Just the other day, a job posting on LinkedIn caught my eye. Raiffeisen banka a.d. Beograd is looking for a “Product Manager za proizvode i usluge u segmentu fizičkih lica,” or in plain English, a Product Manager for retail client products. And you know, the more I read into it, the more I got a sense of déjà vu.
**About the Company:** Raiffeisen Bank – a name that needs no introduction. An Austrian banking group, a behemoth of the European financial market. Its Serbian division is a large, respected player that, like all giants, is with some creaking turning towards “digital transformation.” They state this directly in the job description, which is commendable.
**What Pain Points They’re Trying to Address:** Let’s be frank. The bank isn’t just looking for a manager. It’s looking for a fighter. Reading between the lines:
* “…participate in the bank’s digital transformation” – this means having to push through legacy systems and conservative thinking.
* “…a highly motivated, dynamic, and assertive individual” – they need someone who won’t just propose, but will push ideas through multiple rounds of approvals.
* “…a ‘Yes, if…’ approach, instead of ‘No, because…'” – this is an HR’s heartfelt plea. They’re tired of internal “blockers” and are looking for someone who can find solutions, not problems.
* A huge part of the job involves analytics, reports (regular and ad hoc), presentations, competitor monitoring, and regulatory oversight.
Essentially, the bank is looking for a Swiss Army knife: an analyst, a strategist, a negotiator, and a bit of a psychotherapist to deal with corporate resistance. This person will have to sift through tons of data, monitor the market 24/7, package it nicely into PowerPoint, and present it to “internal and external decision-making bodies.” It’s important work, but let’s admit – 80% of it consists of routine tasks that people don’t particularly enjoy.
**Now, let’s imagine we solve this problem using AI.**
Instead of searching the market for yet another “assertive and dynamic” hero who will burn out from battling Excel and bureaucracy within a year, we create a system. Let’s call it “AI Product Analyst.” This isn’t a single tool, but a suite of technologies that takes on all the grunt work.
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*Dialogue in a Future Meeting Room:*
**Skeptic (Head of Department):** — So, you’re suggesting replacing the Product Manager position… with a program? Are you serious? Who will interact with people, who will demonstrate “innovation and customer-centricity”?
**Optimist (Me, IT Manager):** — Not replace, but augment. We’re not firing people; we’re giving them superpowers. We’re not hiring a human-function, but a system that will free our best strategists from routine. Allow me to demonstrate.
**Step 1: Create an all-seeing market eye.**
We configure AI agents that continuously scan competitor websites, financial news, central bank reports, and new legislative initiatives. Every day at 9:00 AM, our Head of Product will have a one-page summary on their desk (or rather, on their screen): “Competitor X lowered mortgage rates by 0.2%, Competitor Y launched a new cashback program. The regulator is preparing amendments to Law Z. Recommended actions…” The human no longer needs to spend hours surfing the internet.
**Step 2: Connect AI to internal data.**
The bank has a BI system (ASEBA BI is mentioned in the job description). We connect a language model (like GPT-4 or Claude 3) to it. Now, there’s no need to wait for an analyst to build a report. The manager simply types into the chat: “Show the dynamics of consumer loan issuance in the 25-35 age segment for the last quarter compared to last year. Highlight regions with anomalous growth or decline.” An answer with graphs appears in 30 seconds. That very “ad hoc reporting” that used to take person-days.
**Step 3: From analysis to hypotheses.**
Having fed the AI with market data and our internal metrics, we can ask it strategic questions:
* “Based on current trends and our product line, propose three concepts for a new digital credit product for freelancers. Describe the target audience, key features, risks, and potential profitability.”
* “Analyze the sales funnel for our flagship loan. Where are we losing the most customers? Suggest 5 hypotheses for improving conversion at these stages.”
AI won’t invent a brilliant idea from scratch. But it will analyze thousands of options and present the most promising ones, backed by data. It’s the ideal brainstorming partner, one that doesn’t tire and has no biases.
**Skeptic:** — Sounds like science fiction. It’s a “black box.” How do I know its conclusions can be trusted? And where’s the “assertiveness” and “proactivity” in all this?
**Optimist:** — Excellent question. We mitigate distrust in several ways. Firstly, the AI always cites the sources of its conclusions. “This proposal is based on an analysis of products X, Y, and Z, and data on the behavior of our clients from segment A.” Secondly, we don’t let it make decisions. The decision remains with the human. AI is an advisor with unlimited analytical capabilities. And “proactivity” and “assertiveness” are now needed not to secure budget for another analyst, but to take a ready-made, AI-calculated model and convince the board to implement it. The focus shifts from manual labor to pure strategy and management.
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**How to Validate AI’s Work?**
Trust, but verify – a sacred rule.
1. **Backtesting:** We take historical data from the past year. We give the AI a task: “Model the optimal pricing policy for our credit cards for the past year.” We compare its “ideal” result with what we actually achieved. The difference is our potential growth area.
2. **A/B Tests:** The AI suggested changing loan terms? Excellent. We launch a pilot project for 1% of clients and look at the real numbers. Data, not opinions, will show whether the AI was right.
3. **Expert Review:** Every serious AI recommendation goes through a “sanity check” by an experienced product manager or executive. But now, this expert spends their time not on collecting data in Excel, but on evaluating a ready-made, calculated strategy.
In the end, instead of looking for one superhero who is a jack of all trades, the bank could invest in a system. This system doesn’t burn out, doesn’t go on vacation, and doesn’t ask for a raise. And the remaining team members could finally focus on what they were hired for – thinking, creating, and making strategic decisions, relying on powerful analytical support that was previously only a dream.
And then, future job descriptions will seek not “assertive” individuals, but “curious ones capable of asking AI the right questions.” And that, in my opinion, is a far more interesting transformation.
Источник: https://www.linkedin.com/jobs/view/4404303244/
