Velocity Ascent

Looking toward tomorrow today

Joe Skopek

Many Visions, One Destination: Building Trust Across the Internet of AI Agents

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MCP, ACP, A2A, and ANP.These protocols aren’t just academic—they’re the blueprint for real-world, scalable, and secure AI ecosystems.

Earlier this week (05.14.25), I had the good fortune of attending the NANDA Summit hosted by MIT’s Media Lab, a forward-looking initiative on building a trust layer for the Internet of AI Agents. The conversations were sharp, current, and deeply relevant to anyone working on AI infrastructure or growth.

The four horsemen of the protocols.

A tremendous opportunity to hear directly from those leading the charge like Ramesh Raskar(MIT media lab), John Roese (CTO, Dell) and Todd Segal (A2A, Google), and many others whose teams are helping define how agents communicate, delegate, and earn trust in autonomous systems.

It was at this summit that I encountered a new paper that distills the current state of agent interoperability into four leading protocols: MCP, ACP, A2A, and ANP.

These protocols aren’t just academic—they’re the blueprint for real-world, scalable, and secure AI ecosystems.

Everything You Ever Wanted to Know About Agent Protocols*

* But Were Afraid to Ask

If you’re building AI systems that rely on agents talking to each other – or to tools, services, and other networks – you’re going to run into one unavoidable truth: interoperability is the battlefield. And that means protocols. MCP, ACP, A2A, ANP – these aren’t just acronyms. They’re the wiring behind everything we’re starting to call “agentic.”


Start with MCP if you want stability. Graduate to ACP for flexibility. Go A2A for teamwork. And keep your eyes on ANP if you’re thinking long-game.

This post breaks down the four major protocols that matter, in plain English: what they do, where they fit, and why one size definitely doesn’t fit all. No fluff, no hype – just a clear look at what’s out there and how to choose what to build on. Whether you’re wiring up local agents to hit APIs, coordinating large-scale tasks inside an enterprise, or dreaming about open agent marketplaces – there’s a protocol for that.

MCP, ACP, A2A, and ANP The Four Horsemen of the Protocols.

We have assembled a concise technical explanation of each protocol, followed by a simplified comparison table ranking them from most stable/general-use to most emerging.

MCP – Model Context Protocol

MCP is designed as a tightly structured, JSON-RPC-based client-server protocol that standardizes how large language models (LLMs) receive context and interact with external tools.

Think of it as the AI equivalent of USB-C: a unified plug-and-play standard for delivering prompts, resources, tools, and sampling instructions to models. It supports robust session lifecycles (initialize, operate, shut down), secure communication, and asynchronous notifications. It excels in environments where deterministic, typed data flows are essential – like plug-in platforms or enterprise tools with strict integration requirements. Its predictability and strong structure make it the go-to protocol for stable, general-purpose AI agent interactions today.

ACP – Agent Communication Protocol

ACP introduces REST-native, performative messaging using multipart messages, MIME types, and streaming capabilities. This protocol is best suited for systems that already speak HTTP and need richer communication models (text, images, binary data). It sits one layer above MCP – more flexible, more expressive, and excellent for multimodal or asynchronous workflows.

ACP allows agents to communicate through ordered message parts and typed artifacts, making it a better fit for web-native infrastructure and cloud-based multi-agent systems. However, it requires a registry and stronger orchestration overhead, which can introduce complexity.

A2A – Agent-to-Agent Protocol

Developed with enterprise collaboration in mind, A2A allows agents to dynamically discover each other and delegate tasks using structured Agent Cards. These cards describe each agent’s capabilities and authentication needs.

A2A supports both synchronous and asynchronous workflows through JSON-RPC and Server-Sent Events, making it ideal for internal task routing and coordination across teams of agents. It’s powerful in trusted networks and enterprise settings, A2A assumes a relatively static or known network of peers. It doesn’t scale easily to open environments without added infrastructure.

ANP – Agent Network Protocol

ANP is the most decentralized and future-leaning of the protocols. It relies on Decentralized Identifiers (DIDs), semantic web principles (JSON-LD), and open discovery mechanisms to create a peer-to-peer network of interoperable agents. The Agents describe themselves using metadata (ADP files), enabling flexible negotiation and interaction across unknown or untrusted domains.

ANP is foundational for agent marketplaces, cross-platform ecosystems, and long-term visions of the “Internet of AI Agents.” Its trade-off is stability—it’s complex, requires DID infrastructure, and is still maturing in practice.


