2025 is shaping up to be a defining year in enterprise technology—and according to the newly released Cloudera report titled The Future of Enterprise AI Agents which surveyed a total of 1,484 global IT leaders, autonomous software agents are at the center of this transformation. These “agentic” AI systems—AI tools that can reason, plan, and act independently—are rapidly moving from theory to widespread adoption across industries, signaling a massive shift in how businesses optimize performance, enhance customer experiences, and drive innovation.

Unlike traditional chatbots, which are limited to pre-programmed workflows, agentic AI systems use advanced large language models (LLMs) and natural language processing (NLP) to understand complex inputs and determine the best course of action without human intervention. This isn’t automation as we’ve known it—this is intelligent delegation at enterprise scale.

Adoption Is Accelerating—And Strategic

Cloudera’s survey reveals that 57% of enterprises began implementing AI agents within the last two years, with 21% doing so just in the last year. For most organizations, this isn’t experimental anymore—it’s strategic. A full 83% believe AI agents are critical to maintaining a competitive edge, and 59% fear falling behind if they delay adoption in 2025.

Companies aren’t stopping at pilots. A remarkable 96% of respondents plan to expand their AI agent deployments in the next 12 months, with half aiming for major, organization-wide rollouts.

Real-World Use Cases Are Taking Off

The report highlights three of the most popular applications for agentic AI:

• Performance optimization bots (66%) – These agents dynamically manage IT infrastructure, such as cloud resource allocation and server loads, to improve system performance in real time.

• Security monitoring agents (63%) – Autonomous systems that analyze network activity, detect anomalies, and respond to cyber threats without human oversight.

• Development assistants (62%) – Agents that write, test, and refine code in response to real-time changes—streamlining DevOps workflows.

These aren’t hypothetical scenarios. They’re active deployments in IT departments, customer support, and even marketing. In fact, 78% of enterprises are using AI agents for customer support, 71% for process automation, and 57% for predictive analytics—demonstrating measurable return on investment (ROI) in core business areas.

The Next Step After GenAI

The synergy between agentic AI and generative AI (GenAI) is a major theme in the Cloudera report. GenAI refers to AI that can create original content—like text, code, or images—based on learned patterns. Enterprises that invested in GenAI are now leveraging agentic AI to orchestrate and extend these capabilities.

98% of organizations are either using or planning to use agentic AI to support GenAI efforts, and 81% are using agents to enhance their existing GenAI models—effectively making GenAI more useful, responsive, and embedded within enterprise workflows.

Open Source Is Gaining Ground

A notable shift highlighted in the survey is the rise of open-source large language models. Once seen as trailing behind proprietary solutions, models like Llama, Mistral, and DeepSeek are now competitive—and often preferable. Why? They offer lower costs, greater control, and flexibility.

Unlike closed models that often require usage through a specific cloud or API (creating issues around data sovereignty and vendor lock-in), open models can be self-hosted. This allows enterprises to better align with compliance standards and internal infrastructure, making open-source AI not only powerful—but practical.

Challenges Remain: Integration, Privacy, and Trust

Despite the enthusiasm, deploying agentic AI is not without friction. The report identifies three leading barriers:

• Data privacy concerns (53%)

• Integration with legacy systems (40%)

• High implementation costs (39%)

Enterprises also report significant technical complexity: 37% found integrating AI agents into existing workflows extremely challenging. These systems require strong infrastructure, skilled teams, and robust governance.

Cloudera’s survey respondents emphasized the need to prioritize data quality, improve model transparency, and strengthen internal ethics frameworks to ensure AI agents are trustworthy and effective.

Bias and Ethical AI: A Core Concern

One of the strongest warnings in the report involves algorithmic bias. Because AI models learn from historical data, they risk perpetuating societal inequities if not carefully managed. The survey cites alarming real-world consequences:

• In healthcare, biased models have led to misdiagnoses in underrepresented populations.

• In defense, biased decision-support systems could influence high-stakes military decisions.

51% of IT leaders are seriously concerned about fairness and bias in AI agents. Encouragingly, 80% report strong confidence in their AI agents’ explainability—a sign that transparency is becoming a priority.

Industry Spotlights: Sector-Specific Impact

Cloudera’s survey offers deep insights into how different sectors are deploying agentic AI:

• Finance & Insurance: Fraud detection (56%), risk assessment (44%), and personalized investment advice (38%) are top use cases.

• Manufacturing: Supply chain optimization (48%), process automation (49%), and safety risk monitoring lead the charge.

• Retail & E-Commerce: AI agents are improving price optimization (49%), customer service (50%), and demand forecasting (48%).

• Healthcare: Appointment scheduling (51%) and diagnostic assistance (50%) are making real impact.

• Telecommunications: Customer support (49%) and churn prediction are key focuses, alongside security monitoring.

Recommendations for Enterprises in 2025

To make the most of this moment, Cloudera outlines four key steps:

1. Strengthen your data infrastructure to handle integration, quality, and privacy at scale.

2. Start small, prove value, and scale thoughtfully—beginning with high-ROI use cases like internal support bots.

3. Establish accountability from day one. AI agents make decisions—someone must own them.

4. Upskill your teams to collaborate with AI and adapt to its evolving capabilities.

Conclusion: From Hype to Impact—Agentic AI Is Here

The Cloudera The Future of Enterprise AI Agents report paints a clear picture: agentic AI is no longer a buzzword—it’s a business imperative. In 2025, forward-thinking enterprises are investing in agents not just to automate tasks, but to augment their workforce, enhance decision-making, and gain a competitive edge in real time.

To succeed in this new era, organizations must move beyond experimentation and embrace thoughtful, ethical deployment of AI agents. Those who lead now will not just adapt—they will define the future of intelligent enterprise.

