Artificial Intelligence (AI)

AI Biomechanical Engines: powering real-world solutions.

Velocity Ascent Live · December 20, 2024 ·

“AI-generated biomechanical engines” refers to a conceptual or technological framework that integrates artificial intelligence (AI) with biomechanical systems to create dynamic, adaptable mechanisms or devices.

That sure is a mouthful 🙂

Putting it simply, these engines could be a blend of biological principles (like human or animal movement) and mechanical or robotic engineering, powered or optimized through AI algorithms. Here’s a breakdown of the components:

1. AI (Artificial Intelligence): AI in this context would involve algorithms and computational models that enable machines or systems to learn, adapt, and make decisions, often based on data inputs. This could include machine learning, neural networks, or deep learning techniques, which allow systems to improve over time or adapt to new conditions.

2. Biomechanical: This term refers to the study and application of mechanical principles to biological systems. It typically involves understanding how living organisms, such as humans or animals, move, function, and interact with their environments. In the context of “engines,” it could refer to mechanical systems that replicate or augment biological movement, such as in prosthetics, robotics, or exoskeletons.

3. Engines: In this context, “engines” likely refers to systems or machines that drive or power a mechanism. It can include anything from the propulsion systems in robots to more complex devices designed to mimic biological functions, such as limbs, joints, or circulatory systems.

Practical Examples:

• Robotics: Robots that use AI to adapt their movements based on real-time data from their environment, effectively mimicking human or animal biomechanics. For instance, robots that use AI to improve their walking gait based on biomechanical principles.

• Prosthetics & Exoskeletons: AI-driven prosthetics or exoskeletons that use machine learning to optimize the movement of artificial limbs in ways that mimic natural human motion. These devices could learn to respond to a user’s intent more fluidly, adjusting for different terrains or activities.

• Biohybrid Systems: Devices that combine both biological and mechanical components, powered by AI to enhance movement or function. An example might be biohybrid robots that incorporate living cells or tissues, with AI systems guiding their movement.

Future Implications:

In the future, AI-generated biomechanical engines could lead to more advanced prosthetics, adaptive robotic limbs, and even new forms of biological augmentation.

Such systems could be used in medicine, personal assistance, military applications, or even in enhancing human capabilities. By integrating AI and biomechanics, these systems might be able to respond to their environment in real-time, providing more natural, human-like, or even superhuman abilities.

Examples in current practice

Certainly! Here are the links to the relevant sources for the examples I mentioned:

1. OpenAI’s Dactyl

• Overview of Dactyl:

• OpenAI’s Robotics: Solving Rubik’s Cube with a Robotic Hand (2018)

• OpenAI’s Dactyl

Dactyl uses reinforcement learning to manipulate objects like the Rubik’s Cube, demonstrating the combination of AI and biomechanical robotics.

2. Boston Dynamics’ Atlas

• Boston Dynamics Atlas:

• Atlas Robot – Boston Dynamics

• Atlas’ Parkour Performance (Video demonstration)

Atlas is known for its ability to perform complex physical tasks like running, jumping, and backflips using advanced machine learning algorithms to simulate human-like biomechanics.

3. Honda’s ASIMO

• Honda ASIMO:

• ASIMO – Honda Worldwide

• ASIMO’s Capabilities and Developments (ASIMO demo video)

ASIMO is a humanoid robot that combines AI with biomechanics to perform human-like movements, from walking to dancing, and adapt to its environment in real time.

4. ExoAtlet Exoskeleton

• ExoAtlet – Exoskeleton:

• ExoAtlet Official Website

• ExoAtlet Rehabilitation Exoskeleton (YouTube video)

The ExoAtlet exoskeleton is designed to help people with mobility impairments regain movement and stability using AI to adapt to the user’s biomechanics.

5. Sharp’s RoBoHoN

• RoBoHoN by Sharp:

• RoBoHoN Official Page – Sharp

• RoBoHoN in Action (YouTube)

RoBoHoN is a humanoid robot that integrates AI to enable complex movements like walking and conversation. It is a smaller, more interactive robot with applications in communication and mobility.

These resources provide additional context and examples of how AI has been applied to biomechanical engines, including robots and exoskeletons.