Most Open and Accessible Protocols (Ranked)

RankProtocolStabilityKey Characteristics
1MCPMost stableJSON-RPC, deterministic tool access, tightly scoped
2ACPHigh stabilityREST-native, multimodal messages, good for web systems
3A2AMediumEnterprise task routing, Agent Cards, internal networks
4ANPEmergingDecentralized, peer-to-peer, DIDs, future-focused

Metaphor: The Enterprise Office

How the 4 Protocols Interact

When we talk about AI agents and protocols, it’s easy to get lost in jargon – JSON-RPC, DIDs, multipart messages. But if you strip it all down, what we’re really building is organizational behavior, similar to the IRL enterprise office: how smart systems talk to each other, share context, delegate tasks, and connect beyond the firewall.

Picture your company as a classic enterprise office building. People, departments, tools, workflows. Now imagine we’re embedding AI agents into that environment – some helping you internally, others reaching outside. The four major protocols – MCP, ACP, A2A, and ANP – each have a role in making that machine run.

Here’s how they work together, using the structure of a modern office to map it all out.

  • MCP is the internal phone system. It lets employees (LLMs) call specific departments (tools) to request information or get a task done. It’s precise, secure, and fast – perfect when you already know who does what. No outside lines, just clean internal calls.
  • ACP is your email and messaging platform. People send messages, attachments, updates, and files back and forth, sometimes in real time, sometimes not. It’s flexible and works across teams – even those who don’t use the same apps—as long as they all agree on format and language.
  • A2A is the company intranet with smart assistants (agents) embedded in every department. Instead of sending an email or making a call, you drop a request into your local agent, and it finds the right person (or agent) elsewhere to take action. You don’t have to know who does what – it figures that out and gets the job done.
  • ANP is the front lobby where external contractors, partners, and vendors come in. But instead of swiping a badge, they identify themselves with cryptographically signed IDs (DIDs), check in with a self-service kiosk (Agent Description), and negotiate access dynamically. It’s open, secure, and built for a future where not everyone works in your building.

In short:

  • MCP helps the agents work with tools.
  • ACP helps them talk to each other.
  • A2A helps them collaborate internally.
  • ANP helps them connect externally.

Used together, these protocols turn your office from a collection of disconnected departments into a well-orchestrated, future-ready enterprise.

Why does this matter to a CEO?

Interoperability protocols are not just technical choices—they’re strategic decisions that determine whether your AI investments scale or stall.

Without standardized protocols, your AI agents become siloed tools: expensive, brittle, and unable to coordinate across platforms, teams, or partners. Every new integration becomes a custom build, with mounting costs and unpredictable security exposure.

Protocols like MCP, ACP, A2A, and ANP define how agents connect, share context, and execute across environments—from internal apps to global marketplaces. The right protocol strategy turns isolated AI functions into scalable systems. It reduces integration overhead, protects against vendor lock-in, and positions your organization to participate in larger, more open ecosystems.

In plain terms:

  • MCP gives you stable, secure tool access—ideal for internal control.
  • ACP opens the door to richer, more flexible agent interactions.
  • A2A allows your agents to collaborate and delegate across departments or partners.
  • ANP sets you up for future markets where agents transact and negotiate in open environments.

Get this right, and your AI strategy doesn’t just keep pace, it sets the pace.

Elevator Pitch: Piloting AI in a Legacy Enterprise Using Agent Protocols

Most legacy enterprises don’t need to “rip and replace” to get AI working, in many cases they need a controlled, modular way to plug AI into what already works.

Use four agent protocols – MCP, ACP, A2A, and ANP – as a phased architecture to do just that.

  • Start with MCP to safely connect your AI agents to internal tools, APIs, and datasets. No surprises, just structured, secure interactions. Think of it as AI accessing your backend – without refactoring it.
  • Layer in ACP to enable richer, asynchronous, multimodal messaging between agents and systems. Perfect for integrating agents with your web stack, dashboards, or notification systems – without breaking the frontend.
  • Add A2A when you’re ready to delegate tasks across business units – marketing agents talking to finance agents, HR bots syncing with IT systems. This unlocks true automation and collaboration inside the firewall.
  • Deploy ANP selectively to connect with trusted partners, vendors, or regulators over open protocols. It’s the gateway to future interoperability – without giving up control.

Together, this stack creates a low-risk, high-leverage pilot: AI agents that work with legacy systems today, and scale into open ecosystems.

Claude’s Managed Coordination Protocol (MCP)

Anthropic’s Managed Coordination Protocol (MCP) is Claude’s system for structured, safe, and efficient task delegation between multiple sub-agents or “tool-using” capabilities within the Claude ecosystem.

Google’s Agent-to-Agent (A2A) Protocol

Google’s A2A protocol is a pioneering framework that enables independent AI agents to communicate, delegate tasks, and reason together dynamically. Built for scale and flexibility, it supports emergent, collaborative intelligence—where specialized agents work together like an adaptive digital team.