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Banking security has never been more critical. As cyber threats grow in sophistication, banks must stay ahead of attackers who exploit outdated systems and evolving fraud tactics. Traditional security measures struggle to keep pace, making artificial intelligence (AI) an essential tool for risk management.

AI’s role in banking has expanded rapidly, with financial institutions investing in advanced machine learning models to detect fraud, strengthen data privacy, and streamline compliance. The market for AI in banking has seen significant growth and is expected to continue expanding (see Fig. 1). According to the U.S. Department of Treasury, many global banks have already experimented with AI-based systems to enhance security, demonstrating a shift toward technologies that process vast amounts of data, detect hidden patterns, and improve overall resilience.

As we enter Q2 in 2025, AI is poised to play an even greater role in safeguarding financial transactions. The question isn’t whether AI will shape banking security – it’s how effectively banks can use it to outmaneuver emerging threats. Let’s explore AI’s impact on fraud detection, privacy protection, and regulatory compliance.

Figure 1.  The U.S. Artificial Intelligence in banking market size

AI-powered fraud detection

Financial institutions process vast numbers of transactions daily, making it difficult for traditional security tools to identify fraudulent activity before it causes harm. AI-driven fraud detection systems address this challenge by analyzing real-time transaction data, spotting unusual patterns, and comparing them against past behavior.

Generative AI is now adding a new layer of complexity to financial fraud. According to the Wall Street Journal, deepfakes have become a growing concern in banking, making scams harder to detect and increasing fraud-related losses (see Fig. 2). This underscores the double-edged nature of AI – it can be both a weapon for cybercriminals and a powerful tool for fraud prevention.

On the defensive side, AI helps investigators focus on high-risk cases rather than sifting through thousands of false positives. Machine learning models can detect subtle signs of suspicious activity, such as abnormal login attempts, rapid transactions from multiple locations, or device-specific anomalies. These early warnings allow banks to intervene before fraud escalates.

As fraud tactics evolve, so does AI. Banks that invest in deep learning technologies can stay ahead of cybercriminals, reducing financial losses and protecting their reputations. AI-driven fraud detection is no longer just an option – it is becoming a necessity in modern banking security.

Figure 2. Generative AI increasing fraud losses

Protecting customer data and privacy

Data privacy regulations are becoming stricter each year. One of the most recent, the Digital Operational Resilience Act (DORA), went into effect just weeks ago, reflecting growing concerns about cybercriminals targeting sensitive financial data. The rising number of data breaches across industries underscores the urgency of stronger security measures (see Fig. 3).

A single data breach can result in hefty fines and a loss of customer trust. AI can strengthen data security by continuously monitoring how sensitive information is accessed and used within an organization. Instead of relying on manual oversight, AI-powered systems detect unusual behavior in real time, flagging potential threats before they escalate.

Banks can also implement AI-driven risk scoring systems that assess each data request based on factors like user behavior, location, and device type. If a request falls outside normal parameters, the system can trigger an alert or block access until further review. According to an IBM report, financial institutions using AI-powered monitoring tools have reduced response times to privacy threats by nearly a third.

As more customers shift to digital banking, the need for robust data protection has never been greater. AI is helping financial institutions stay ahead of cybercriminals, ensuring compliance with evolving regulations while reinforcing customer confidence in their digital transactions.

Figure 3. Percentage of data breaches by industry

Strengthening compliance and AML efforts

Money laundering has long been a challenge for the banking sector, prompting governments to impose increasingly stringent compliance requirements. Banks must detect illicit transactions that often blend seamlessly with legitimate financial activity. At the same time, the global market for anti-money laundering (AML) systems continues to grow (see Fig. 4).

AI enhances AML efforts by analyzing vast amounts of data faster and more accurately than traditional manual reviews. According to a 2024 EMEA AML Survey by PwC, top financial institutions have reduced compliance costs by up to 15 percent by integrating AI into their AML processes.

AI-powered systems monitor transactions for complex patterns that may indicate money laundering, such as sudden spikes in transaction volume, international transfers with no clear business purpose, and repeated deposits followed by rapid withdrawals. These systems can also cross-reference multiple data sources, including public records and watchlists, to flag individuals or organizations with a history of financial misconduct.

By automating key parts of the compliance process, AI allows financial institutions to focus on high-risk cases rather than getting overwhelmed by false positives. This not only improves regulatory compliance but also reduces the backlog of potential violations, ensuring a more proactive approach to financial security.

Figure 4. Global anti-money laundering market

AI’s broader influence on banking security

Fraud detection, data protection, and compliance are just part of AI’s growing role in financial security. Advanced AI models are transforming nearly every aspect of banking, from customer onboarding to credit scoring. These systems pull data from multiple sources—web platforms, mobile apps, and even social media—to assess risk in near real-time. According to the Global Finance & Banking Review, AI-driven analytics have improved investment predictions by 45 percent.

AI is also helping banks anticipate emerging threats. As cybercriminals develop more sophisticated tactics, AI-powered tools can analyze patterns and predict potential attack methods before they become widespread. This proactive approach reduces last-minute crisis management, allowing banks to implement stronger defenses in advance.

As AI capabilities continue to expand, financial institutions must balance innovation with responsible use. AI offers immense potential for improving security, but its effectiveness depends on thoughtful implementation and ongoing oversight. Banks that embrace AI-driven security strategies will be better positioned to protect their customers, comply with regulations, and maintain trust in an increasingly digital financial landscape.

Final thoughts

AI is reshaping banking security, helping financial institutions protect assets, reduce fraud, and strengthen customer trust. From fraud detection and automated compliance checks to predictive analytics, AI-driven systems are reducing guesswork and enhancing risk management.

