bigspark

AI in regulated industries isn’t just about moving faster. It’s about making sure that progress can stand up to scrutiny. Yesterday at AI SUMMIT BARCELONA, our CEO Robin Bradley joined Mark Hill, COO at Tenth Revolution Group, and Juan Antonio Buero Viana, Head of AI at BASF, on the Main Stage for “AI in Regulated Industries: Booster or Brake?” The conversation explored the balance between accelerating AI adoption and ensuring the right foundations are in place around governance, compliance, risk and accountability. It was a great discussion around what it takes to move AI beyond experimentation and into responsible, production ready use across the enterprise. A big thank you to Tenth Revolution Group, Mark and Juan for having Robin as part of the conversation, and to everyone who joined us at the Main Stage. Responsible, production ready AI is becoming increasingly important, and these are conversations worth continuing. If you’re at AI SUMMIT BARCELONA, be sure to connect with Robin Bradley while you’re here. #AISummitBarcelona #ResponsibleAI #EnterpriseAI #ArtificialIntelligence #AISB26

Most AI business cases for financial services are built around cost and speed. The complaints handling work we did for a UK retail bank is a reminder that the more durable case is often about people. Handlers were spending around 35 minutes drafting each final response letter, time that mostly went on repetitive formatting rather than the judgement calls the role actually needs. We phased AI in gradually, first simplifying and templating responses, then automating investigation and evidence gathering, and only then assisting with the decision itself, with a person reviewing and approving every recommendation. The letter generation and review cycle dropped by 85%. Employee engagement rose by 40%, because handlers were finally spending their time on the parts of the job that needed a person, with a human still accountable for every customer outcome throughout, exactly what Consumer Duty expects. An AI deployment that customer facing teams actually want to keep using is a stronger signal than any efficiency metric on its own. #ResponsibleAI #AIInFinancialServices #CustomerExperience

Most AI pilots in financial services stall at the same point. A model that works technically still waits months for a compliance or risk sign-off, because the data behind it was never built to satisfy that review in the first place. Every new use case then needs its own clearance process, checked separately against data protection, model risk, and internal policy requirements. The Pilot Trap describes exactly this, technology proven, process unproven. Data built to be safe for AI processing from the outset removes that bottleneck, because compliance requirements are satisfied by the data itself rather than negotiated fresh for every use case. Firms scoping AI pilots for 2027 are better served by fixing the sign-off step now than by running another pilot that proves what everyone already suspected. #DataEngineering #ResponsibleAI #DataGovernance #RiskManagement #DigitalTransformation

A four week AI test is only useful if it produces a real decision at the end, not just a demo that everyone found interesting. Working with a large EU financial institution, we built a machine learning lab using their existing infrastructure to test two genuinely different hypotheses in parallel, whether ML could identify the source of system failures from infrastructure logs, and whether it could flag high risk anomalous user behaviour from proxy logs. Both questions reached a clear answer inside four weeks. One hypothesis was strong enough to justify further investment. The other was shelved, not because it failed, but because the lab showed it was not the highest value place to spend the next quarter's budget. The value of a short test is not the speed. It is having a real decision to make once the results are in. #ResponsibleAI #MachineLearning #AIstrategy #Innovation #FinancialServices #DigitalTransformation

Underwriting and claims models are trained on the risks that showed up in the historical book, which is narrower than the risks the model will actually face. A new product line, an emerging peril, or a claims pattern with no real precedent yet are exactly the situations where a model's judgement matters most, and exactly what its training data represents least. Testing only against historical claims shows how a model would have performed against last year's risk profile. Simulation-based synthetic data extends that test coverage. Because it is generated by modelling underlying behaviours and interactions rather than drawn only from historical claims, it can represent emerging perils, new product exposures, and rare claims patterns that haven't yet built up enough history to test against directly. An underwriting model earns its reliability from the range of claims scenarios it has actually been tested against. #SyntheticData #RiskManagement #ResponsibleAI

bigspark is now live on G-Cloud 15 with PRISM, our AI governance and risk management platform. PRISM helps organisations document AI use cases, connect engineering decisions to regulatory requirements, and manage AI risk from early intent through to technical assessment, with clear ownership at every stage. Built in is Aizle, bigspark's synthetic data capability, so teams can test and validate AI systems without using real customer data. If your organisation is trying to move on AI without losing sight of governance, search "PRISM" on the Digital Marketplace, or get in touch with our team: https://lnkd.in/eHyZwCXM #AIGovernance #GCloud15 #DigitalMarketplace #RiskManagement #PublicSector