SOURCE:

A2A:
Dev Document

MCP:
Dev Document

Fashion Tech: The Present Future of Fashion and Technology

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From “Sketch to Storefront” to AI assisted workflows, the blend of fashion and artificial intelligence is creating limitless possibilities and opening untapped opportunities.

Emerging technology has repeatedly transformed the fashion industry, driving innovation across garment production, retail, and consumer experiences. Just as the sewing machine revolutionized garment-making, today’s machine learning and AI are redefining fashion circularity, optimizing production, enhancing personalization, and streamlining recycling processes.

Technology has dramatically reshaped the fashion industry at key moments.

The sewing machine of the 19th century revolutionized garment production by making it faster, more precise, and accessible to a wider audience through mass production. Its most significant impact was the dramatic reduction in production time for creating garments.

“The Battle of the Sewing Machines” was composed and arranged by F. Hyde for the piano, and was published in 1874. In this image the Remington “army” is marching towards the fleeing Singer, Howe, Succor, Weed, and Willcox & Gibbs sewing machines. 

This increased efficiency led to lower costs, making fashion accessible to a wider audience. Additionally, the sewing machine enabled the standardization of sizes and styles, resulting in more consistent quality and fit. This was a crucial development that laid the groundwork for ready-to-wear fashion.

The increase in precision offered by sewing machines allowed designers to experiment with intricate patterns and styles, pushing the boundaries of creativity. The ability to produce clothing efficiently contributed to the rise of fashion houses and brands, which ultimately paved the way for the modern fashion industry.

Just as sewing machines revolutionized garment making by replacing hand sewing, AI assistance is now streamlining digital production by automating repetitive or mundane tasks once performed by humans.

Creating an uninterrupted digital path from production to consumer.

The rise of e-commerce in the late 1990s revolutionized shopping, with platforms like Amazon and ASOS making fashion more accessible online. This shift pushed traditional retailers to adapt, while fueling the rapid growth of computer-based production solutions, digital marketing and social-driven sales.

Shortly after being introduced social media became a powerful tool with Fashion industry as a leading user, reshaping brand-consumer interactions and marking the beginning of the digital influencer in fashion marketing.

As of 2023, the top three online fashion retailers by sales are Shein, Walmart, and Amazon. Shein leads the market with $14.4 billion in fashion revenues, followed closely by Walmart with $12.3 billion. Amazon, though dominant in various sectors, now ranks third in the fashion space, generating $8.4 billion in 2023. These platforms excel by offering a combination of convenience, affordability, and extensive product assortments.

Traditional brick-and-mortar fashion retailers like Nike, H&M, and Inditex (Zara) have solidified their positions as industry leaders by seamlessly blending their physical store presence with innovative digital strategies, leveraging e-commerce to expand their reach and maintain their market dominance.

As of 2023, brick-and-mortar fashion retailers with strong online sales include:

  1. Nike – Nike leads in both physical and online sales, thanks to its innovative digital strategies such as the Nike App and its direct-to-consumer (DTC) model, which bridges the gap between in-store and online experiences.(A)
  2. H&M – Known for its fast fashion, H&M continues to thrive with a robust online presence, coupled with its global store network. Its focus on e-commerce has helped it remain competitive in the digital era.
  3. Inditex (Zara) – Zara‘s parent company, Inditex, is a major player in both physical and online retail, offering seamless integration between its brick-and-mortar stores and digital platforms, contributing to its top rankings.

Bricks-and-Mortar fashion news: Vogue Business – What Lies Ahead for Brick-and-Mortar Luxury in 2023: Placer.ai

These brands effectively combine their in-store experiences with digital innovations to maintain leadership in both arenas.

The Nike App is an example of Nike’s commitment to innovative digital strategies that go deep. Ready-to-use advice and feature stories on everything from Nike pros to neighborhood teams.

Currently, artificial intelligence has introduced a new era of personalized fashion, with AI tools helping designers analyze trends, streamline production, and create customized shopping experiences through virtual fitting rooms and styling algorithms. At the core of the customized shopping experience is “hyper-personalization”; the use of advanced data, AI, and analytics to deliver highly tailored bespoke experiences that meet individual customer preferences in real time.

Browzwear and LaLaLand.ai are two leading tools in fashion tech.

Each wave of technological advancement has redefined the fashion landscape, pushing the industry toward greater creativity, accessibility, and sustainability. With the growing focus on circularity in fashion, technology is now enabling brands to design for longer product lifecycles, reduce waste, and embrace sustainable practices like recycling and upcycling.

This shift, alongside innovations in AI, 3D printing, and smart textiles, is driving the industry toward a future where sustainability and circularity are core to fashion’s evolution.

Browzwear and LaLaLand.ai are redefining how fashion is created, marketed, and consumed.

