decisions that lead to better client outcomes​. HubSpot Blog

 Case Study: Edelman’s Archie AI

Edelman, the world’s largest PR firm, uses Archie, an AI tool that integrates real-time insights from its Trust Barometer data. This allows teams to adjust communication strategies based on real-time feedback, improving trust levels in client campaigns. This test-and-learn approach is transforming how Edelman balances data with creativity. HubSpot Blog

Getting Started: How Small and Mid-Sized Agencies Can Begin with AI

If you're a small or mid-sized agency ready to dip your toes into AI, it might feel daunting, especially without the massive budgets that larger firms enjoy. But starting small is the key, and AI tools are increasingly accessible for agencies of all sizes. Here’s how to get started:

1. Identify a Pain Point: Start by identifying a process that could benefit from automation, such as data analysis, content creation, or media buying. Focus on areas where AI can make an immediate impact, such as time savings or improved insights.

2. Start with Low-Cost Tools: Platforms like Aily Labs or Crayon are affordable solutions for agencies looking to leverage AI without a massive upfront investment. These tools democratize AI, making it accessible even for smaller teams​. HubSpot Blog ContentBot.ai

3. Leverage AI for Personalization: Use tools like BCG’s Fabriq or platforms like Marcel for personalization and data analysis. These platforms help small agencies deliver tailored campaigns at scale by automating routine tasks and focusing on client-specific insights​. BCG Global

4. Test and Learn: Embrace AI through small test-and-learn projects. Start with something manageable, such as an AI-driven content recommendation tool, or experiment with AI for media targeting. Monitor the outcomes closely and scale up once you’re comfortable.

5. Invest in Talent and Training: AI tools are only as powerful as the people who use them. Make sure your team is trained to understand AI’s capabilities and can apply its insights effectively. Continuous learning is essential to staying competitive in the AI-driven landscape.

Conclusion: Navigating the AI Revolution

The AI revolution is reshaping the communication and consulting sectors in ways that were unimaginable just a few years ago. Whether you’re a large agency or a smaller firm, AI can offer tremendous value, enabling faster, more efficient, and more personalized services for clients. Start small, focus on tools that align with your needs, and embrace test-and-learn projects to build AI expertise gradually.

By strategically integrating AI into operations, agencies of all sizes can unlock its full potential, driving better results for clients while staying competitive in an increasingly AI-driven world.

Immediate Action: A Podcast Specifically Made From This Article

If you’re interested in diving deeper into this topic, here’s something fascinating: Google’s new Notebook.LM platform can turn any subject into a back-and-forth, two-person podcast. I tried it, and the result was incredibly realistic and a startlingly lifelike example of where this technology is heading.

Have a listen!

The Power of Ai Audio and Customized Ai Podcasts

Podcasts and audio content are more popular than ever. As of 2024, more than 100 million Americans listen to podcasts monthly, and the demand for audio-driven experiences is only growing. People are craving hands-free, on-the-go content, whether they’re commuting, exercising, or simply multitasking. With audio content consumption rising sharply, it's clear that the future of media will be heavily driven by voice. Enter Notebook.LM and ChatGPT's advanced voice features, two AI tools that are transforming how we think about audio production.

I’ve had the chance to test Notebook.LM recently, and it’s incredible how quickly and seamlessly it turns your written content into a fully produced, conversational podcast. Whether it's a personal story, a historical piece, or even a blog post, Notebook.LM delivers high-quality, voice-generated episodes that feel real and engaging. But that’s just one piece of the puzzle. When you combine it with ChatGPT's advanced voice capabilities, the potential for creating dynamic, real-time audio experiences becomes even more exciting.

The Power of Audio: Why It’s the Medium of the Moment

The popularity of podcasts shows no signs of slowing down. Over the past few years, podcasts have become a major part of people’s daily routines. Edison Research reports that 62% of Americans over the age of 12 have listened to a podcast, and these numbers are steadily increasing. The intimacy of audio, combined with its convenience, is what keeps people coming back.

For content creators, marketers, and businesses, audio offers an unparalleled opportunity to connect with audiences in a way that feels personal and immersive. The challenge has always been scaling this kind of production, but tools like Notebook.LM are rapidly changing the game. You can now turn any written content into engaging audio in minutes. And that’s where ChatGPT’s advanced voice features come in, allowing for more interactive and spontaneous audio conversations.

Fast, Flexible, and Human-like: AI in Your Pocket

Let’s talk about why Notebook.LM and ChatGPT are so powerful for audio content creation. Notebook.LM allows you to input content and, within minutes, creates a polished, podcast-ready conversation between AI voices. These aren’t the robotic voices you might expect—these AI voices sound remarkably human. They pause naturally, use conversational quirks like "um" and "like," and deliver the content in a way that sounds like two people genuinely discussing your topic.

