Agenda & Speakers

Beat the rush and join us early for complimentary barista-made coffee and breakfast.

Kick off the day with a welcome from your MC and a look at what's ahead across both streams.

Despite years of tooling maturity, the distance between an AI prototype that works and a system engineering teams would confidently put in front of production traffic keeps growing rather than shrinking. This opening keynote names that gap directly and looks at the unglamorous engineering work of closing it.

• How each generation of more capable models introduces new failure modes and scaling challenges before teams have fully hardened infrastructure for the previous generation
• What teams are discovering about technical debt accumulated from shipping AI features on infrastructure that was never designed for agents taking real action
• What "production-ready" actually means for AI systems in 2027, and which engineering practices genuinely transfer from traditional software infrastructure

As AI features move from experimental budget lines to core product infrastructure with real unit economics, this keynote examines the engineering practices separating teams who have brought inference costs under genuine control from those still treating cost as a problem to solve later.

• Understanding the gap between how AI infrastructure costs are typically modelled during planning and how they actually behave once a feature reaches real usage scale
• Practical techniques — caching, model routing, batching, right-sizing model selection to task complexity — that deliver genuine cost reduction versus optimisations that sound good but don't move the number
• How some teams are giving engineers direct visibility into the cost implications of their architectural decisions, rather than treating cost optimisation as an after-the-fact exercise

With new frontier and open-weight models shipping faster than most engineering teams can evaluate them, this panel brings together AI and ML platform leaders to discuss how they are making model selection and infrastructure decisions durable enough to survive the next model release cycle.

• How the constant temptation to swap in the latest, most capable model is creating hidden engineering and evaluation costs that rarely get accounted for
• Practical architectural patterns that let teams change underlying models without rewriting downstream application logic every time
• Under what genuine technical and cost conditions teams find fine-tuning smaller models still outperforms throwing a larger frontier model at the problem

Faced with unsustainable inference costs on a real-time trading desk copilot where every millisecond of added latency had genuine financial consequences, one fintech infrastructure leader rebuilt the model-serving pipeline to cut costs while keeping response times within trading-critical thresholds.

• How the team identified which optimisations could reduce cost without violating the strict latency requirements the trading use case demanded
• How the team built a routing layer that sent simpler queries to smaller, cheaper models while reserving frontier model calls for genuinely complex requests
• How the team validated that cost and latency improvements remained stable during high-volume, high-volatility trading periods, not just steady-state conditions

Recharge with refreshments and structured networking with your peers.

A hands-on, interactive session working through a real enterprise AI scenario as a room. Details announced soon.

Anticipating scrutiny from state insurance regulators and internal compliance over an AI-assisted underwriting system, one insurtech engineering leader built decision provenance and audit logging directly into the system architecture, turning what could have been a compliance liability into a documented strength during examination.

• How the team anticipated the specific documentation regulators would require and built logging architecture around those questions from the outset
• The specific technical approach used to log sufficient detail for audit purposes without introducing unacceptable latency or storage overhead
• How having audit infrastructure built in from day one meant the regulatory examination process was substantially smoother than the team had feared

After a production agent chained a minor tool-call error into a significant downstream data corruption incident, one platform engineering leader rebuilt the agent's permission and validation architecture to contain failures before they could cascade.

• How the team traced the incident back to insufficient validation between chained tool calls, where an early mistake wasn't caught before triggering further automated actions
• The specific validation and rollback checkpoints introduced between agent actions to prevent a single error from propagating through an entire workflow
• How the team redesigned the agent's access model around least-privilege principles, limiting the blast radius of any future error

Financial services, healthcare, and other regulated industries are facing increasingly specific requirements around AI system auditability and explainability. This panel explores how engineering teams are building compliance directly into system architecture rather than bolting it on after the fact.

• Concrete examples of the evidence and documentation regulators expect when reviewing AI systems used in regulated contexts
• Technical patterns for capturing decision provenance, model versioning, and prompt history in a way that satisfies compliance review without crippling system performance
• Honest comparison of the effort and technical debt incurred by teams who built compliance in from day one versus those who added it after a system was already in production

Small-group, discussion-based sessions where you'll work through real enterprise AI challenges with peers in similar roles. Roundtable topics will be announced soon.

Enjoy a complimentary lunch while connecting with fellow attendees.

Put your knowledge to the test in this fast-paced quiz covering real-world trivia, key concepts, and emerging trends. Compete for bragging rights — and a voucher — as the top scorer takes the crown.

Facing growing board-level concern about single-cloud provider dependency for a HIPAA-regulated AI platform, one healthtech infrastructure leader built a pragmatic portability layer that reduced genuine lock-in risk without the cost and disruption of a full multi-cloud rebuild.

• How the team assessed which specific dependencies posed genuine commercial or continuity risk, rather than pursuing full portability for its own sake
• The selective approach taken to make the most business-critical parts of the platform portable, while leaving lower-risk components as-is
• How the team communicated the risk reduction achieved to leadership without needing to claim full multi-cloud parity

Site reliability engineering practices built around deterministic systems and predictable failure modes are meeting AI systems whose behaviour varies run to run. This talk examines what reliability engineering genuinely requires when you can't fully predict what your system will do with a given input.

• How uptime and latency metrics, while still necessary, fail to capture the quality and consistency failures that matter most for AI-driven features
• How leading teams are treating continuous evaluation of model output quality as a core reliability discipline, on par with traditional uptime monitoring
• Which traditional infrastructure and reliability practices genuinely transfer to AI systems, and where practitioners still need to build something new

The demand for engineers who can build and operate production AI infrastructure now outpaces supply. This interactive session brings together engineering leaders to discuss what skills genuinely differentiate strong AI infrastructure talent, and how teams are building capability when the ideal hire rarely exists on the open market.