In 2025, AI-powered security measures are expected to become standard in leading banks, helping them safeguard sensitive data and meet regulatory demands. When banking organizations implement AI responsibly, AI can not only mitigate risks but also lay the foundation for a more secure and resilient financial industry.

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Steve Lucas, CEO and Chairman of Boomi, is the author of Digital Impact and a multi-time CEO with nearly 30 years of leadership experience in enterprise software. He has held CEO and senior executive roles at some of the world’s leading cloud organizations, including Marketo, iCIMS, Adobe, SAP, Salesforce, and BusinessObjects.

Boomi is a leading provider of cloud-based integration platform as a service (iPaaS), helping organizations connect applications, data, and systems across hybrid IT environments. Its low-code platform enables rapid integration, automation, API management, and data synchronization to support digital transformation and streamline operations for businesses of all sizes.

As a multi-time CEO, how has your leadership approach evolved in the face of AI-driven disruption? What’s different about leading now vs. a decade ago?

Leading today is fundamentally different from even three years ago, let alone a decade. Back then, digital transformation was a strategic advantage. Today, it’s a survival imperative. AI-driven disruption has completely reset expectations around speed, adaptability, and data-driven decision-making. As a CEO, that means I no longer have the luxury of linear planning or incremental improvement. The pace of change, particularly in my industry, demands bold, system-level thinking and execution.

If you’re thinking that AI is just another tool in your stack, you’re wrong. It’s a force multiplier. Or at least it can be if you architect your organization with AI at the center of everything you do. In every discussion with my team, I always ask: “Have we thought about how we can use AI in this initiative?” It’s literally part of every discussion. That’s changed how I lead. I’ve always been hyper-focused on integration, data transparency, and breaking down silos. But now, all of that is in service of making AI better. Leadership is still about aligning teams around goals. But now AI is at the heart of achieving those goals.

Above all, today’s CEOs must be deeply human in how they lead. AI is accelerating everything, and that can worry people. It’s why the human element (our values, our judgment, our empathy) must guide how we deploy it. It’s no longer just about digital transformation. It’s about human transformation.

Your book argues that AI will fail without fixing digital infrastructure. Can you explain what you mean by “digital fragmentation” and why it’s such a critical issue right now?

Digital fragmentation is the silent killer of enterprise AI efforts. Over the last two decades, organizations have raced to digitize their workplaces, adding more systems, apps, clouds, and platforms. But in that rush, few paused to build meaningful integration between them. The result is a tangled web of disconnected technologies and data silos that can’t talk to each other. The sum was less than all of those parts.

Now, AI is forcing companies to finally confront that fragmentation. AI systems require clean, connected, real-time data to function well. But most businesses are trying to scale AI across an unstable data foundation. That’s why, according to industry data, more than 70% of enterprise AI projects fail. It’s not because AI doesn’t work, but because the digital environment around it is too fragmented for it to succeed.

In Digital Impact, I argue that before any leader invests another dollar in AI, they must first fix the foundation. That means creating an integrated, AI-ready architecture that connects systems, harmonizes data, and enables intelligent automation. Otherwise, AI will only amplify the chaos.

In “Digital Impact,” you highlight real-world examples where integrated tech is making a difference — from disaster relief to sustainable farming. What case study surprised or inspired you the most while writing the book?

The example that stuck with me most was the work done during a series of natural disasters to provide rapid emergency relief through integrated systems. In one case, multiple disconnected government and aid organizations had to collaborate in real-time, sharing data on everything from infrastructure damage to the location of vulnerable populations.

Historically, that kind of coordination would’ve taken days if not weeks. But with integrated digital infrastructure and automation, they were able to respond in hours. Emergency supplies were rerouted, housing was secured for displaced families, and aid was delivered with a level of speed and precision that saved lives.

That case showed to me what’s possible when we stop treating integration as an IT problem and start seeing it as a human imperative. Technology is at its best when it disappears into the background and just works seamlessly, intelligently, and in service of real people.

The subtitle of your book references “The Human Element” of AI-driven transformation. How do we ensure people remain at the center of this technological shift?

That’s the most important question of all. In Digital Impact, I argue that the most powerful AI strategy is a human strategy. We’re not building AI for machines. We’re building it to serve people. But it’s easy to lose sight of that in the rush to automate, scale, and optimize.

To keep people at the center, we must design AI systems that enhance human capacity, not replace it. That means creating tools that reduce digital friction, support better decision-making, and free up time for more meaningful human work. It also means being deliberate about transparency, fairness, and ethics when AI makes decisions that affect people’s lives.

Most importantly, we need to equip every employee with the skills, access, and confidence to work alongside AI. It’s about melding the best of human and machine intelligence. This task isn’t relegated to just data scientists or engineers. This is a moment for inclusive transformation, not exclusive innovation. If the human element is overlooked, AI will become just another tech fad. But if we get it right, it can be the most humanizing force in the digital age.

You mention that organizations are building skyscrapers on sand. What are some of the most common architectural mistakes companies make when adopting AI?

The most common mistake is treating AI as a plug-and-play solution rather than an ecosystem evolution. Leaders are often dazzled by the promise of AI and jump straight into implementation without addressing the digital sprawl beneath it. That’s like building a penthouse suite on top of a collapsing building.

One major architectural issue is siloed systems. Most enterprises run dozens, even hundreds, of disconnected applications. Their data is locked in proprietary formats, spread across clouds, departments, and platforms. AI can’t thrive in that environment. It needs clean, consistent, real-time, interconnected data.

Another big mistake is underestimating the importance of integration and automation. Companies implement AI pilots that work in isolation — but they don’t scale because the underlying workflows aren’t automated or integrated across systems. It’s like putting a rocket engine on a bicycle.

