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Belgium 20-10-2026 Squad Only Virtual English

Deciding What Runs Where: Cloud, Edge, On-Prem, or Hybrid; How to place workloads based on performance, cost, sovereignty, resilience, and AI requirements.

Cloud decisions are becoming more selective. The challenge is deciding where each workload should run based on cost, performance, control, resilience, sovereignty, and business need. Three pressure points usually drive the decision. - Cost visibility becomes harder when usage grows across cloud services, data platforms, and AI workloads. - Performance and latency matter more when systems need fast access to data or support operational processes. - Sovereignty and resilience become critical when data location, continuity, vendor dependency, and exit options enter the discussion. The working question is simple: how do we place workloads where they create the most value while keeping the hybrid landscape manageable? If these trade-offs are on your agenda, let’s compare how others are handling similar choices.

Belgium 10-11-2026 Squad Only Virtual English

Choosing the Right Data Platform Without Creating New Silos; How to balance speed, flexibility, semantics, governance, and long-term maintainability.

Data platforms are meant to simplify access and support better use of data. The challenge is choosing and governing the platform landscape in a way that creates consistency, reuse, and long-term value. Three pressure points usually appear. - Platform choice becomes difficult when teams have different needs for speed, flexibility, cost, and control. - Business meaning needs protection: customer, revenue, product, risk, and FTE need shared definitions across tools. - Ownership and governance need to be clear, so platforms grow with accountability and discipline. The working question is simple: how do we give teams the data capabilities they need while keeping the platform landscape understandable, governed, and reusable? If these questions are familiar, let’s compare approaches with others facing the same data landscape.

Belgium 13-11-2026 Squad Only Physical English

Building the Business Case for New IT Capabilities; How to justify investment when value is strategic, shared, or only visible over time.

Many IT capabilities are essential and difficult to fund through a simple project ROI. The challenge is showing why architecture, governance, resilience, cybersecurity maturity, data foundations, and AI readiness deserve investment before the gap becomes expensive. Three pressure points make the case harder. - Shared value means the benefit often sits across several teams, while the budget sits in one place. - Timing creates friction because the cost is immediate while the value appears over time. - Risk translation is needed so business and finance leaders understand the consequence of delaying the investment. The working question is simple: how do we turn foundational IT capabilities into credible investment cases that link cost, risk, business value, and future flexibility? If you are building these cases internally, let’s compare how others are positioning value, risk, and timing.

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Applying AI to help students find a career path they love

Working Eye is a startup headquartered in the UK that has turned traditional careers advice into a personalized AI-driven discovery experience using interactive cognitive technology and multimedia to help every pupil, student and working adult make better career decisions in the ever-evolving world of work.

When and how did you choose your career? Did you ever doubt whether you’d selected the right path?

“I’ve raised four children and guided each of them through subject selection at school, as well as further education, and job selection. Each time I observed the same challenges: they received minimal career guidance in school and there was no single source of knowledge to help them explore career paths that were unfamiliar to me personally,” explains Peter Cayless, Founder and CEO of Working Eye.

Cayless found that the existing guidance available to students was too broad-brushed and it came too late in the subject selection process. Students did not have a way to explore their options independently and thoroughly early on in their journey. In fact, studies have shown that 96% of graduates switch careers by the age of 24 and half of all graduates follow a career path that does not relate to their degree (source: New College of the Humanities [link resides outside of ibm.com]). In both cases, the main reason given was that they had little idea of what was really involved in the job before they took it. This led Cayless to launch Working Eye (link resides outside of ibm.com), an AI-driven career discovery platform that aims to inspire people by helping them discover themselves and all the possibilities for a career they will love.

Working Eye’s career discovery platform (link resides outside of ibm.com) is designed to be a digital coach that facilitates a student’s career exploration process. The assistant poses discovery questions to the student in natural language and then guides them on a highly unique exploration process that helps them identify a potential career path that is tailored to their specific needs, strengths and priorities. As the assistant guides the student through this dialogue process, it presents various pieces of written and video content which help the student better understand the career that is best suited to them and shows them how to get started on that path. All of this is powered by IBM technology. “IBM watsonx Assistant is the backbone of the chat between the user and the platform, while IBM Watson Discovery is the backbone of finding the right material, collating it for the student,” says Alan Joenn, Chief Operating Officer of Working Eye. The platform’s primary source of data is a government website called the National Career Service Database, as well as the video content created by the Working Eye team.

During its initial pilot of the platform, the Working Eye team tested the digital coach with approximately 1,500 students, parents and teachers. The platform received a 100% approval rating in school panels and 48% parental uptake. “We like to refer to Working Eye as a hand to hold throughout your working life. It is a beautiful example of how AI can work for good. AI isn’t necessarily going to take your job. It has the potential to help you get the job you want,” adds Cayless.

100%

approval rating in school panels during initial pilot

48%

parental uptake during initial pilot

“

We like to refer to Working Eye as a hand to hold throughout your working life. It is a beautiful example of how AI can work for good. AI isn’t necessarily going to take your job. It has the potential to help you get the job you want. ”

Peter CaylessFounder and CEOWorking Eye

About Working Eye Ltd.

Technologies

IBM Watson Discovery IBM Watsonx Assistant

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