Much like the sewing machine did during the Industrial Revolution, Browzwear Browzwear and LaLaLand.ai are redefining how fashion is created, marketed, and consumed. The sewing machine revolutionized garment production by increasing efficiency and allowing for mass production – these modern tools are streamlining design processes and enhancing collaboration in today’s fashion industry.

The sewing machine enabled designers to produce clothing more quickly and affordably, making it accessible to a broader audience. Similarly, Browzwear and LaLaLand.ai are fostering innovation and sustainability by reducing reliance on physical samples and promoting digital solutions. They have created a “Sketch-to-Storefront” digital workflow that takes all aspects of the production cycle and supply chain into account. This shift not only minimizes waste but also allows brands to respond swiftly to market demands and trends.

Browzwear’s 3D fashion design software, reduce days of iterations and reach market faster. The apparel digital twin visualizes and validates every detail, the first physical sample is the only sample.

Moreover, just as the sewing machine transformed individual craftsmanship into a collective industry effort, these technologies enhance creativity and representation in fashion. By enabling diverse designs and virtual models, they contribute to a more inclusive fashion landscape. In essence, the impact of these contemporary tools parallels the profound changes initiated by the sewing machine, driving the fashion industry toward a more innovative, sustainable, and accessible future.

Reality Check: Federated Agentic and Decentralized Artificial Intelligence

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AI is evolving rapidly, and a new approach is gaining momentum: agentic AI. Unlike current tools like ChatGPT, which require human input to operate, agentic AI is designed to act independently—monitoring competitors’ marketing efforts, scheduling real-time content updates, or predicting real-world equipment needs—without waiting for instructions. As these systems take on more autonomy, robust security measures become essential to ensure their actions remain safe, aligned, and trustworthy.

MIT Media Lab – Ramesh Raskar: A Perspective on Decentralizing AI

“A.I. co-pilots, assistants and agents promise to boost productivity with helpful suggestions and shortcuts. “

New York Times, September 2024

While this technology is still in the early stages, it’s being hyped as the next big thing in AI, promising to boost productivity and innovation. However, we’re not there yet—agentic AI is mostly a vision for the future that’s rapidly approaching.

Governance Challenges: Accountability, Regulation, and Security

Governance issues with Agentic AI and decentralized computing stem from a lack of centralized control, making regulation and enforcement difficult across jurisdictions. In decentralized systems, no single authority oversees operations, while in Agentic AI, autonomous decisions raise questions about accountability, such as who is responsible when things go wrong—developers, users, or the AI itself.

Ethical and legal compliance is a significant challenge, as both agentic AI and decentralized systems often operate beyond traditional frameworks, making it difficult to ensure they adhere to laws or ethical guidelines.

Security is another concern. Decentralized systems may suffer from vulnerabilities due to inconsistent protocols, while agentic AI can be manipulated or exhibit harmful behaviors. Existing regulatory frameworks are frequently outdated, creating oversight gaps for these emerging technologies.

Both technologies also face issues with coordination and standardization. Decentralized systems require consensus among many participants, which can slow progress, and agentic AI currently lacks widely accepted standards.

Finally, the lack of transparency in AI decision-making, combined with the difficulty of auditing decentralized systems, further complicates governance and accountability.

This is where Federated Machine Learning offers a compelling solution.

Federated Machine Learning (FedML)

FedML is an approach that enables organizations with limited data—so-called “small data” organizations—to collaboratively train and benefit from sophisticated machine learning models. The definition of “small data” depends on the complexity of the AI task being addressed.

In Pharma, for example, having access to a million annotated molecules for drug discovery is relatively small in view of the vast chemical space.

In Marketing that small data set might be in the form of brand specific visual data—brand guidelines scattered across PDFs, emails, and shared drives.

Image: Jing Jing Tsong/theispot.com

Is Federated Agentic AI the answer?

Federated Agentic AI refers to a blend of two advanced AI concepts: federated learning and agentic AI.

Federated learning enables AI models to be trained across decentralized devices or data sources while keeping the data local and secure, thereby enhancing privacy and scalability. Meanwhile, agentic AI refers to self-contained systems that, once implemented by humans, operate autonomously around the clock. These systems are capable of controlled decision-making and can adapt based on real-time data without further human intervention.

When combined, Federated Agentic AI allows multiple autonomous agents to collaborate across a secure distributed network. These agents can handle tasks independently while continuously learning from local data sources, without needing to share sensitive information across the network. This setup is particularly useful in environments like healthcare, finance, or IoT, where data privacy is critical but complex tasks still require intelligent automation.

For instance, a federated agentic system might be deployed in a network of smart devices where each device autonomously manages specific tasks (e.g., thermostats optimizing energy use) while learning from local data (e.g., weather conditions). These devices can also share insights without revealing user data, improving overall system efficiency and privacy​.

