Jessica Rusu, chief data, information and intelligence officer at the FCA, recently gave a speech on using AI and tech to deliver the FCA’s strategic priorities.

United Kingdom
Technology

Jessica Rusu, chief data, information and intelligence officer
at the FCA, recently gave a
speech on using AI and tech to deliver the FCA’s strategic
priorities.

The FCA’s strategic priorities are:

• Innovation will help firms attract new customers and serve
  their existing ones better.




• Innovation will help fight financial crime, allowing the FCA
  and firms to be one step ahead of the criminals who seek to disrupt
  markets.




• Innovation will help the FCA to be a smarter regulator,
  improving its processes and allowing it to become more efficient
  and effective. For example, it will stop asking firms for data that
  it does not need.




• Innovation will help support growth.

Industry and innovators, entrepreneurs and explorers want a
practical, pro-growth and proportionate regulatory environment.The
FCA is starting a new supercharged Sandbox in October which is
likely to cover topics such as financial inclusion, financial
wellbeing, and financial crime and fraud.

The FCA has carried out joint surveys with the Bank of England
which found that 75% of firms have already adopted some form of AI.
However, most are using it internally rather than in ways that
could benefit customers and markets. The FCA understands from its
own experience of tech adoption that it’s often internal
processes that are easier to develop. It is testing large language
models to analyse text and deliver efficiencies in its
authorisations and supervisory processes. It wants to respond, make
decisions and raise concerns faster, without compromising
quality.

The FCA’s synthetic data expert group is about to publish
its second report offering industry-led insight into navigating the
use of synthetic data.

Firms have also expressed concerns to the FCA about potentially
ambiguous governance frameworks stopping firms from innovating with
AI. The FCA believes that its existing frameworks, such as the
Senior Managers Regime and the Consumer Duty, give it oversight of
AI in financial services and mean that it does not need new rules.
In fact, it says that avoiding new regulation allows it to remain
nimble and responsive as technology and markets change and its
processes aren’t fast enough to keep up with AI
developments.

The speech follows a
consultation by the FCA on AI live testing, which ended on 13
June 2025. The FCA plans to launch AI Live Testing, as part of the
existingAI
Lab, to support the safe and responsible deployment of AI by
firms and achieve positive outcomes for UK consumers and
markets.

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about your specific circumstances.

AI has captivated industries with promises to redefine efficiency, innovation and decision-making. Some of the nation’s biggest companies, including Microsoft, Meta and Amazon, are projected to pour an astonishing $320 billion into AI by 2025. As remarkable as these developments are, the technology’s swift evolution has exposed some significant challenges. Though these issues aren’t insurmountable, navigating them requires careful consideration and a smart strategy. Take data depletion, for example — one of the more pressing concerns fueled by AI’s rapid rise.

Also Read: The GPU Shortage: How It’s Impacting AI Development and What Comes Next?

AI systems are trained on enormous datasets, but they’re now consuming high-quality, human-generated data faster than it can be created. A shortage of diverse, reliable content could hinder the long-term sustainability of model training. Synthetic data offers one potential solution, but it comes with its own set of risks, including quality degradation and bias reinforcement. Another emerging path is agentic AI, which learns more like humans and adapts in real time without relying solely on static datasets.

Given all the options, high-tech companies’ eagerness to explore these emerging technologies is understandable, but it’s critical to avoid the bandwagon effect when considering new solutions. Before jumping headfirst into the AI race, organizations need to understand not just what’s possible, but what’s sustainable.

Develop a Clear AI Strategy to Pursue Right-Fit Solutions

It’s not just AI but the diverse potential of its applications that has enticed countless companies to jump on board; however, tales of instant success across the AI spectrum of offerings are rare. A baby-steps approach seems to be the rule rather than the exception, as indicated by a recent Deloitte survey that found only 4% of enterprises pursuing AI are actively piloting or implementing agentic AI systems. Organizations that adopt various forms of AI for trendiness rather than intention often find themselves stuck in the trial phase with little to show for their efforts. Scattered approaches lead to wasted resources, siloed projects and negligible ROI.

Businesses that align their initiatives with core objectives are better positioned to unlock AI’s potential. A successful strategy focuses on solving tangible problems, not indulging in alluring technology for appearance’s sake. Comprehensive plans should include solutions that automate routine tasks, such as document processing or repetitive workflows, and tools that enhance decision-making by leveraging advanced data models to predict outcomes.

AI strategies should also embrace technology as a way to strengthen the workforce by augmenting human intelligence rather than replacing it. For example, agentic AI can play a pivotal role in enhancing sales operations as agents can autonomously engage with prospects, answer questions and even close deals — all while collaborating with human colleagues. This human-AI partnership delivers greater efficiency and personalization. Unlike reactive bots, agentic models facilitate meaningful, refined outcomes while retaining emotional intelligence.