Digital Impact lays out what I call “AI-readiness” architecture, which is a set of principles for building modular, connected, secure, and scalable systems. Without that, AI is just window dressing.

Many leaders believe throwing more AI at problems will drive results. What’s the risk in that mindset, and how can your book help reset expectations?

The biggest risk is mistaking activity for progress. More AI doesn’t automatically mean better outcomes if you apply it to broken, fragmented systems. If you don’t fix the underlying process, AI will just amplify the existing flaws. You’ll automate inefficiency, scale bias, and accelerate chaos.

We’ve seen organizations spend millions deploying AI models only to hit a wall because they lacked clean data, integrated workflows, or change management strategies. In Digital Impact, I call this the “shiny object trap.” Leaders chase the latest model or tool, but they forget to ask the most important question: Is our organization ready to use this well?

The book is a wake-up call. It helps reset expectations by grounding AI transformation in business reality. It’s not about how much AI you deploy but how thoughtfully you apply it, how well it integrates with your ecosystem, and how it serves your people.

This is the moment for clarity over hype, architecture over acceleration, and people over platforms.

You’ve said, “SaaS as we know it is dead.” Can you elaborate on what replaces it in an AI-first world — and how agents will transform our interaction with software?

Absolutely. SaaS as we know it – tabs, logins, dashboards, manual workflows – is already on life support. The next era is about intelligent agents: AI-powered copilots that autonomously take actions on your behalf based on the parameters you set and the data you provide.

In an AI-first world, software becomes invisible. You won’t “use” apps in the traditional sense. Instead, you’ll tell agents what you need, and they’ll execute those tasks by accessing apps and systems. Want to onboard a new employee? An agent will spin up the right tickets in IT, provision access, update your HRIS, and send the welcome email – all without a human clicking through five systems. It’s fascinating!

Agents are replacing interfaces. They’re redefining productivity. SaaS isn’t going away, but how we interact with it is fundamentally shifting. The companies recognizing this now will outpace those still optimizing for clicks and dashboards.

Boomi is pioneering AI agents that can work across apps. In practical terms, what kinds of tasks are these agents taking over today — and what’s next?

Our Boomi Enterprise Platform automates time-consuming tasks humans hate, and systems can’t handle alone. It’s the messy middle. Think about syncing customer data between Salesforce and NetSuite, resolving supply chain discrepancies, or validating invoices across finance platforms.

These aren’t flashy use cases. They’re foundational. And that’s the point. We’re not talking about replacing humans. We’re talking about augmenting teams by removing digital friction and connecting data across systems so people can focus on high-impact work.

What’s next? Context-aware agents that don’t just follow rules but learn. Agents that understand business intent and adapt to change. We’re building toward a world where every employee has an AI partner that works across apps, learns preferences, and proactively solves problems before they escalate.

What role do platforms like Boomi play in helping organizations shift from traditional software use to intelligent automation powered by agents?

Boomi is the connective tissue. You can’t deploy agents effectively in a fragmented, disconnected ecosystem. Without integration, automation, and clean data, agents are like brilliant minds stuck in a digital traffic jam.

Boomi clears the road. We unify apps, automate workflows, and expose data in ways agents can actually use. Think of us as the infrastructure layer for agentic AI. We’re plugging into hundreds of systems, enabling automation across them, and delivering real-time intelligence to agents so they can act with context.

We’re not just enabling AI. We’re empowering it to be useful. That’s the difference between cool tech demos and scalable transformation. With Boomi, organizations can make the leap from software as a destination to AI as an action engine.

What inspired you to write this book now, and how do you hope it will change how tech and business leaders think about transformation?

I wrote Digital Impact because we’re standing at a pivotal moment in the history of technology. I believe most leaders are focused on the wrong thing.

Right now, everyone’s talking about AI. But few are talking about how AI actually works in the real world. The truth is you can have the most powerful AI on the planet, but if your systems are fragmented, your data is stale, and your infrastructure is brittle, that AI is useless.

I’ve seen too many digital transformation efforts fail because they ignored the plumbing: the connections, the automation, the data readiness. I wanted to expose that hard truth, but also offer a way forward. This book is a blueprint for how to make AI and transformation actually work, not just theoretically, but practically, system by system, team by team.

Is there a core message or call to action you want every reader of Digital Impact to walk away with?

Yes! Fix the foundation.

We can’t keep building tech empires in digital quicksand. Before you chase the next AI headline, ask: Are our systems connected? Is our data flowing freely? Are our teams aligned around outcomes, not tools?

Digital Impact is a call to return to first principles. Integration. Automation. Human-centered design. These are not “back office” concerns; they’re the front lines of transformation.

The leaders who succeed in this era will be the ones who build infrastructure that’s intelligent, agile, and invisible. My hope is that this book helps more leaders focus on what matters most, so we can all deliver on the promise of AI and create a better digital future for everyone.

Thank you for the great interview, readers who wish to learn more should read Digital Impact  or visit Boomi.

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LLMs are known for their ability to generate code, giving companies virtually unlimited software engineering resources on demand. But what if you could harness that power in real time, letting LLMS write and execute code instantly in production to solve problems as they arise? That’s the bold new reality Riza is building.

Today, the company emerged from stealth with $2.7M in funding, led by Matrix Partners, with participation from 43, to enable LLMs to write and run code autonomously. This advancement allows AI product engineers to enhance their agents’ and workflows’ capability, accuracy, and reliability.

The company plans to use the funding to expand its team, continue its work on untrusted code execution, and develop more tools to make AI code generation more reliable.

