November 25-27

Governments are among the world’s largest purchasers of technology and innovation, yet many organisations continue to approach them using traditional business-to-business (B2B) sales strategies that rarely succeed.
Drawing on more than three decades of experience spanning diplomacy, government relations, international business development, and AI-supported stakeholder engagement, Alistair Starling presents a practical framework for building successful business-to-government (B2G) relationships across Europe.
The session introduces the European Diplomats methodology for AI-powered commercial diplomacy – a structured approach that combines stakeholder mapping, policy intelligence, relationship architecture, and strategic engagement planning. Participants will learn how AI can accelerate the identification of key decision-makers, analyse complex institutional ecosystems, monitor policy developments, prioritise engagement opportunities, and strengthen long-term relationships with governments rather than relying on transactional sales.
Rather than focusing solely on procurement, the presentation demonstrates how organisations can engage at an earlier stage within policy, regulatory, and innovation ecosystems, allowing them to become trusted partners before procurement processes even begin.
Using practical examples from education, digital transformation, AI adoption, international partnerships, and government engagement across Europe, the session demonstrates how organisations have reduced time to engagement, improved stakeholder targeting, strengthened institutional partnerships, and expanded access to public-sector opportunities.
Attendees will leave with a practical framework, an implementation roadmap, a stakeholder-mapping methodology, and an AI-supported workflow, all of which can be applied immediately within their own organisations.

Telecommunications operators in the Asia-Pacific (APAC) region are beginning to treat artificial intelligence (AI) tokens as billable allowances, much like gigabytes of mobile data or minutes of voice. This session will explore what this emerging “AI token economy” means for telecommunications operators, why the APAC model matters, and how Western operators can adapt the concept for enterprise, sovereign, and regulated AI services.

The APAC market shows how operators can move beyond simply carrying AI traffic and begin packaging, rating, bundling, and billing for access to intelligence. By offering AI usage allowances to consumers, households, developers, small and medium-sized enterprises (SMEs), and larger enterprises, operators are positioning tokens as a recurring unit of consumption. However, this model does not transfer directly to Western markets, where consumers already access AI through global applications, hyperscalers, and device-native assistants. For many operators, the stronger opportunity is likely to be in business-to-business (B2B) services, including private inference, AI factories, graphics processing unit as a service (GPUaaS), sovereign AI platforms, edge AI, and AI model marketplaces.

The presentation will also focus on a critical technical and commercial issue: token visibility. Operators cannot reliably count AI tokens from encrypted network traffic alone; authoritative usage records must come from the AI gateway, model platform, marketplace, or provider settlement layer. This creates a strategic choice: remain a connectivity provider or move into the AI service control plane.

Stephen Douglas will also examine why AI-native key performance indicators (KPIs) are becoming essential. Traditional measures such as latency, throughput, and availability remain important, but the AI experience depends on new metrics, including time to first token, inter-token latency, tokens per second, task completion time, cost per token, tokens per watt, and tool-call latency. These metrics will shape assurance, service-level agreement (SLA) management, and monetisation models.

Attendees will leave with practical insights into three telecommunications AI monetisation models: connectivity-only services, AI assurance, and AI service brokerage. The core message is clear: APAC is making tokens the new gigabyte; Western operators could follow suit or position tokens as the new cloud consumption unit.

In ten years, will a company still exist – or only the version of it that survived AI? AI does not add to what a business does. It rewrites how that business creates value. One person with AI can now do the work of ten, and the bottleneck is no longer skill – it is the organisation. The companies that move first take the market. The ones that wait become the market.

Drawing on his own live company rebuild, Šarūnas Chomentauskas walks attendees through six principles that define an AI-native operating model, including integrated delivery that lets one person own end-to-end work; specialist functions as horizontal slices, not silos; and AI as the new professional service. Each principle is grounded in what worked, what broke, and what it cost. Attendees leave with a blueprint for the next decade – and a clearer view of which side of the line their company is on.

For thirty years, the web has split marketing into specialist silos, with organic search, paid media, public relations (PR), customer relationship management (CRM), and analytics each running its own team, budget, and dashboard. Artificial intelligence is now welding those silos back into one practice, because search engines, answer engines, ad surfaces, and buyer-facing agents all resolve to a single question about a company: can the machine read, trust, and cite the brand, and do real people feel warmly enough toward it to be quoted approvingly? The answer resolves to the oldest asset in the business: positive sentiment that converts to sales, running under the name marketers used before the web: branding.