Fintech in Focus: AgenticAI is changing the Innovation Landscape.

velocityascent · November 29, 2024 ·

Ultimately, the human remains the final decision-maker, ensuring accountability and contextual judgment in critical scenarios.

The fintech and hedge fund sectors thrive on innovation, but adopting new technology comes with its own risks and responsibilities. Measured adaptation—like gradually integrating Agentic AI—allows organizations to innovate while maintaining stability and trust. In this post, we explore how emerging technologies, ethical considerations, and novel strategies are transforming the financial world.

What is Agentic AI?
Agentic AI refers to artificial intelligence systems designed to operate as autonomous agents that can analyze data, make decisions, and execute tasks with minimal human intervention. In fintech, Agentic AI can be used to create “digital twins” of team members—virtual replicas that simulate human decision-making in a closed environment that enhance productivity and collaboration. By using Twins, companies can streamline routine and mundane processes, freeing the team members to focus more energy on mission and innovation.

Ultimately, the human remains the final decision-maker, ensuring accountability and contextual judgment in critical scenarios.

1. Emerging Technology: Driving the Next Wave of Financial Innovation

AI-Powered Insights:
Hedge funds like Two Sigma have embraced machine learning to analyze non-linear relationships in market data, helping them outperform traditional quant models. The measured integration of AI ensures algorithm reliability before full-scale deployment.
Blockchain Beyond Bitcoin:
JPMorgan’s blockchain platform, Onyx, facilitates secure, efficient interbank payments. This cautious adoption of blockchain technology focuses on specific, scalable use cases instead of speculative ventures.
Quantum Computing:
Although still in its infancy, quantum computing firms like D-Wave are collaborating with financial institutions on experiments in portfolio optimization. A measured approach involves using quantum technology for supplementary analyses rather than replacing conventional models.
Edge Computing & Real-Time Decision Making:
Nasdaq’s use of edge computing allows for microsecond-level trade execution while maintaining centralized oversight to prevent errors.

2. Ethical and Privacy Concerns in a Hyperconnected World

Data Privacy and Usage:
Apple’s differential privacy model showcases how firms can analyze aggregated user data without compromising individual identities. Fintech companies could adopt similar anonymization techniques for customer analytics.
AI Bias and Fairness:
A study showed racial biases in credit scoring algorithms. By slowly rolling out AI with robust testing frameworks, companies like FICO are working to ensure fairness without jeopardizing operational efficiency.
Cybersecurity Threats:
Hedge fund Citadel’s investment in zero-trust architecture highlights the importance of assuming all network traffic is potentially compromised and taking preventive measures accordingly.
ESG Metrics:
BlackRock’s use of Aladdin Climate software ensures portfolios meet ESG benchmarks without compromising returns. The gradual introduction of such tools avoids alienating traditional investors.

3. Emerging Investment Strategies: Navigating a New Era

Sustainable Investing:
The rise of renewable energy ETFs demonstrates how measured allocations to green portfolios can balance innovation with risk management.
Alternative Data Sources:
Hedge funds like Renaissance Technologies have leveraged satellite imagery for crop yield predictions. However, their success lies in testing alternative data methods on historical datasets before integrating them into live strategies.
Decentralized Finance (DeFi) Opportunities:
Aave offers decentralized lending protocols. Firms that explore DeFi cautiously by using small experimental funds can gain early insights without exposing significant capital.
Hybrid Models:
Morgan Stanley’s AI-supported human advisors showcase the value of combining machine efficiency with human intuition. Gradual integration ensures customers retain trust in the personal touch.

Conclusion: Adapting to the New Normal

The future of fintech and hedge funds lies in measured innovation. Technologies like Agentic AI offer transformative potential, but thoughtful integration is key to sustainable success. Leaders who embrace change with calculated steps will shape the next chapter of financial excellence.

Next steps…
How will you measure your steps in the journey toward innovation? Our approach is process based and responsive. Let’s discuss.

Addendum: Live Links to Examples

Reality Check: Federated Agentic and Decentralized Artificial Intelligence

Joe Skopek · September 12, 2024 ·

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.

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

Joe Skopek · August 30, 2024 ·

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?

Joe Skopek · August 26, 2024 ·

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.