How Riza addresses the pain points of software engineers

Former Twilio, Stripe, and Retool engineers Andrew Benton and Kyle Gray founded Riza to tackle a critical challenge in AI and software development. While LLMs can generate code, safely executing it, especially when untrusted or dynamically generated, involves significant risks and complexity. Traditional infrastructure demands human review and a complex setup for secure code execution, slowing development and increasing operational overhead.

Riza’s story began with a Slack message from an old coworker. They faced a challenging problem: safely executing LLM-generated code without compromising infrastructure or getting bogged down by endless human reviews. Benton and Gray — veterans of building developer APIs and plugin systems — created a prototype during a weekend hackathon: a secure, sandboxed WebAssembly runtime that could run untrusted code in isolation. This prototype became Riza’s foundation.

Riza provides an “AI-first infrastructure” that enables developers and AI agents to run code safely and efficiently using a sandboxed WebAssembly (WASM) runtime. This allows LLMs and applications to execute code in multiple languages (such as Python and JavaScript) in isolation, protecting the host environment. The platform is simple to set up, supports multiple languages, and frees developers from managing complex infrastructure or worrying about security vulnerabilities from untrusted code.

Through this approach, Riza enables safe execution of untrusted or LLM-generated code, reduces latency and setup time for code execution in development, CI, and production, and enhances AI agents’ capabilities by allowing them to write and run their tools.

Riza’s “Just-in-Time Programming” 

The Riza team coined “Just-in-Time Programming” to describe this pattern. Running unreviewed AI-generated code in production carries significant security risks, including server compromise and data exfiltration. Adopters of Just-in-Time Programming must either build their safeguards or accept these risks.

Riza’s production-ready environment for safely running untrusted code lets companies securely harness the benefits of Just-in-Time Programming. For instance, one customer generates custom reports combining data from multiple sources. An LLM writes code to fetch, join, and analyse the data, then creates charts embedded directly in the report. While LLMs often struggle with direct data manipulation and analysis tasks, having them write and execute code produces reliable, accurate reports.

Having just announced its platform’s general availability, Riza has gained significant traction among companies implementing Just-in-Time Programming. Their customers generated over 850 million code execution requests in March alone.

“We are in the midst of a generational shift in software where AI and coding agents will become the primary infrastructure users,” says Patrick Malatack, partner at Matrix. “This shift doesn’t eliminate human users but creates new needs that existing solutions can’t meet. At Riza, you have a team of engineers who have built some of the most popular developer APIs in history — applying all that knowledge to build the next generation of compute infrastructure.”

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Skin cancer is reportedly one of the most common cancers globally, with millions diagnosed every year. Despite its prevalence, many healthcare systems are ill-equipped to handle the rising demand for early detection, with dermatologists in short supply. In Europe, there are just 30 dermatologists per million people, which is far too few to meet patient needs. Delays in diagnosis can mean the difference between life and death. 

This is where British healthtech startup comes into the picture with it’s mission to ensure no one dies from skin cancer. 

Funding to fuel global expansion and growth 

The startup has just landes £15 million in Series B funding, led by Intrepid Growth Partners. This funding marks a major milestone following Skin Analytics’ historic achievement of receiving EU MDR Class III CE mark approval, making its AI system, DERM, the first legally authorised AI device in the world capable of making independent clinical decisions for skin cancer diagnosis, without human review.

With this investment, Skin Analytics plans to expand its focus and launch products that cover all dermatology concerns as well as expand internationally to other key markets struggling with dermatologist shortages such as Europe and Australia. It will also accelerate their move into the US market. 

Also, the company is actively recruiting top-tier AI and healthcare talent, using this funding to attract world-class professionals who want to transform cancer detection forever.

AI that thinks like a dermatologist  

Founded in 2012 by Neil Daly, Skin Analytics was born from a simple but powerful idea: technology could save lives if it enabled early and accessible skin cancer detection. After launching teledermatology services in 2015, the company evolved into an AI pioneer, aiming to bring scalable solutions to overburdened health systems.

DERM by Skin Analytics is not just another medical AI. It is trained on a vast real-world dataset accumulated over 12 years. It recognises cancerous lesions, pre-malignant conditions, and common benign lookalikes. Using this database, DERM evaluates moles and skin lesions and makes triage recommendations, identifying which patients need specialist care and which don’t.  

DERM can discharge up to 40% of urgent suspected cancer referrals, allowing dermatologists to focus on the patients who need them most. It has also reduced face-to-face dermatology appointments by up to 95% in NHS settings.

The AI is touted to have a Negative Predictive Value of 99.8%, meaning it almost never misses a cancer that requires urgent attention. That even outperforms the 98.9% accuracy rate of dermatologists in face-to-face consultations. To date, the system has been deployed in 26 NHS sites, assessed over 150,000 patients, and detected more than 14,000 cancers.

What’s next? 

Skin Analytics is not just another healthtech company. It is reshaping how we approach one of the world’s most common and deadly diseases. With rigorous science, regulatory credibility, and now a solid financial boost, the company is uniquely positioned to democratise dermatology, reduce diagnostic delays, and save thousands of lives.  

As AI reshapes medicine, Skin Analytics offers a compelling example of how technology can amplify clinical care, not replace it, bringing timely, accurate skin cancer detection to patients when and where they need it most.

Neil Daly, founder and CEO of Skin Analytics, said: “AI allows us to move from a world of specialist scarcity to one where we have the capacity to see everyone who is concerned about their skin. Starting with skin cancer, this funding allows us to work with our partners to build new models of care that everyone can access, whenever they want to. That brings us closer to the world where no one dies from skin cancer. We have proven this technology in the UK and are now making it available globally.”

