Inspired by the article “Deutsche Bank’s seven lead use cases for GenAI” (source behind paywall).
In a recent Risk Live Europe conference Deutsche boasted about the seven exciting GenAI themes either under development or completed. I read them and could not get excited the least. I won’t infringe with the author’s terms and will cover the main areas in my words.
AI should be used to innovate or improve the bank’s position, not just to use for press releases. It’s sad how the current buzzword GenAI can be slapped on every activity stream regardless of actual application of AI. I wonder why the efficiency gains boosting strategy “Buy and configure over build and customise” has not been adopted? The AI marketplace is moving fast and I believe the key activities should be the strategic focus in the public and corporate sectors overall – know what you have, develop data-driven decision making, change the culture by up-skilling people to use the modern tools, upgrade your processes.
Here’s a quick pessimist (a.k.a informed optimist) view of the initiatives:
- A document processing system that sifts, sorts and categorises the content into structured data sets and can run predefined workflows on it. As described here, could be achieved by an OCR/content extraction for paper or otherwise non structured text documents, rules based content searches, and a few workflows – basically an RPA activity and nothing new.
- Email processing – “If we can read that and turn it into data, we can automatically process it, give it to the right person and even actually respond,” said the Deutsche executive. Nothing to do with AI of any sorts. Email has been sorted and forwarded following the set rules using a CRM since… a long time ago… and now can be done by Exchange Online. Why reinvent the wheel and do it as a separate application, is beyond comprehension. Where’s the innovation?
- Excel and million other random apps used in an ad-hoc manner to store client lists is a really poor BCM practice however widely exercised. Fix the underlying cause (lack of common process), not the resulting mess and you have gained your multi-million worth of hidden benefits. Else the mess keeps growing, and so does the bank’s AI team of mechanical turks.
- Another tool is allegedly used to ingest data from public sources as media monitoring /fake news discovery. Either the journalist missed the point, the presenter was blowing out hot air or something is missing here. Being clever, you’ll use a media monitoring service rather than build it oneself. Then you automate your legal workflow to send cease-and-desist notices and manage the cases. Oh, that is also available as SaaS…
- A digital assistant, trained on a specific form (such as 10-K and 10-Q filings made by US companies) and a dataset, has been rolled out. Take it as configured SaaS product and focus your effort on people up-skilling.
- Using digital agents in customer service is probably the most valuable of the lot as, when trained on adequate data and made to respond in a natural way (caveat – in select languages only), can make a meaningful contribution to the support business unit’s bottom line.
- Microsoft Copilot roll-out (personal productivity and coding enhancement) on the other hand is a procedural change (adopting SaaS service for tasks such as direct transcription of Teams calls is a config + cultural change) and not a fintech innovation.
Takeaway? Using different off-the-shelf and open source tools (some labelled as LLM, others as ML-something) to structure your unstructured data, make it discoverable/usable and use no/low-code approach to allow the data to improve the business outcome is definitely a way to go. In total, 1-2 initiatives out of seven may be actually beneficial financially and boost productivity while the others are masking existing poor practices. Some activities are given to remain profitable in the long term, others form part of the learning curve (need to be seen doing something AI related). Is this digitalising the CX and your business? Yes, to an extent. Is this an innovation? Nope, sorry.
Eesti 
English (UK)