Complaints handling is one of the clearest examples of why scaling AI into customer facing decisions cannot mean removing human judgement from the process. Handlers were spending around 35 minutes crafting each final response letter, a repetitive task that limited time for the complex, value adding parts of the job and introduced inconsistency into decisions that carry real regulatory weight. The fix was not to hand the decision to AI. It was to phase AI in at the points where it adds the most value while keeping a person accountable for the outcome: first simplifying and templating written responses, then automating the investigation and evidence gathering work, and only then assisting with the decision itself, with a human reviewing and approving every recommendation. That phasing is what makes the approach defensible under Consumer Duty and under regulatory scrutiny generally. AI does the labour. A person still owns the decision. #ResponsibleAI #AIinBanking #AIInFinancialServices #AITransformation #RiskManagement

Proud to share that I’ve been recognised as Most Influential CEO 2026: Responsible AI Leadership UK by CEO Monthly. It’s a great honour, but for me, this recognition is really a reflection of what we’re building at bigspark. AI is moving incredibly quickly, but the real challenge isn’t simply adopting new technology. It’s making sure AI is implemented responsibly, securely and in a way that creates genuine business value. Over the past 20 years, I’ve had the opportunity to work across AI, data, cloud and emerging technologies. What continues to motivate me is helping organisations move beyond experimentation and turn these technologies into practical, real-world outcomes. A huge thank you to everyone at bigspark, our clients, partners and everyone who has supported us along the way. This recognition belongs to the whole team. Very proud of what we’re achieving together and excited for what comes next. Read the full recognition from CEO Monthly - https://lnkd.in/ewuQD6jr #Leadership #ResponsibleAI #AI #Data #Innovation #CEO #bigspark

The phased AI model we built for complaint handling delivered an 85% reduction in the letter generation and review cycle, around 30 minutes saved per case, and a 40% increase in employee engagement, because handlers were freed from repetitive drafting to focus on the complex judgement calls that actually need a person. None of that came from removing oversight. It came from a fine-tuned GenAI model trained on brand tone, response templates and complaints data, paired with empathy scoring and hallucination checks on every generated letter, continuous monitoring of model performance, and a permanent human review step before any recommendation becomes a decision. Scaling AI into a customer facing, regulator visible process is not a trade-off between speed and control. Done properly, the control is what makes the speed sustainable. #GenerativeAI #ResponsibleAI #AITransformation

Most AI roadmaps get built around the data a team already has. That is a reasonable starting point and also, quietly, the reason so many roadmaps stall at the same point mid-year, the next phase needs data the team does not have and was never planning to build. A roadmap scoped around this year's coverage looks achievable because it is measured against what is already available, not what the next twelve months of use cases will actually require. Expansion into a new product line, a new stress scenario, a new customer segment, each of these needs data the current environment was not built to represent. Discovering that mid-roadmap is expensive. Planning for it upfront is not. This is precisely what synthetic data environments are built to remove as a constraint. Rare, expensive-to-capture, or not-yet-observed scenarios can be modelled directly rather than waited for, so a roadmap can be scoped around what the business actually needs to test next, not just what happens to already exist in a warehouse. The firms writing their 2027 AI roadmap this quarter have an opportunity most will not take, building the data foundation to match the roadmap's ambition rather than its current data estate. #ResponsibleAI #SyntheticData #AIStrategy #DataStrategy #Innovation #DigitalTransformation