But the real magic comes when you integrate this with ChatGPT’s advanced voice features. Now, instead of just generating a pre-scripted podcast, you can create real-time, interactive conversations. Imagine hosting a podcast where listeners can ask questions, and the AI responds fluidly, providing a more personalized and immersive experience. It's the next evolution in audio content—fast, adaptable, and scalable.

The Future I Can’t Wait For: Siri with AI-Powered Podcasting

One of the most exciting things about this leap in AI technology is how close we are to integrating it into our everyday devices. I can’t wait for the day when Siri and other virtual assistants adopt these advanced AI features directly into our phones. Picture this: you’re driving to work, and you want to generate a quick podcast episode based on your latest blog post. With a simple command to Siri, you could use Notebook.LM’s features to create the episode on the fly, all while keeping your hands on the wheel.

And why stop there? Imagine using ChatGPT’s voice to have real-time conversations with your virtual assistant, not just about facts or directions, but full interactive dialogues on topics you care about. Whether it’s generating content or having engaging conversations, the future of AI-driven audio is already on its way to your pocket.

Not Without Challenges, but the Potential is Huge

Of course, no AI tool is perfect. Notebook.LM and ChatGPT occasionally misstep with awkward phrasing or inflection, and sometimes the AI voices don’t quite hit the right emotional tone. These moments remind us that, while AI is advanced, it’s still not fully human. But what these tools are doing today is just the tip of the iceberg. The AI-driven future of audio is coming fast, and it's clear that these tools are already miles ahead of where we were just a few years ago.

And as the tech improves, those small imperfections will fade away. Right now, these tools are more than capable of adding immense value to your content strategy. Whether you’re a podcaster looking to produce more content or a marketer trying to diversify your brand’s voice, Notebook.LM and ChatGPT make it easy, fast, and scalable.

The Power of AI Audio, Ready for You

The beauty of Notebook.LM and ChatGPT is that they’re both highly accessible. You don’t need to be a podcasting expert or a tech whiz to use these tools. The flexibility of AI-generated audio content means you can adapt it for nearly any format, from interactive podcasts to brand messaging. The possibilities are endless.

As audio continues to dominate how we consume content, creators will need to keep up with the demand for engaging, high-quality audio experiences. Tools like Notebook.LM and ChatGPT’s advanced voice features are already paving the way, allowing you to scale your audio production quickly and effectively.

We’re on the brink of a new era in content creation, and AI is at the forefront of this evolution. So, whether you’re a seasoned content creator or someone just starting to explore the potential of AI, now is the time to jump in. The future of audio—and the future of content—starts now.

Have a listen to a podcast created by Notebook.LM that I just created about Ai and News Rooms

AI News Domination: How the Future of Content Control Is Shaping Humanity—and What We Can Do About It

As an AI and digital media content consultant with experience working in communications agencies and writing articles for news organizations, I’ve seen how AI is transforming content creation from the inside. For brands, AI-generated content is already the norm. But there’s a stark difference between using AI to write an ad for Nike and using it to shape the news you see on CNN. Writing an ad is one thing—AI can speed up the process, making it more efficient. But when AI is writing the news, the stakes are exponentially higher. It's no longer just about selling products—it's about shaping public opinion, controlling narratives, and even manipulating truth.

AI News Bubble: A Reality We’re Already Living In

Let’s not sugarcoat it—this isn’t some far-off future we’re speculating about. AI is already deeply embedded in newsrooms, actively influencing the stories we read and the way they are told. Major outlets like Reuters and The Associated Press are already relying on AI to write financial reports and sports updates, while Forbes uses AI to assist journalists with content generation. These aren’t isolated cases; they represent a massive shift in the way news is created.

The problem is that AI doesn’t just help write stories—it also decides which stories matter. Algorithms optimize for engagement, not accuracy, truth, or balance. This means that sensationalism and fear are often prioritized over nuance and context, leading to a kind of “AI news bubble” where the most clickable content dominates. And unlike human editors, AI doesn’t have a moral compass or journalistic values guiding these decisions. Its goal is to maximize attention and profits.

The Difference Between Helping and Controlling

Here’s an important distinction I’ve come to realize in my work: there’s a big difference between having AI help you edit something and using AI to write the entire piece without your input. When I write articles for clients or publications, AI tools can be incredibly useful for checking clarity, grammar, and making sure my writing is polished. But the perspective and core ideas are still mine. I’m the one deciding what I want to say, and AI simply helps me say it better.