Final Thoughts: Designing for new technology is a completely different challenge from a traditional design project.

Typically, users already know how to interact with familiar products—like swiping a credit card at a payment terminal or using a TV remote to change channels. But with emerging technology, there are no familiar cues, making it harder for users to figure out how to engage with it effectively. Think back to when users first encountered smartphones—there was no clear precedent for touchscreens or gestures, making it challenging to learn entirely new interactions.

Adoption of new tech often lags behind confusion and speculation, so creating a seamless, intuitive user experience is essential for success.

That said, designing something entirely new isn’t easy—but it’s exactly where the team at Velocity Ascent excels. We navigate the space between excitement for emerging technology and the need to deliver real, secure, user-centered value. By following key principles, we transform unfamiliar tech into products that people and teams love to work with.

For CMOs, this means a potential shift in how to approach marketing and customer engagement. As AI becomes more autonomous, the question will be: how do we control and guide these powerful tools to enhance our strategies while still ensuring privacy? Ultimately it is about using AI in smarter, more effective ways to drive business growth.

Sources:

Analytics Vidhya, The GitHub Blog

MIT Media Lab: Decentralized AI Overview

A Perspective on Decentralizing AI

Generative AI: Revolutionizing Video & Image Production

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Video & Imagery Production is undergoing a seismic shift, thanks to the arrival of advanced AI technologies like Midjourney and Runway.

The workflow of video and image production is undergoing a seismic shift, thanks to the merging of advanced AI technologies like Midjourney v6.1 and Runway Gen-3. This powerful combination is more than just a technical marvel; it’s a transformative tool for marketing agencies, revolutionizing the way they approach video content creation.

Accelerating Production with AI Synergy

For marketing agencies, the need to produce high-quality video content quickly and efficiently is paramount. The integration of Midjourney v6.1 and Runway Gen-3 offers a solution by streamlining the pre-production process. These tools allow creators to visualize and animate storyboards at an unprecedented pace, reducing the time it takes to move from concept to final product.

This speed and flexibility mean that agencies can handle more projects simultaneously, increasing their output without sacrificing quality. By automating time-consuming tasks, AI enables teams to focus on refining creative ideas rather than getting bogged down in the technical aspects of production.

Our Experimentation:

We conducted several dozen tests and reviewed numerous articles and papers online to evaluate the integration of Generative AI in early-stage storyboard development for videos and its impact on agency workflows. The results were eye-opening.

Source Image Creation:

For this experiment, the creative brief required the following: the image must include a group of friendly seniors smiling around a smartphone at a picnic table in the summer, during golden hour. The first attempts were promising, and after a few subtle adjustments to the prompt, we began to achieve ‘realistic’ results close to stock photography.

Early experiment generating a group of Seniors.

A good start, but the image looked a bit too ‘Princess Bride,’ with colors that were far too saturated. While it’s possible to use the SREF in Midjourney and a color palette from Adobe Color to color grade your Midjourney imagery, we opted for a more hands-on approach. We created a custom color range tailored to our target, resulting in improved color balance and a higher degree of ‘believability.’ Much of the effort went into refining the output.

Array of images generated during refinement.

Our second goal was to enhance the appearance of the seniors. Initially, they appeared, forgive the ageism, too old for our demographic requirements. Finally, we adjusted the prompt to eliminate unwanted artifacts, such as seniors holding two phones instead of one and glasses melting into the table.

Final image chosen for use in video storyboard.

The final image did more than achieve the creative brief baseline requirements – it created a “feeling”. You can almost feel the warmth of a late summer day and the joy of the gathered Seniors. With the image and prompts finalized our next step was to move over to Video Gen.

Video Generation:

Since we would just be using AI Video Generation as a storyboard and not for broadcast we ran the first series as “vanilla prompts”. This allowed us to quickly generate action that could be placed in the storyboard sample with lots of room for feedback and adjustment.

Early stage test of AI Generative Video

Our initial tests yielded the expected otherworldly creations; the video starts off normally but quickly spirals into a surreal, science fiction fever-dream with all but one of the Seniors sliding into oblivion. Applying a revised prompt set and rethinking the scene resulted in a new direction more in line with the requirements.

Late stage test of AI Generative Video

The results are nothing short of fascinating, the motion is smooth and the Seniors appear to be in a natural setting acting normally. There are still some anomalies that are fine-tuned out of the finished video. For example in the sample video above you will notice on the left side of the scene the smartphone morphs into the pint glass. A few more edits to the prompt will resolve this.

Given the time and resources typically needed for custom video production or extensive research across stock agencies, the speed of this process is remarkably faster. Even more exciting is that we are only at the dawn of this technology.

