Acceldata unveils London speaker line-up for AI event

Acceldata has announced the speaker line-up for its Autonomous London conference, the first European edition of the company's Autonomous series.

The event will focus on enterprise artificial intelligence, with sessions on data sovereignty, data management and business transformation. Speakers come from banking, pharmaceuticals, technology and research organisations.

Among those due to appear are Acceldata Chief Executive Officer Rohit Chaudhury and Chief Technology Officer Ashwin Rajeeva. The programme also includes representatives from HSBC, Pfizer, IBM, Lloyds Banking Group and Merck, alongside speakers from Oxford University, the Tony Blair Institute, University College London and ISG Research.

The line-up reflects a growing debate among large organisations over how to deploy AI systems while meeting stricter data residency and sovereignty rules. The issue has become more prominent in Europe as companies weigh the cost and operational impact of moving information across borders or between platforms.

European focus

Autonomous London follows two previous editions of the conference in San Francisco. Staging the event in London places greater emphasis on European concerns around regulation, governance and the practical limits of data movement in AI projects.

The agenda suggests financial services and life sciences will be central to those discussions. HSBC's Sonu Sachdeva is set to speak on building AI payments platforms, while Pfizer's Michalis Chatziparaskevas will address preparation for agentic data management. Merck's Zdenek Duchek is due to discuss operational data confidence and trusted AI.

IBM speakers Faizan Mohammad and Andrew Tollington are scheduled to cover data modernisation and sovereignty. Clare Schramm Fergus of Lloyds Banking Group and Yang Li of Cosine are expected to discuss business transformation initiatives, while Laura Gilbert of Oxford University and the Tony Blair Institute and David Barber of University College London will examine sovereign AI.

Matt Aslett of ISG Research is also listed to present research on AI, data quality and observability. Together, those themes point to a wider concern among enterprise technology teams over whether existing data estates are reliable enough to support more autonomous AI systems.

Data pressures

Businesses have spent years consolidating and modernising data platforms, but the rise of AI has added fresh pressure. Many now need to support more frequent data access, tighter governance and stronger audit trails while rolling out AI tools across multiple business units.

The challenge is particularly acute for large multinational groups with hybrid systems spread across on-premises and cloud environments. In sectors such as banking and healthcare, companies often have to balance technical efficiency against compliance obligations and internal risk controls.

The event will examine how enterprises are working with hybrid and distributed data without always relocating it. Acceldata argues that moving data less often is becoming a practical requirement for organisations trying to apply AI at scale while staying within local regulatory boundaries.

Mahesh Kumar, Chief Marketing Officer at Acceldata, commented on the theme of the event.

"Agentic AI has changed the physics of enterprise data. Agents don't reach for data occasionally the way people do; they access it constantly, at machine speed, and that shifts the economics of AI in fundamental ways. Architectures that meter every access or require data to move can't sustain it. This event brings together the world's leading enterprises solving exactly that, running AI on hybrid and distributed data without always relocating it. Acceldata supports some of the world's largest hybrid data deployments, helping companies activate AI on the data they already have," said Kumar.

The speaker roster suggests Acceldata is trying to position the conference as a forum for user organisations rather than just a corporate showcase. Including banks, drugmakers, academic researchers and industry analysts broadens the discussion beyond software architecture to policy, governance and operational change.

It also underlines how concerns about trust in AI now extend well beyond model performance. For many enterprises, the immediate questions are whether data is dependable, whether it can remain in the right jurisdiction, and whether internal teams can manage increasingly complex data environments without adding cost and risk.

Keeping data in place is becoming a precondition for deploying AI.