Riverty is looking for a (Senior) Program & Project Manager Business Operations (m/f/d).
First, a few words about the company. Riverty is a serious player in the fintech market, part of the media giant Bertelsmann. They specialize in flexible payments and debt management across Europe. Imagine thousands of transactions, data points, and customer stories spread across 11 countries. The scale is impressive, and it obviously demands ironclad order.
What pain point is Riverty trying to solve? It’s all too familiar to anyone who’s worked in a large corporation. There are a multitude of projects, especially those focused on “efficiency improvement and AI implementation” (what an irony, isn’t it?). These projects run in different countries, within different teams. Management needs transparency. They need to understand which project will succeed and which will drag the company down. They need to ensure that best practices from Germany aren’t reinvented in Spain. A classic coordination challenge. And to solve it, they want to hire a person. An experienced, expensive individual, with certifications and Jira knowledge. A one-man band who will run to meetings, consolidate reports, send reminders, escalate issues, and draw pretty charts for top management.
Now, let’s imagine for a moment that this role isn’t performed by a human, but by a system. Let’s call it AI PMO – an Artificial Intelligence-powered Program Management Office. Instead of a single employee who physically cannot be in ten places at once and keep all statuses in mind, we create a digital brain for the company’s entire operational activity.
– Wait, – someone from the audience might say. – Are you suggesting replacing an experienced manager with a program? What about stakeholder communication, the empathy that Riverty itself writes about?
– Excellent question, – I’ll reply. – Let’s break it down. We’re not removing people; we’re giving them superpowers. Our AI PMO isn’t a chatbot that asks “How can I help you?”. It’s an analytical hub, working 24/7.
Here’s how it could look in practice.
Step 1: Integration. We connect our AI to all data sources: Jira, Salesforce, financial systems, customer service databases, even corporate chats like Slack or Teams. It doesn’t just collect data; it understands its context. It sees that a task in Jira is ‘stuck’ without movement, while in Slack, the team is discussing a blocking API issue.
Step 2: Analysis and Prioritization. Based on the company’s strategic goals (for example, “reduce average handling time by 15%”), the AI analyzes all current and proposed initiatives. It can automatically evaluate each one across dozens of parameters: potential impact on KPIs, required resources, risks, dependencies on other projects. Instead of lengthy prioritization meetings, management receives a dashboard with a clear ranking: “Here are the top 5 projects that will deliver 80% of the results. And these three – it’s better to put them on hold, as they conflict in resources with more important tasks.”
Step 3: Transparency and Forecasting. The system tracks progress in real-time. But it doesn’t just show “40% complete.” It builds a predictive model. “Attention, the ‘Scoring Optimization’ project in the Polish office is 75% likely to miss its deadline due to a shortage of developers. Recommended action: reallocate one specialist from the ‘UI Update’ project.” This is no longer a report on the past; it’s a navigator for the future. Stakeholder communication? The AI itself generates weekly summaries for each of them, highlighting exactly what’s important for their level – from technical details for a team lead to the impact on EBITDA for the CFO.
Step 4: Knowledge Sharing. The AI sees that the team in Berlin successfully applied a new A/B-testing methodology and achieved conversion growth. The system automatically creates a “best practice card” and suggests it to the team in Münster, which is just starting a similar project. This no longer depends on whether two managers happened to chat over coffee at a corporate event.
How to overcome distrust? No need for a revolution. Start with the role of an “AI assistant.” Let it first simply prepare reports that people then check. Within a month, everyone will see that it does this faster and more accurately. Then, entrust it with predictive analytics. When its forecasts start coming true, the level of trust will grow. The key is that the AI should always be able to explain its conclusions: “I consider this project risky because: 1, 2, 3.”
How to validate the results? It’s simple. Let’s take two comparable areas of operation. In one, we keep a classic PM. In the second, we implement an AI PMO, where human project managers use it as their main tool. And after six months, we compare metrics: project implementation speed, KPI achievement, budget adherence, and, importantly, the level of stress and burnout among teams who no longer have to spend hours on manual reporting.
Riverty wants to hire a human to manage AI implementation projects. But they could have immediately implemented AI that would manage all projects. This is not replacing humans with machines. This is replacing manual labor and cognitive overload with decision-making based on complete, objective, and intelligent data. And then, humans will be left with the most important things – strategy, creativity, and that very empathy. But not in reports, but in communication with the team.
Источник: https://www.linkedin.com/jobs/view/4348932306/