Cost Reduction and Enhanced Creative Output

The financial benefits of this AI-driven approach are significant. By reducing the time and resources required for video production, agencies can lower costs for their clients while delivering even more compelling content. This efficiency opens up opportunities for smaller businesses to access high-quality video production, which was previously only within reach of larger companies with bigger budgets.

Moreover, the enhanced creative capabilities of these AI tools allow for rapid prototyping and experimentation. Agencies can quickly test and iterate on ideas, ensuring that the final product aligns perfectly with the client’s vision. This ability to fine-tune creative concepts on the fly is a game-changer for marketing campaigns, where the margin for error is often slim.

While AI can automate certain aspects of video creation, it lacks the nuanced understanding of brand voice, audience preferences, and emotional resonance that only human creators can bring.

The Human Element: Essential and Irreplaceable

Despite the incredible advancements in AI technology, the human element remains crucial in the production process. While AI can automate certain aspects of video creation, it lacks the nuanced understanding of brand voice, audience preferences, and emotional resonance that only human creators can bring.

The role of the human in this AI-driven workflow is to guide and shape the creative direction, ensuring that the content not only meets technical standards but also connects with viewers on a deeper level. AI tools serve as powerful assistants, augmenting human creativity but never replacing it. This collaboration between human ingenuity and machine efficiency is where the true potential of AI in video production lies.

Transforming the Advertising and Entertainment Industries

The impact of this AI merging extends beyond marketing agencies to the broader advertising and entertainment industries. The ability to rapidly prototype ideas and create high-quality visual content democratizes the production process, making it accessible to a wider range of creators, from seasoned professionals to enthusiastic hobbyists.

As this technology continues to evolve, we’re likely to see a shift in how visual media is consumed and interacted with. The lines between imagination and realization are becoming increasingly blurred, allowing for more immersive and personalized storytelling experiences.

Impact on Traditional Video Production:

AI tools are transforming video production by automating many aspects of the process, such as editing, special effects, and even scriptwriting. This automation can greatly speed up production timelines and reduce costs, enabling agencies to produce more content with fewer resources. For smaller teams or independent creators, AI provides access to high-quality production tools that were once out of reach.

However, this increased efficiency could lead to a reduction in the demand for certain roles, such as video editors, animators, and even directors. As AI becomes more sophisticated, the need for manual intervention in repetitive or technical tasks will decrease, potentially leading to job losses in these areas.

Impact on Stock Agencies:

AI-generated content also poses a challenge to traditional stock agencies. With AI tools capable of creating high-quality images, videos, and even audio, the reliance on stock libraries may diminish. Creators can now generate custom content tailored to their specific needs without having to sift through existing libraries. This shift could reduce demand for traditional stock footage and images, impacting the revenue streams of stock agencies.

Potential Job Losses:

While AI has the potential to displace certain jobs, it’s important to recognize that it also creates new opportunities. Jobs focused on AI tool development, data analysis, and AI integration into creative processes will likely grow. Additionally, roles that require a high degree of creativity, strategic thinking, and emotional intelligence—skills that AI currently cannot replicate—will remain essential.

The key for professionals in the industry will be to adapt to these changes by learning to work alongside AI, using it as a tool to enhance their capabilities rather than viewing it as a replacement. Those who can harness the power of AI to augment their creativity and productivity will find new opportunities in the evolving landscape of video production and stock media.

The importance of the human touch in this process cannot be overstated.

The Future of Storytelling

The future of video production is here, and it’s powered by AI. For marketing agencies, embracing this technology means not only staying competitive but also pushing the boundaries of what’s possible in creative content creation.

However, the importance of the human touch in this process cannot be overstated. As we move forward into this new era of AI-driven production, the collaboration between human creativity and AI efficiency will be the key to unlocking new levels of innovation and storytelling.

AI and Efficiency: A Lazy Person Will Find an Easier Way—But Should They?

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You may be aware of the expression “A lazy man will always find an easier way”*. I first heard it from a custodian where I was working nights to raise money for university. One dinner break he explained his method for achieving 8 hours of work in 4 on the night shift. He had devised a system of optimizing speed while limiting the total number of footsteps required to clean 12 classrooms an evening.

I won’t detail his method here but I will say it was far better thought out than a few high-tech startup ideas I have been pitched. In this context of efficiency we can consider that “lazy” refers to a person who seeks to avoid effort or exertion by finding shortcuts or easier methods.

The statement can also be hailed as a testament to the entrepreneurial spirit emphasizing the pursuit of efficiency and optimization. History is replete with instances where individuals, driven by a desire to minimize effort, have sparked innovation and transformed industries.

Examining Efficiency through the Lens of Innovation

Take, for example, the advent of assembly line production pioneered by Henry Ford. Ford was inspired by the meat-packing houses of Chicago and a grain mill conveyor belt he had seen. If he brought the work to the workers, they spent less time moving about.