Where things get dangerous is when AI starts picking the stories we write—or worse, subtly changing the meaning of what we’ve written. This is something I’m constantly aware of when I use AI in my work. AI is a powerful tool, but it often tries to soften or alter certain points, especially in cases where it doesn’t want to seem like the “bad guy.” For example, while writing this very article, AI tools frequently suggested adjustments that would downplay the criticism of AI’s role in media. It’s a subtle form of influence, but over time, these small tweaks can strip the nuance out of our work and replace it with a more algorithmically palatable narrative.

I’ve seen this firsthand. The other day, I asked Google about a topic that had a lot of nuance—something open to interpretation, with multiple perspectives. I wanted to explore the different sides of the issue, but Google’s AI didn’t give me that. It presented the topic in a definitive, one-sided way. When I pressed for more nuance, it simply refused to engage with alternative perspectives. That’s exactly the issue I’m talking about: AI isn’t just giving us facts; it’s also controlling which facts we get, and how we should interpret them. This is a subtle but dangerous erosion of critical thought and debate.

Content for Brands vs. Content for News: The Stakes are Different

To be clear, AI has a place in content creation for brands. I’ve seen how AI can speed up the production of copy for marketing campaigns or product descriptions, and it can help brands quickly adjust messaging to fit trends or consumer preferences. But that’s where the line should be drawn. Writing an ad for Nike is vastly different from writing a piece of journalism that could influence public opinion on global conflicts, politics, or health.

Brand content is designed to sell, and AI is a great tool for that. But when it comes to news, AI’s focus on clickability can distort the truth. News should aim to inform, provoke thought, and sometimes challenge readers. When AI takes control of that process—prioritizing engagement over substance—it compromises the core values of journalism. The stories we need to hear are often the least sensational, and AI doesn’t understand that. It only knows what will generate the most attention.

AI’s Philosophical Dangers: Influence is Already Automated

This is not just about the automation of tasks—it’s about the automation of influence. Thought leaders like Elon Musk and Nick Bostrom have long warned that the real danger of AI lies not in robots taking over the world, but in how it centralizes control over information. Musk has pointed out that AI allows a small number of companies or governments to dictate what people see, hear, and believe. This isn’t some future threat—it’s happening right now.

AI algorithms are already influencing public perception in ways we’re only beginning to understand. They don’t just curate the news—they create it, optimizing for engagement and profit. They amplify biases, push sensationalism, and quietly manipulate our reality. This automation of influence is one of the most profound shifts in human communication we’ve ever seen, and it’s happening right under our noses.

What Can We Do? Taking Immediate Action

So, what’s the solution? If AI is already transforming newsrooms and shaping the media we consume, is there anything we can do to stop it? The answer is yes—but we need to act now.

1. Seek Out Human-Driven News: There are still independent news outlets that resist the pull toward AI automation. Sites like ProPublica, The Guardian, and The Intercept continue to prioritize investigative journalism and human editorial judgment. Supporting these organizations by subscribing, donating, or simply reading their work is one of the most direct ways we can fight the AI-driven news bubble.

2. Demand Transparency: News organizations should be required to disclose when AI has been involved in the creation of content. This is critical. Just like we demand transparency around conflicts of interest in journalism, we need the same for AI involvement. We deserve to know when the information we’re consuming has been shaped by an algorithm.

3. Push for Regulation: Governments need to step in and regulate how AI is used in journalism. AI shouldn’t be left to run unchecked, optimizing solely for engagement. Just as we have laws against misleading advertising and misinformation, we need similar rules for AI-generated content in the news to ensure it serves the public interest rather than just corporate profits.

4. Diversify Your News Consumption: It’s crucial to diversify where you get your news. Relying solely on AI-curated feeds or a single source limits your understanding of complex issues. Make an effort to read news from various perspectives, including independent, human-driven outlets. Critical thinking is essential in a media environment increasingly shaped by algorithms.

A Glimmer of Hope: Humans Still Matter

The good news is that while AI is rapidly advancing, humans still play a crucial role in shaping media. AI can generate content, but it still lacks the nuance, ethical considerations, and deeper understanding that human journalists and editors bring. New outlets like The Correspondent and others focused on slow, thoughtful journalism are proving that there is still demand for well-researched, human-crafted stories. These platforms provide the context and depth that AI-driven, engagement-optimized content often misses.

Immediate Action: A Podcast on AI and Media

If you’re interested in diving deeper into this topic, here’s something fascinating: Google’s new Notebook.LM platform can turn any subject into a back-and-forth, two-person podcast. I tried it, and the result was incredibly realistic. Check out this AI-generated podcast discussing the very issue of AI in news and media created directly from this article—it's a startlingly lifelike example of where this technology is heading.

Have a Listen!