TechEx Learning Hub EuropeMulti
AI LeadershipDay 1
Enterprise AIDay 1
AI BuildersDay 1
Data & AnalyticsDay 2
Future AIDay 2
AI DeveloperDay 2
Threat Detection, Incident Response & Security OperationsDay 1
Cybersecurity Leadership & Enterprise RiskDay 1
Identity, Zero Trust & Security ArchitectureDay 2
Cloud, AI & The Future of Cyber DefenceDay 2
Green Investment, Digital Innovation, and Physical InfrastructureDay 1
Data Centre Services, Ecosystems & Business ModelsDay 2
Chairpersons welcome and opening remarks.
As organizations invest in intelligent automation, measuring tangible business value becomes critical. This panel will explore methodologies, KPIs, and frameworks to quantify the impact of automation initiatives—from cost savings and efficiency gains to revenue growth and risk reduction. Industry leaders will share real-world examples, discuss challenges in tracking ROI, and offer strategies to demonstrate measurable outcomes to stakeholders. Attendees will leave with actionable insights to assess, justify, and optimize the value of their automation projects.
True intelligent automation is not about bots, it’s about building a digital spine that connects strategy to execution. This session explores the broader relationship between process transformation and digitalisation, highlighting the importance of governance alignment, experience-led design, and change orchestration. Learn how leading organisations ensure automation initiatives reinforce enterprise standards rather than creating complexity.
Shadow AI isn’t a future risk; it’s happening right now. Employees across your organisation are pasting sensitive data into AI tools, generating code with security flaws, and deploying workflows with zero oversight. In this session, we’ll show how Tines gives every team a secure place to build with AI confidently, while security retains the visibility and control to govern it.
As AI moves from experimentation into everyday operations, many organizations are rethinking whether their most valuable automation should live in someone else’s cloud. This talk explores what it means to bring AI back in-house using open-source tools and a practical, engineering-first mindset.
We will look at how teams can build useful internal AI systems around document retrieval, knowledge access, task automation, and assistant-like tools that respect privacy, control, and operational constraints. It touches on Python-based development and on-premise agentic systems, while emphasizing architectural choices, trade-offs, and lessons learned from implementation.
Attendees will leave with a clearer picture of when in-house AI makes sense, what it takes to run it responsibly, and how open-source AI can support production-grade, transparent, and safe automation.
AI is already inside your business. In Turbotic’s UK survey, 88% of organisations report Shadow AI in use, and half name data security and regulatory compliance as their greatest concern. Gartner predicts the average Fortune 500 enterprise will run more than 150,000 AI agents in production by 2028, yet only 13% of organisations feel they have the right governance in place to manage them.
In this session, Henrik Maurin shares what separates AI that runs in operations from AI that stays in pilots. He explains how to keep automation visible, governed and measured, and why the goal should be capability inside your own team rather than long-term dependency on a vendor.
You will leave with a short set of questions to take back to your team, and a clear view of whether your automation strategy has the right guardrails.
See a real-world case study on how agentic AI and intelligent automation drive significant ROI in the implementation and maintenance of ServiceNow. The example can also be applied to many other enterprise software areas.
Key audience takeaways
As organisations embrace intelligent automation, fragmented data and disconnected systems often limit the value they can realise. This panel explores how automation-first approaches can unify operational data into intelligent, AI-driven decision platforms. Industry leaders will discuss integrating enterprise systems, enabling end-to-end automation, creating a single source of operational truth, and using AI-powered insights to improve decision-making, efficiency and business outcomes.
Recently recognized as a Gartner Cool Vendor in Process Intelligence Innovation, Tekst helps Shared Services and GBS leaders fix complex Q2C workflows in a day, not months
Quote-to-Cash AI initiatives routinely fail because organisations jump straight to automation without first understanding how work actually flows — across shared inboxes, CRM, and ERP systems, riddled with exceptions and undocumented variants. Traditional process mining tools compound the problem: they only see structured system logs, missing the unstructured conversations (email, PDFs, spreadsheets) where most Q2C requests originate.
This session shows how Tekst’s process intelligence platform closes that gap in a day, not months. Using a three-step approach — Topic Discovery, Process Mining, and targeted Automation — Tekst mines conversation data alongside system logs to reveal the true end-to-end Q2C process, quantify where time and revenue are lost, and pinpoint high-value automation opportunities with measurable ROI. Real customer examples (including a lead-time reduction from two weeks to 24 hours) illustrate how Shared Services and GBS leaders can move from “we don’t know where the bottlenecks are” to “we’ve fixed the top three” in a single working session.
Industrial automation is entering a new era. Traditional rule-based workflows and static process definitions are being replaced by AI-native orchestration platforms capable of understanding business intent and autonomously generating execution strategies.
This session explores how unified automation platforms are evolving into intelligent control planes that connect enterprise systems, warehouse operations, robotics, and human decision-makers. Attendees will learn how intent-based AI agents collaborate to design workflows, optimize operations, monitor performance, and continuously improve complex automation environments.
The presentation will demonstrate how digital AI companions are becoming the next generation of operational control towers—turning operational data into actionable decisions, enabling self-optimizing workflows, and creating a bridge between human expertise and autonomous systems.
Key themes:
Supply chains are moving beyond task automation toward systems that can sense disruption, interpret business context, decide within policy, and act across operational tools. This presentation clarifies the distinction between semi-automation, automation, agentic systems, and constrained autonomy, with a focus on the decision rights required at each stage. It examines where humans should remain in the loop, arguing that oversight should reflect risk, reversibility, and customer, financial, safety, and compliance impact.
The session also addresses the foundations of trustworthy AI in supply chain operations: grounded data, controlled tool access, explicit decision thresholds, auditability, and recovery paths. It explores the practical challenge of hallucination, showing how approved operational data, business rules, and live system signals can prevent agents from inventing inventory, capacity, policy, or customer commitments.
Finally, the presentation connects autonomy to business value. Participants will learn how to select high-frequency, bounded decisions, establish baselines, test agents in shadow mode, and measure verified ROI before scaling. The goal is a practical 90-day path to trusted, measurable autonomous operations.
As organisations move from deterministic automation to AI-enabled workflows, governance can no longer be treated as a policy exercise that happens after deployment. Decisions, recommendations and automated actions increasingly pass between AI models, workflow platforms, operational teams and human approvers, creating new questions about accountability, escalation and control.
In this practitioner-led session, I will examine how organisations can operationalise responsible AI within enterprise automation. Drawing on experience across high-volume automation, regulated operations, digital-product delivery and AI governance, I will introduce a practical operating model for defining decision boundaries, maintaining meaningful human oversight, managing exceptions and producing evidence that automated systems continue to deliver their intended outcomes.
The session will explore where human intervention should occur, how responsibility should be allocated across product, technology and operational teams, and why conventional automation controls may be insufficient when AI introduces probabilistic decisions. Attendees will leave with a structured framework for scaling AI-enabled workflows while preserving transparency, auditability, operational resilience and accountable human judgement.