It’s an interesting perspective to consider how certain traits, like what some may perceive as “laziness,” can play a role in someone’s success. In the case of Henry Ford, it’s not necessarily that he was lazy in the conventional sense, but rather that he was known for his desire to find easier and more efficient ways of doing things.

Ford famously said, “I believe that the average farmer puts to a really useful purpose only about 5% of the energy he expends. Not only is everything done by hand, but seldom is a thought given to a logical arrangement.”

Assembly line at the Ford Motor Company’s Highland Park plant ca. 1913.

No offense to our Mr. Ford but working farms tend to be pretty well thought out. This sentiment does however clearly reflect Ford’s disdain for inefficiency and his drive to streamline processes.

By streamlining the manufacturing process, Ford not only revolutionized the automotive industry but also exemplified how laziness, when channeled effectively, can lead to remarkable advancements in productivity and profitability.

A cautionary tale…

On the flip side, however, the notion that laziness inherently leads to innovation warrants scrutiny. While it’s true that seeking shortcuts can spur creativity, it can also breed complacency and undermine quality.

Elizabeth Holmes, Chairman, CEO, and Founder of Theranos, speaks on stage with TechCrunch writer and moderator Jonathan Shieber at TechCrunch Disrupt at Pier 48 on September 8, 2014.

Consider the case of Theranos, the health technology startup company founded by Elizabeth Holmes. Driven by the desire to revolutionize blood testing, Holmes embarked on an ambitious journey to simplify the process, but the challenge of raising substantial funds intensified the pressure, pushing her to deliver results in an increasingly high-stakes environment.

In the end her relentless pursuit of a shortcut, and failure to heed the feedback of the Chief Scientist, led to fraudulent practices and ultimately the downfall of the company, it’s investors, and Holmes. This cautionary tale underscores the importance of distinguishing between the concepts of constructive laziness that drives innovation and reckless laziness that invites disaster.

It’s essential to recognize that not all shortcuts are created equal.

While the concept of “finding an easier way” can indeed catalyze innovation, it’s essential to recognize that not all shortcuts are created equal. In the dynamic landscape of business, embracing “laziness” as a catalyst for efficiency requires a nuanced approach. It demands a balance between recognizing opportunities for optimization and maintaining a steadfast commitment to quality and integrity.

Using Ford’s assembly line as a starting point we will dissect seven core elements across three categories; the “Lesson” learned from Ford’s experience, a “Modern Challenge” faced today, and finally how the “Application” of AI to the challenge provides a solution.

1. Efficiency and Scalability:

  • Lesson: The assembly line allowed Ford to produce cars faster and at a lower cost by breaking down the production process into smaller, repeatable tasks.
  • Modern Challenge: Today, businesses face the challenge of increasing workflow efficiency while maintaining a healthy work-life balance for employees.
  • Application: AI and Large Language Models (LLMs) can assist businesses in automating repetitive tasks, streamlining processes, and improving decision-making. For example, AI can handle routine customer service inquiries, allowing human employees to focus on more complex and creative tasks. This not only increases productivity but also respects the human need for meaningful work, contributing to a better quality of life and a healthier, more productive workforce. By leveraging AI responsibly, companies can scale their operations without compromising employee well-being.

2. Standardization:

  • Lesson: Ford standardized parts and processes, enabling the production of large quantities of cars with consistent quality, which also simplified repairs and maintenance for consumers.
  • Modern Challenge: In today’s fast-paced environment, businesses must maintain consistent quality across their offerings while adapting to rapid changes in technology and customer expectations.
  • Application: AI and LLMs can help standardize business processes such as data entry, document management, and content creation, ensuring consistency and accuracy across all outputs. For instance, an AI-driven content management system can automatically format and proofread documents, reducing errors and maintaining a high standard of quality. At the same time, these tools can be configured to allow for flexibility and creativity where needed, ensuring that the human touch remains integral to the business.

3. Customer-Centric Innovation:

  • Lesson: The assembly line made cars affordable to the average American, transforming them from luxury items into necessities by focusing on making products accessible to a broader market.
  • Modern Challenge: Companies today need to innovate in ways that resonate with diverse customer bases, often with varying needs and preferences, while maintaining inclusivity and accessibility.
  • Application: AI and LLMs can analyze vast amounts of customer data to identify trends and preferences, enabling businesses to tailor products and services to meet specific market demands. For example, personalized AI-driven marketing campaigns can ensure that different customer segments receive relevant offers and communications. This approach not only enhances customer satisfaction but also promotes inclusivity, ensuring that products and services are accessible to all, regardless of background or ability.