Mark Machin, Co-founder & Managing Partner at Intrepid Growth Partners, commented: “At Intrepid Growth Partners, we invest in transformative machine intelligence-driven solutions that address critical challenges, and Skin Analytics exemplifies this vision. Their technology is redefining early skin cancer detection, improving patient outcomes while reducing healthcare costs. We are excited to support their growth as they scale their impact globally.”

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In March 2023, an 18-year-old worker tragically lost his life after falling 22 feet while working on a commercial roof in Missouri. Investigations revealed that the employer failed to provide the required fall protection. Additionally, it was found that minors were illegally employed in roofing work from May 2022 to June 2023. As a result, the company agreed to pay $290,000 in penalties and fines. ​

Key Takeaways for Roofing Professionals:

1. Prioritize Fall Protection: Ensure that appropriate fall protection measures are in place and that all team members are trained in their use.​
2. Adhere to Labor Laws: Comply with labor laws, especially those concerning the employment of minors in hazardous occupations like roofing.​
3. Implement Safety Training: Conduct regular safety training sessions to keep the team informed about best practices and regulatory requirements.​
4. Conduct Regular Safety Audits: Perform routine checks to identify and rectify potential safety hazards on the job site.​

By keeping these points in mind, we can help prevent such incidents and ensure the safety of both our teams and the structures we work on.

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Aviva Investors, a global asset management arm of Aviva plc, has announced a strategic €11.4 million investment into Lakestar’s Growth Fund II. This is it’s latest step towards broadening exposure to high-growth private markets and scaling innovation across Europe. The move demonstrates Aviva’s growing focus on venture and growth capital, particularly in tech-driven sectors poised for long-term disruption.  

This is Aviva Investors’ third venture and growth capital commitment and comes shortly after the recent launch of the Aviva Investors Venture & Growth Capital LTAF (Long Term Asset Fund). The LTAF was designed to address the structural challenge of limited private market access for Defined Contribution (DC) pension schemes and wealth managers.  

Targeting growth: A closer look at the investment 

The investment will be deployed through Lakestar’s Growth Fund II, which backs growth-stage companies across deeptech, healthcare, fintech, and digitalisation. Lakestar’s focus is on identifying transformative companies that are transitioning from early-stage to commercial scale, businesses that require capital and strategic support to expand internationally and solidify category leadership.   

Lakestar: A powerhouse in European venture capital  

Lakestar was founded in 2012 by Klaus Hommels, a key figure in European tech investment and Chair of the NATO Innovation Fund. It has become one of Europe’s most prominent venture and growth investors. The firm has raised over €2 billion to date and has supported more than 210 companies.  

With a hands-on investment approach, deep sector expertise, and global network, Lakestar is known for guiding companies through rapid scaling and navigating complex market dynamics. The Growth Fund II continues this legacy by focusing on companies that blend commercial success with strategic relevance to Europe’s innovation agenda.  

Building the future: Investments in Europe and the UK

Lakestar’s portfolio showcases some of the most innovative and disruptive companies across Europe and the UK, reflecting its strategic focus on long-term, high-impact technologies. Here are a few standout names:

Neko Health: A Swedish healthtech startup offering AI-powered full-body scanning for early disease detection.  

Isar Aerospace: A Munich-based space tech company developing low-cost, sustainable satellite launch solutions.  

Revolut: A UK-based fintech unicorn revolutionising banking and personal finance through a fully digital platform.  

Glovo: A Spanish on-demand delivery platform connecting users with restaurants, groceries, and courier services in minutes.  

GetYourGuide: A Berlin-based traveltech company offering curated experiences and tours in destinations worldwide.  

Eigen Technologies: A UK AI company using natural language processing to extract insights from complex documents.  

Sennder: A digital freight forwarder modernising logistics through data-driven trucking solutions across Europe.  

These companies represent Lakestar’s commitment to supporting scaleups solving real-world problems in health, finance, logistics, and digital infrastructure, all areas where Europe has strong competitive advantages.

An alignment of vision and value  

The commitment to Lakestar’s Growth Fund II signals a meaningful alignment between Aviva Investors’ long-term capital strategy and Europe’s growing tech ecosystem. With access to differentiated, high-impact investments in health, deep tech, and digital infrastructure, this partnership supports the next generation of tech leaders shaping the future of industries and economies.

As pension and wealth clients increasingly seek alternative assets with both growth potential and purpose, Aviva Investors is well-positioned to bridge the gap between institutional capital and Europe’s most forward-looking innovators.

Ben Luckett, Managing Director, Venture and Strategic Capital, at Aviva Investors, said:   “Lakestar has a rich history and wealth of expertise, discovering and supporting pioneering companies which have grown to be at the forefront of their industries, including Spotify and Revolut. The European technology sector has such vast potential to nurture world-leading ideas from globally-important clusters which can drive future growth. We believe Lakestar’s can give us unrivalled access to these opportunities, supporting portfolio company growth and unlocking long-term investment returns for our clients.”

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In an era where the cost of developing AI is skyrocketing, London-based startup Inephany is carving a disruptive path with its intelligent optimisation platform for neural networks. With a fresh $2.2 million pre-seed round led by Amadeus Capital Partners, and backed by Sure Valley Ventures and AI pioneer Professor Steve Young, the company is poised to transform how Large Language Models (LLMs) and other neural architectures are trained and deployed.

The investment will be channelled into three strategic priorities: growing the core engineering and research team with top-tier talent, advancing the AI optimisation engine to handle more complex and diverse models, and supporting the company’s first wave of enterprise adopters.

Inephany’s answer to the AI compute crisis 

The exponential growth of AI has ushered in an era of unprecedented computational demands. Since 2012, AI computational power has been doubling approximately every 3.4 months, far outpacing Moore’s Law, which predicted a doubling every two years. 

