Artificial Intelligence (AI)

AI fintech agents: Fifteen examples of the edge being cut.

Velocity Ascent Live · January 21, 2025 ·

These 15 AI agents represent the future of automation, decision-making, and workflow orchestration in fintech.

What if your software could not only read and write — but also learn about your particular business, its history, competitors, challenges, successes – and even the less than successful ventures.
With that knowledge your software will make a range of decisions, handle exceptions, and dynamically coordinate entire workflows while keeping you aware every step of the way.

Nerd Alert – Welcome to the era of AI agents: the successors to rigid RPA (Robotic Process Automation) bots and linear “automation” tools. These very smart, self-directed systems transform industries by bridging the gap between human and machine, enabling real-time financial analysis with complex task orchestration — all with human oversight as needed.

If you’re in fin tech — or almost any digital-heavy industry, — you’ve certainly noticed the shift towards automation and artificial intelligence. The rate at which the AI agent landscape is growing can feel overwhelming.

I jumped into that raging river of AI tools and came out holding 15 handpicked AI fintech agents across finance (obviously) and a mix of general-use categories that are a good starting point for testing the agent waters on what appears to be the future of business operations. These are AI-based tools are bespoke designed to manage your specific workflows, process live and historical data, and make a set of decisions without the need for constant human input.

Note: the following section has been written with a heavy dose of key words to feed those ever-hungry content bots. As always this list in no way implies endorsement – use at your discretion.

Fifteen Fintech AI Platforms:

OpenBB – AI for Financial Analysis
OpenBB is an open-source platform designed for finance professionals to access financial data, perform analysis, and automate reporting. It’s especially valuable for those in the fintech and merchant cash advance sectors, where real-time data analysis and decision-making are crucial. You can integrate it with other financial tools to streamline your operations.
Explore OpenBB
Docisional – AI for Business Decision Support
Docisional helps businesses analyze financial data and make intelligent decisions by using AI-driven insights. It can integrate with CRM systems, financial databases, and even machine learning models to forecast trends and generate action plans. Ideal for fast-paced environments like fintech.
Explore Docisional
Jasper AI – Content Creation and Marketing Automation
In the age of content-driven marketing, Jasper AI can help create copy, blog posts, and even financial reports. For fintech companies that need to produce regular content and communicate with clients, this tool saves time and improves quality.
Explore Jasper AI
CalypsoAI – Risk and Security Management
For businesses involved in financial transactions, such as merchant cash advances, CalypsoAI is an essential tool for risk management and fraud detection. Its AI-driven solutions can help prevent fraudulent activity in real-time while maintaining secure transactions.
Explore CalypsoAI
LangChain – Multimodal AI for Data Retrieval
For businesses managing complex data systems, LangChain simplifies information retrieval through natural language processing. It can help streamline workflows, making it easier to extract and use critical financial data in the fintech sector.
Explore LangChain
Cognigy – AI for Customer Support
Cognigy powers AI-driven customer service chatbots. Its natural language processing capabilities make it perfect for handling customer inquiries in the merchant cash advance space, where fast, responsive communication is essential.
Explore Cognigy
Kahuna Labs – Automation for Financial Data
Kahuna Labs uses AI to automate data handling in the financial services space. With this agent, fintech firms can automate credit scoring, loan processing, and even payment reconciliation, making business operations more efficient.
Explore Kahuna Labs
Fiddler AI – Explainable AI for Financial Services
For financial firms that need transparency and accountability, Fiddler AI helps by making AI decisions interpretable and understandable. This ensures that data-driven decisions in fintech are both effective and compliant.
Explore Fiddler AI
D-ID – AI for Document Verification
In the merchant cash advance industry, document verification is a crucial step in the process. D-ID uses AI to verify identity documents and validate their authenticity quickly and accurately, reducing errors and fraud.
Explore D-ID
Skyflow – Privacy and Data Security AI
For any fintech business handling sensitive customer data, Skyflow provides a privacy-first data platform powered by AI. It helps ensure that financial data remains secure, private, and compliant with industry regulations like GDPR.
Explore Skyflow
Replit – Developer-Friendly AI for Building and Scaling
Finally, Replit allows developers to integrate AI into their software more efficiently. For fintech companies building their own applications or automating internal processes, Replit can accelerate development, integration, and deployment of AI features.
Explore Replit
HoneyHive – AI for Financial Data Insights
HoneyHive is an AI-powered analytics platform that helps fintech companies derive actionable insights from large datasets. It’s particularly useful for uncovering patterns in transaction data and identifying growth opportunities in real-time.
Explore HoneyHive
Prophet – Predictive Analytics for Financial Forecasting
Prophet specializes in predictive analytics and time-series forecasting. It’s a perfect solution for fintech firms looking to forecast cash flows, investment performance, and market trends — helping businesses make proactive, data-backed decisions.
Explore Prophet
Gracker.ai – AI for Financial Risk Assessment
Gracker.ai uses machine learning to provide real-time risk assessments for financial transactions. It helps fintech companies identify potential risks and prevent fraud before it happens, making it a key tool for anyone handling sensitive financial data.
Explore Gracker.ai
Radiant Security – AI for Cybersecurity
As fintech companies deal with large amounts of financial data, Radiant Security offers an AI-driven cybersecurity solution that can detect and mitigate threats in real-time. It’s an essential tool for protecting both your company’s and your clients’ financial information from evolving cyber threats.
Explore Radiant Security

… And there you have it – 15 of the latest collection of AI tools for fintech – just remember that half of these tools are probably obsolete at publish date. 🙂

Embrace the AI-Driven Fintech Future

The fusion of AI and fintech isn’t just a trend—it’s a revolution reshaping how we manage money, streamline operations, and innovate in the financial world. From automating mundane tasks to delivering hyper-personalized financial advice, the fifteen examples we’ve explored showcase the power of AI agents to transform both individual experiences and institutional efficiency.

Whether you’re an entrepreneur, a busy professional, or just curious about how AI is changing the world, these AI agents are built to make your life easier, your workflows smoother, and your financial operations more intelligent. The best part? Many of these tools are open source, meaning you can customize them to meet your specific needs and integrate them into existing systems. With options ranging from task automation to advanced data analysis, these agents adapt to your goals, saving you time and effort while enhancing productivity.

So, where do you go from here? The future of fintech lies in measured innovation—adopting these tools thoughtfully to balance progress with stability. Here are some actionable steps you can take today to join this transformation:

Experiment with Open-Source Tools: Dive into platforms like H2O.ai or LlamaIndex and test how they can automate tasks or analyze data for your personal or business finances.
Stay Informed: Follow fintech innovators on social media (like @stripe or @Klarna) to see real-time updates on how AI agents are evolving and being applied in the industry.
Start Small: Pick one AI agent from the list—say, a chatbot for customer service or a fraud detection tool—and integrate it into your staging workflow. Then measure the impact on your time and bottom line.
Join the Conversation: Share your thoughts or questions about AI in fintech in on social media to discuss how these agents might work for you.

The financial landscape is shifting fast, and AI agents are at the helm. Whether you’re looking to optimize your budget, scale a startup, or simply explore cutting-edge tech, now’s the time to take a step toward this intelligent future. How will you harness these tools to elevate your financial game? Let’s keep the momentum going—your next breakthrough might be just one AI agent away.

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.