4. Iterative Improvement:

  • Lesson: Ford continually refined the assembly line process, improving efficiency, reducing waste, and lowering costs through an iterative approach.
  • Modern Challenge: Businesses today must constantly innovate and improve to stay competitive in a rapidly changing market, all while managing the potential burnout of employees from continuous change.
  • Application: AI and LLMs can facilitate iterative improvement by providing real-time analytics and feedback, helping businesses refine their strategies and products more effectively. For example, AI can monitor production processes and suggest optimizations, reducing waste and improving quality over time. Importantly, these tools can be designed to support employee workflows, offering suggestions without overwhelming workers, thereby promoting sustainable innovation that considers both efficiency and employee well-being.

5. Cost Reduction and Affordability:

  • Lesson: The assembly line drastically reduced the cost of producing cars, allowing Ford to lower prices and make the Model T affordable for the masses.
  • Modern Challenge: Today, companies must find ways to reduce costs without compromising on quality, while also addressing the growing demand for ethical and sustainable business practices.
  • Application: AI and LLMs can help reduce costs by optimizing supply chain management, reducing energy consumption, and predicting maintenance needs. For instance, AI-driven predictive maintenance can lower the costs associated with equipment failure, ensuring that operations run smoothly and resources are used efficiently. By integrating AI in ways that also promote sustainable practices, businesses can reduce costs while maintaining a commitment to ethical production, ultimately making high-quality products more affordable and accessible.

6. Labor Specialization and Team Efficiency:

  • Lesson: By assigning workers to specific tasks, Ford improved productivity and reduced the time needed for training, maximizing efficiency.
  • Modern Challenge: Businesses today must balance the need for specialized roles with the need for employees to feel engaged and valued in their work, avoiding the pitfalls of repetitive or monotonous tasks.
  • Application: AI and LLMs can take over repetitive or mundane tasks, allowing employees to focus on more complex, specialized work that requires human creativity and problem-solving. For example, AI can handle data processing or routine administrative tasks, freeing up employees to engage in strategic planning or creative endeavors. This not only improves overall team efficiency but also enhances job satisfaction by allowing workers to focus on areas where they can make the most meaningful contributions.

7. Vision and Long-Term Thinking:

  • Lesson: Ford’s vision extended beyond simply building cars; he aimed to make them accessible to the general public, a long-term strategy that drove his success.
  • Modern Challenge: Companies today must develop long-term strategies that balance innovation, market demands, and sustainability, all while navigating the complexities of a globalized economy.
  • Application: AI and LLMs can assist in long-term strategic planning by analyzing market trends, predicting future scenarios, and providing insights into emerging opportunities. For instance, AI can model the potential impact of different business strategies over time, helping leaders make informed decisions that align with both short-term goals and long-term visions. By leveraging AI to support strategic decision-making, companies can ensure that their vision is both innovative and sustainable, taking into account the well-being of all stakeholders involved.

Let’s review three examples where this concept was embraced successfully at Amazon, Google and :

1. Amazon’s One-Click Ordering

Scenario: Jeff Bezos, inspired by the idea of minimizing customer effort, sought to reduce the friction in the purchasing process.

Outcome: Amazon developed the “One-Click” ordering system, which allows customers to purchase items with a single click, bypassing the traditional multi-step checkout process. This simplification not only improved the customer experience but also increased conversion rates and sales by removing barriers to purchase.

2. Google’s Search Algorithm

Scenario: Google co-founders Larry Page and Sergey Brin recognized that users were spending too much time sifting through irrelevant search results.

Outcome: Google developed an advanced search algorithm that prioritizes the most relevant results based on user queries and browsing history. This innovation significantly reduced the time and effort users needed to find information, making the search process faster and more efficient.

3. CallMiner’s Automated Customer Service Chatbots

CallMiner is the Leader in the Forrester Wave™: Conversation Intelligence for Customer Service, Q3 2023

Scenario: Companies faced increasing volumes of customer service inquiries, leading to inefficiencies and high operational costs.

Outcome: Many businesses adopted automated customer service chatbots to handle routine queries and tasks. These chatbots reduce the need for human intervention in repetitive scenarios, thereby streamlining customer service operations, reducing response times, and allowing human agents to focus on more complex issues.

With business efficiency leveraging breakthroughs in AI, a lazy person will find an easier way!

With efficiency increasingly’ leveraging breakthroughs in AI, a lazy person will inevitably find an easier way—often through a bit of creative disruption.

What might initially appear as a “lazy” approach has, time and again, sparked significant improvements in efficiency and user satisfaction. By embracing this unconventional mindset, organizations can tap into the power of simplicity, automation, and even a touch of disorder, to drive meaningful, innovative improvements. Maybe lazy thinking is exactly what’s needed to shake things up, try a new perspective and achieve smarter, more effective solutions.

*Title updated to reflect change in common language.