Training advanced models like GPT-4 is estimated to have cost between $60 million and $100 million, and projections for next-generation models suggest expenses could reach up to $1 billion. This rapid increase in computational demand highlights the unsustainable nature of traditional training and optimisation methods.

Inephany addresses this challenge with its innovative AI-driven optimisation system, which intelligently manages the training process in real time. It paves the way for scalable and sustainable AI development, offering at least ten times more cost-effective solution.

The team behind this innovation 

Inephany was founded in 2024 by AI veterans, including Dr. John Torr, a former machine learning engineer at Apple Siri; Hami Bahraynian; and Maurice von Sturm, co-founders of conversational AI startup Wluper. The team has deep AI expertise, spanning speech recognition, dialogue systems, and neural network training. With a bold mission to rethink how models learn and evolve, Inephany aims to enable AI’s more efficient, scalable, and environmentally conscious future.

The founders were collectively frustrated with the inefficiencies of current AI training methods. Having seen firsthand how compute-heavy and unsustainable model development had become, they envisioned a platform that could make training smarter rather than just more powerful, optimising performance through intelligent guidance rather than brute-force scaling.

A revolutionary approach to optimisation

Unlike conventional approaches that rely on extensive trial-and-error, Inephany’s technology enhances sample efficiency, accelerates training timelines, reduces development durations, and improves overall model performance, while significantly cutting compute expenses. 

This system enables neural networks to learn more quickly and with fewer computational resources, unlocking at least 10x cost savings. It reduces not only the energy footprint of training models like GPT-4, but also the time and resources needed to iterate and deploy. 

Although Inephany initially focuses on training-time optimisation for LLMs, its technology is designed to be model-agnostic. The platform has promising applications in other architectures, such as Recurrent Neural Networks (RNNs) for financial forecasting, and Convolutional Neural Networks (CNNs) for computer vision in autonomous vehicles. The company also plans to extend its optimisation capabilities to inference-time compute, enabling end-to-end efficiency gains across the AI lifecycle.

Our thoughts 

Inephany is a deep tech startup with technical depth, commercial relevance, and a sustainable mission. As compute costs soar and AI development becomes more exclusive, its approach democratises access and improves global innovation capacity. With top-tier investors, a stellar founding team, and broad applicability, it’s positioned to become a foundational player in the next wave of AI evolution.

John Torr, CEO at Inephany, said: “We are thrilled to be backed by such experienced investors, and having a seasoned entrepreneur and AI pioneer like Professor Steve Young as our chair is a true privilege. Current approaches to training LLMs and other neural networks are extremely wasteful across multiple dimensions. Our unique solution tackles this inefficiency head-on, with the potential to radically reduce both the cost and time required to train and optimise state-of-the-art models. As we prepare to deliver our first products later this year, we are incredibly excited to embark on the next chapter of our journey—and to help shape the ongoing AI revolution by transforming AI optimisation.”

Amelia Armour, Partner at Amadeus Capital Partners, said: “We very much look forward to backing John, Hami, and Maurice as they tackle key efficiency challenges in current AI training. Their innovative approach to automating and optimising neural network training has the potential to reduce costs by an order of magnitude and accelerate advancements across AI applications. If rolled out at scale, the impact of this on what models can deliver will be very substantial.”

Professor Steve Young said: “As the use of AI spreads ever wider, moving beyond the traditional applications of speech, language and vision into new and diverse areas such as weather prediction, healthcare, drug discovery and materials design, the need for very efficient training of accurate neural models is becoming critical.  The groundbreaking new approach being developed by Inephany marks a step change in neural model training technology and I am delighted to join the team as chair and investor.

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AI agents are attractive because they leverage LLM reasoning to offer autonomous decision-making while interacting with apps and data sources. However, many pilot projects haven’t reached production due to limited visibility into agents’ behaviour. Without the ability to monitor progress, intervene when needed, or securely authenticate agents into existing applications, teams lack sufficient control.

These limitations are particularly critical in regulated industries such as Financial Services and customer-facing applications where compliance, auditability, and trust are non-negotiable. Portia AI solves this problem.

Today, the company secured £4.4 million in funding to help developers build AI agents in production with better human oversight. The funding, led by General Catalyst, will accelerate the development of Portia’s open-source framework and cloud platform for building controllable, production-grade AI agents.

In what has been dubbed “the year of the agent,” Portia AI provides developers with tools to build production-grade AI agents they can predict, control, and authenticate. With this funding round, they’re expanding their developer team and preparing to release several exciting features throughout the year to create more reliable production agents.

Building frameworks for controlled and secure AI Agents

Portia AI was founded by Emma Burrows and Mounir Mouawad, two technology leaders with extensive experience in the fintech and tech industries. In March, the company emerged from stealth as a specialised AI platform that built frameworks for controlled and secure AI agents.

Mounir and Emma met at Stripe in London. As CTO of Stripe UK, Emma built the London engineering team from scratch and developed the e-commerce stack at Charlotte Tilbury. Mounir led several product expansions in EMEA, including bank-as-a-service at Stripe and the launch of Google Pay in 30 markets. Together, they bring high technical standards, stellar execution, and an unwavering focus on customer problems.

“AI engineers with deep expertise painstakingly navigate their way through the challenges of deploying reliable agents in production, but we want to democratise the solutions for all developers,” said Emma Burrows, Portia AI’s co-founder and CTO. Co-founder and CEO Mounir Mouawad adds, “Companies want to build products and processes on agents that feel like transparent, collaborative partners rather than black boxes.”