Strategies Should Combat Data Depletion and Protect Existing High-Quality Data 

AI’s ravenous appetite for data is raising alarms across industries. Researchers predict the supply of human-generated internet data suitable for training expansive AI models will be exhausted between 2026 and 2032, creating an innovation bottleneck with big potential implications.

AI strategies must recognize that the value lies in the technology’s ability to interpret complex scenarios and conditions. So without the right training data, AI’s outputs are at risk of becoming narrow, biased or obsolete. High-quality, diverse datasets are essential to building reliable models that reflect real-world diversity and nuance.

Amid the looming data drought, synthetic data offers a glimmer of hope. Companies can generate AI data that mirrors real-world situations to potentially offset proprietary content limitations and create task-specific datasets. While promising, synthetic data does come with its own set of drawbacks, such as quality decay, also known as model collapse. Continuously training AI on AI-generated content leads to degraded performance over time, similar to the way photocopying a photocopy repeatedly would erode the original image quality.

Also Read: Why Q-Learning Matters for Robotics and Industrial Automation Executives

Beyond exploring options to generate new data, high-tech businesses must also ensure their strategies prioritize the security of existing datasets. Poor data hygiene, errors and accidental deletions can derail AI operations and lead to costly setbacks. For example, Samsung Securities once issued $100 billion worth of phantom shares due to an input error. By the time the issue was caught, employees had already sold approximately $300 million in nonexistent stock, triggering a major financial and reputational fallout for Samsung.

Protecting data assets means building a sturdy governance framework that includes regular backups, fail-safe protocols and continuous data audits to create an operational safety net. Additionally, investing in advanced cybersecurity mitigates risks like data breaches or external attacks, safeguarding a company’s most valued digital assets.

Preparing for an AI-Driven Future 

The incoming wave of AI success belongs to organizations that blend innovation with intentionality. Businesses that resist hype and take a grounded approach to sustainable transformation stand the best chance of maximizing emerging technology’s potential.

The development of a true, proactive AI strategy hinges on the successful alignment of innovation with clear business objectives and measurable goals. Prioritizing high-quality, diverse datasets ensures accurate, unbiased AI decision-making, while exploring solutions like synthetic data can combat various risks, such as data depletion. AI is reshaping industries with unprecedented momentum. By acting deliberately and ethically, high-tech businesses can turn this technological watershed moment into a long-term competitive advantage.

[To share your insights with us, please write to psen@itechseries.com]

Vidu AI, a generative artificial intelligence video platform developed by Chinese firm ShengShu Technology, today announced an update to its latest Q1 model featuring an advanced “reference-to-video” feature powered by semantic understanding.

The company is developing a generative video AI model that competes with OpenAI’s Sora, which can produce vivid video sequences. The update allows for richer video context for the production of video scenes involving multiple elements that remain the same between clips from frame to frame.

Users can now upload up to seven reference images and include a prompt that combines them for the AI to use in a scene. For example, the AI uses what the company calls “semantic understanding” to reference the images and relate them to the text prompt and even infer missing elements to generate key objects.

“This update breaks through the limits of what creators thought they could do with AI video,” said Chief Executive Luo Yihang. “We’re getting closer to enabling users to create fully realized scenes, complete with a detailed cast of characters, objects, and backgrounds, by expanding multi-image referencing to support up to seven inputs.”

For example, a user could upload an image of a young woman in a green dress, an idyllic forest scene and an owl. Then input the prompt: “The woman plays the violin in the forest while the owl flies down and lands on a nearby branch at sunrise.”

Yihang said the Vidu Q1 semantic core engine will generate a violin in her hands, preserving scene consistency and narrative quality throughout the clip. Using this technology, creators no longer need to face steep technical hurdles when attempting to create complex scenes. A text prompt and images are all they need when producing consistent video scenes.

Vidu is competing with Google LLC’s Veo 3, released in late May. Its generative video capabilities include natural English prompts and reference images alongside a filmmaking tool called Flow, which allows users to manage narrative design to develop entire short AI-generated films that include visuals, special effects and audio, including speech.

ShengShu announced a partnership with Los Angeles-based animation studio Aura Productions in late March to release a 50-episode short film sci-fi anime series fully generated by AI. The project seeks to redefine digital entertainment by using AI capabilities to augment traditional narrative techniques. It is slated for release across major social media platforms this year.

“AI is no longer just a tool; it’s a creative enhancement that allows us to scale production while maintaining artistic integrity,” said D.T. Carpenter, showrunner at Aura, told Variety about the project.

Image: Vidu AI