In this talk, Marty Weintraub walks attendees through the full board he tracks every day, showing how organic, paid, PR, platforms, and measurement have quietly fused into one integrated discipline, and how the teams that govern brand facts as a shared source of truth will out-earn the federated teams still arguing over which budget should fund the work. Attendees will leave with a working model for running marketing as one connected system built for humans and machines at once.

Attendees will leave able to:

Map the five converging lanes of organic, paid, PR, platforms, and measurement as facets of one integrated brand system.
Build a single source of truth for brand facts so automated systems broadcast a clean, consistent signal.
Read brand-era metrics like share of voice, sentiment, and citation persistence alongside clicks to prove the sentiment-to-sales loop still closes.
Assign clear ownership for brand truth across the organizational chart before the next platform shift forces the decision.
Turn earned coverage and third-party validation into citation infrastructure that models can quote back to buyers.

English is a second language for most tech professionals in Europe. For Malcolm Stewart, English is his native language, but even he has witnessed it deteriorate and morph into something uncanny. In this talk, Malcolm Stewart shares the story of how he got his language back, offering tips and tricks on how to make AI-generated content sound authentic and on-brand.

What happens when an ad does not sit next to the content, but inside the answer itself? In this talk, Kotryna Kurt will break down how large language model (LLM)-powered ads, such as ChatGPT-style ads, change the rules of targeting, creative, and measurement for brands whose advertising strategies have been built around search, social, and display. She will walk through real scenarios of how conversations become inventory, what “intent” looks like in a chat interface, and how brands can show up without seeming intrusive or interruptive.

Dr. Rybachuk draws a direct parallel with pharmaceutical regulation: no drug company could distribute a product affecting brain development in 450 million children without long-term clinical trials. Yet this is precisely what is happening with educational AI today. The talk presents the case for neuroscience-based regulatory frameworks for EdTech, proposes a certification model analogous to pharmaceutical oversight, and offers a practical roadmap for developers and policymakers who want to act before legislation forces them to.

Adopting AI agents is one thing. Building a team of them that actually runs an organization’s work is another. Once an organization makes the leap from conversational AI to autonomous agents, the next question arrives quickly: how does a team move from a single agent doing one task to a coordinated system that handles entire functions of a business, end-to-end?
This hands-on session addresses that question. Drawing on years of building APG Media as an AI-first agency – with a library of custom skills, Model Context Protocol (MCP) servers connecting agents to live tools, and orchestration loops running across more than 70 clients – Aurimas Paulius Girčys opens up the actual machinery behind a working multi-agent operation. Attendees will see how a single instruction becomes a plan, how that plan is decomposed and delegated to specialized sub-agents, each with its own context, tools, and a narrow job, and where humans stay in the loop and why.
This is not a product demo. It is the architecture, the patterns, and the hard-won lessons of someone who builds these systems for a living – including what breaks, what is worth automating, and what absolutely is not. Aurimas Paulius Girčys covers skills as reusable building blocks, why a skill library, not a user interface, is the real asset, what MCP actually does to give agents “hands,” the orchestration pattern that separates planning from execution, and how to place approval checkpoints so that teams gain speed without losing control or accountability.
For technical operators, founders, and builders who have already adopted agents and now want to architect a coordinated system rather than a pile of one-off automations, this session is the blueprint for going from a clever automation to infrastructure that a business can run on.

The decisions that look free today can become expensive in six months – and AI accelerates the process at exactly the moment when the bill is quietly written. This talk is about that trade-off. The friction that vibe coding removes used to be where feasibility and constraints were discussed, so implicit decisions are made by the model instead.
In this talk, Alex Ponomarev covers where “free now” turns into “costly later”: blurred guardrails, code shipped without understanding its structure, and verification deferred. He shows how to price that deferred bill up front – by choosing which decisions to log and which to keep reversible. AI does not eliminate technical debt. It issues it faster and bills it on a delay.

Most organizations have given their people artificial intelligence (AI) tools and called it a strategy. The licenses are paid for. The output is generic. The transformation never came. The missing piece is not a better tool. It is organizational voice – the ability to encode who an organization is, how it thinks, and what it stands for directly into every AI assistant its team uses daily.

Stefan Chiriacescu built that system from scratch. The result: an 11× revenue increase, a team that moved from execution to strategy, and a 30-person organization operating with the output of an organization three times its size.

In this session, Stefan Chiriacescu will show attendees the live architecture behind that outcome – how to give AI systems a real organizational identity, how to map intelligence across every department, and how to turn every employee into the most capable version of themselves.

Not a concept. Not a demo. A working system attendees can replicate by Monday.