Portia AI tackles significant challenges in implementing AI agents for practical business functions. Clarifying and auditing agent actions minimises the likelihood of unforeseen behaviour. Human-in-the-loop validations guarantee that essential actions demand explicit approval or input instead of depending solely on the LLM. Detailed, real-time authentication ensures robust security while maintaining user-friendliness. Open-source resources and an accessible catalogue facilitate faster adoption and customisation.

The company’s name draws inspiration from literature. According to the founders, “Portia is our favourite protagonist from Adrian Tchaikovsky’s ‘Children of Time,’ which is also one of our mutual top three sci-fi novels!” The character Portia is part of an evolving spider species in the novel that advances its civilisation through technological and social innovation, representing resilience and connection—qualities the founders want to embody in their AI company.

What makes Portia AI special?

Portia AI stands out in the crowded AI agent landscape by focusing on three core pillars: predictability, control, and authentication. Its open-source framework helps developers and businesses build powerful and autonomous AI agents that are also transparent, steerable, and secure.

Portia AI enables agents to generate explicit, step-by-step plans responding to user prompts. Each step specifies which tools to use, what inputs are needed, and what outputs to expect. Execution agents carry out these plans while tracking the state at every step.

A unique feature of Portia AI is its “clarification” mechanism. Suppose an agent encounters missing information or ambiguous instructions or requires explicit approval (such as for a large financial transaction). In that case, execution can be paused, and structured human input can be requested before proceeding.

Portia AI provides a robust authentication layer, allowing agents to access external tools and APIS securely on behalf of users. This authentication is handled just-in-time, with token refresh, so agents only access what they need when needed—minimising security risks and improving user experience. The platform includes a growing catalogue of pre-integrated tools (e.g., Google, Slack, Zendesk, GitHub), accelerating deployment for common business workflows.

Developers can extend Portia’s capabilities by adding tools or customising agent behaviours. The open-source SDK and cloud dashboard make it accessible for various use cases, from fintech co-pilots to customer support automation. Every plan run is serialised and can be stored, retrieved, and audited.

“By seamlessly integrating AI agents, external tools, and human input – and by tackling challenges like JIT authorisation head-on – we believe Portia AI is paving the way for a new era of intelligent automation,” said Juliet Bailin, Partner at General Catalyst. “It also cannot be understated how brilliantly positioned Emma and Mounir are to build a generational company. Their vision is expansive, and they are well known among developers and business leaders alike for their ingenuity. We are exhilarated to be working alongside them.”

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Founders Fund, the influential venture capital firm co-founded by Peter Thiel, has completed the raise of its third growth fund, closing at $4.6 billion, well above the $3 billion target initially reported in February. This step up from its $3.4 billion predecessor, closed in early 2022, underscores renewed investor confidence and signals that the venture capital market is entering a new bullish phase after a muted 2023.

Strong backing from investors 

The new vehicle, Founders Fund Growth III, is focused on late-stage investments and drew capital from a whopping 270 limited partners (LPs), according to a regulatory filing on Friday with the Securities and Exchange Commission. The oversubscription reflects the firm’s strong reputation and consistent performance, making it a magnet for institutional investors and high-net-worth individuals seeking exposure to high-growth tech companies. 

Notably, a substantial portion of the fund’s capital reportedly came from the firm’s own general partners, including Peter Thiel, Napoleon Ta, and Trae Stephens, demonstrating strong internal conviction.

The fundraising is also a reversal from Founders Fund’s more cautious stance in 2023, when it downsized a new fund amid the venture capital downturn. At the time, deal activity had slowed dramatically due to macroeconomic uncertainty, rising interest rates, and declining tech valuations. This latest raise, however, suggests that the firm believes the market has turned a corner, and that late-stage opportunities, especially in sectors like artificial intelligence and defence tech, are ripe for backing.

Doubling down on defence tech

While many venture firms are pouring their resources into artificial intelligence, Founders Fund is taking a broader view by doubling down on defence technology, a sector it has backed long before it became trendy. Partner Trae Stephens, also a cofounder of Anduril, has been a vocal advocate for investing in national security infrastructure and has even published manifestos defending the ethical rationale for venture funding in this space.

With geopolitical tensions rising and governments increasing defence budgets, defence tech has become a key sector to watch, and Founders Fund is well-positioned as one of its earliest champions. The firm’s ties to defence startups, combined with its proximity to former Trump administration officials and national policy debates, give it a unique advantage in identifying and scaling frontier technologies with military and security applications.

A quiet yet strategic European footprint

Beyond the U.S., Founders Fund has been increasingly active in the UK and European startup ecosystems, which have matured considerably over the past five years. It has backed UK cybersecurity firm Darktrace, AI research powerhouse Stability AI, and blockchain forensics startup Elliptic, among others. These investments reflect the firm’s interest in frontier technologies and its belief that Europe can produce globally competitive companies, especially in security and AI.

The strategic appeal of Europe lies in its strong research institutions, growing pools of technical talent, and increasingly favorable policy environments for innovation. As governments across the continent pour resources into digital sovereignty and defence modernisation, the timing may be ideal for US-based funds to scale their exposure.

Positioned for the next wave

With fresh capital and renewed conviction, Founders Fund is now poised to shape the future of high-growth tech in sectors that go beyond today’s hype cycles. Whether it’s defence, AI, or hard science startups, the firm is known for backing companies that aim to solve problems with long-term, civilisation-scale impact.

This new $4.6 billion fund gives Founders Fund the firepower to not only participate in late-stage rounds but also to set terms, support aggressive expansion, and provide follow-on capital in uncertain markets. It also reinforces its position as one of the few VC firms that can consistently raise mega-funds without compromising on ideological or strategic focus.

As 2025 unfolds, Founders Fund Growth III will be a critical vehicle to watch not just for the startups it supports, but for the signals it sends about where tech, geopolitics, and venture capital are heading next.

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