• What engineering leaders find actually correlates with success building and operating production AI infrastructure, versus credentials that look impressive on paper
• How teams are weighing the cost and time of upskilling existing engineers against the increasingly expensive market for experienced AI infrastructure hires
• Emerging patterns in how leading organizations are organizing roles and responsibilities across ML platform, infrastructure, and application engineering functions

Wrap-up of the day's key takeaways across both streams — and your chance to win some epic prizes.

Unwind with your peers for a couple of drinks on us!

See you at Enterprise AI Global London on 28 October 2027.

Beat the rush and join us early for complimentary barista-made coffee and breakfast.

Kick off the day with a welcome from your MC and a look at what's ahead across both streams.

This keynote explores how enterprises are moving from AI experimentation to disciplined execution, where governance, value and scale matter as much as innovation.

• Why the next phase of enterprise AI is less about proving what is possible and more about proving what works in the real world
• How leaders can align data, technology, risk and business teams to build AI programs that scale with control and confidence
• What executives need to prioritize in 2027 and beyond, from operating models and workforce readiness to measurable outcomes and accountability

This keynote will help leaders decide whether to build, buy or bend an AI tool to fit their needs, and how to make that choice based on value, risk, speed and long-term flexibility.

• What to look for when deciding whether an AI solution can be bent to fit existing workflows, data and governance requirements
• How to weigh cost, capability, control and time-to-value so the decision supports the business rather than the hype
• The common mistakes that lead teams to overbuild, overbuy or force-fit tools that do not match the real use case

This panel will explore how American enterprises are navigating fast-moving AI governance expectations while still pushing for innovation, speed and competitive advantage.

• What the current governance and standards landscape means for enterprise AI programs across risk, compliance, data use and accountability
• How organizations can build controls that are strong enough to manage regulatory pressure without slowing experimentation and delivery
• What practical approaches help enterprises balance innovation with trust, transparency and responsible use at scale

Learn how simplifying a crowded AI tool landscape reduces duplication and confusion, and creates clearer ways of working that made teams faster and more effective.

• How organizations can identify overlapping platforms, duplicated workflows and hidden waste across their AI and automation stack
• The practical steps needed to rationalize tools, streamline ownership and reduce confusion for business and technology teams
• What metrics and operating changes prove the simplification is working, from lower costs to better adoption and clearer accountability

Recharge with refreshments and structured networking with your peers.

A hands-on, interactive session working through a real enterprise AI scenario as a room. Details announced soon.

Learn how organizations can navigate the legal, technical and operational challenges of rolling out AI across multiple countries, and what it takes to keep the program moving without losing control.

• How to handle differing legal and policy requirements across regions, including privacy, data residency and approval processes
• The technical issues that make global rollout harder than expected, from integration and infrastructure to model consistency and support
• How operational alignment across local teams, central leadership and vendors helps programs scale without fragmenting

Hear how one organization tackled unclear AI ownership, inconsistent controls and rising compliance pressure to make enterprise AI safer and more scalable.

• How the team identified the governance gaps blocking AI from moving beyond pilots
• What practical controls, standards and workflows were introduced to keep AI deployment moving without slowing innovation
• The lessons learned for other organizations trying to build trust in AI while managing regulation, privacy and internal risk expectations

This panel will explore how American enterprises are rolling out AI in ways that work for executives, the workforce and customers, while managing change consistently across large, complex organizations.

• How leaders are building executive alignment so AI adoption is tied to business outcomes, risk appetite and long-term strategy
• What employees need to understand, learn and trust for AI to become part of day-to-day work rather than a source of resistance
• How customer-facing organizations are introducing AI in ways that improve experience without eroding confidence, transparency or service quality
• The change management approaches that help enterprises scale adoption across business units, locations and functions without creating confusion or fatigue

Small-group, discussion-based sessions where you'll work through real enterprise AI challenges with peers in similar roles. Roundtable topics will be announced soon.

Enjoy a complimentary lunch while connecting with fellow attendees.

Put your knowledge to the test in this fast-paced quiz covering real-world trivia, key concepts, and emerging trends. Compete for bragging rights — and a voucher — as the top scorer takes the crown.

Explore how leaders can evaluate AI programs honestly, recognize when a use case is underperforming, and make the hard call to pause, simplify or scale down before more time and budget are lost.

• The early warning signs that an AI program is not delivering, from weak adoption and poor data quality to unclear value and rising operational drag
• How to build a practical review framework for deciding whether to fix, pause or stop a program, rather than endlessly funding a weak initiative
• How to reframe scaling down as a strategic decision that frees resources for higher-value AI use cases

This keynote will explore how organizations can train existing teams, attract scarce AI talent and develop the capabilities needed to keep pace with new technology.

• What it takes to find and retain AI talent in a market where demand is high and skills are moving fast
• How leaders can develop internal talent pipelines so workforce capability grows alongside technology adoption
• The role of culture, leadership and career pathways in turning AI from a skills gap into a competitive advantage

This interactive think tank will explore how organizations can reduce fear around AI by addressing employee concerns about replacement and customer resistance to change through trust, communication and practical adoption strategies.

• Why AI anxiety starts with uncertainty, and how leaders can respond to job fears with clarity about roles, skills and the human value that still matters
• What messaging and engagement approaches help customers feel informed rather than overwhelmed when AI changes how products, services or decisions are delivered
• How to create safe, useful first experiences that build confidence for both employees and customers instead of forcing adoption too quickly
• What leaders can learn from organizations that have turned hesitation into momentum by pairing transparency, training and visible early wins

Wrap-up of the day's key takeaways across both streams — and your chance to win some epic prizes.

Unwind with your peers for a couple of drinks on us!

See you at Enterprise AI Global London on 28 October 2027.