Data overview

Originally founded by four brothers (of which one, Bob Bankinger, is still alive) 50 years ago, the fictional company BigBank today operates in the US, Germany, UK and Nordics. Their success recipe has been relentless focus on customers, with a name relationship manager assigned to everyone, combined with an internal family-like culture. This has allowed them to attract affluent and profitable customers especially for mortgages and wealth products.

When digital came 20 years ago, BigBank initially wanted to “ride it out” but quickly realised their customers do want to convenience of carrying a bank in their pocket. They bought the necessary digital solutions from vendors and maintained a very lean internal IT department. Good for simplicity but meant they were poor and slow in differentiating with digital services.

One year ago BigBank decided to take a different approach with AI: they wanted to be in the forefront of change and launched “AI Advantage” as large transformation program. Led by Arthur Aiver, a rising star from their UK offices, this program was budgeted hundreds of millions of dollars and tasked with developing solutions like “AI banker” (clone the relationship managers as virtual avatars to enable “24/7 chat with your banker”), MaxBoss (replacing human middle managers with AI agents responsible for performance management and coordination - spearheaded in the US) and AI-assisted Product Development (insourcing software engineering, equipping them with latest vibe coding tools and letting loose on critical products and customer journeys - spearheaded in the UK).

However, one year later the initial buzz is starting to fade. The initiatives are not delivering value while costs are exceeding budgets. Some leaders are asking if they should cancel the whole thing and wait it out, like they did with digital. Others are urging to double down and sort out the mess to emerge as a leader.

Your consulting team was brought in to figure out how to proceed. The fate of BigBank is resting on your shoulders!

Data processing

You conducted 20 interviews over Teams across US, UK, Germany and the Nordics and uploaded the audio transcripts to Skimle’s workspace for automatic transcription. You did the initial “automatic analysis” where Skimle created a first pass of the categories, and are now ready to dig deeper to your data!

Research tasks

Let’s first get an overview of the data. Navigate to the Category view and click on the Identified themes category to read a summary of the findings, see which sub-categories are prominent and access individual quotes. Explore the automatically created issue tree to see which categories are prominent in the data. What are the emerging findings?
One of the people interviewed was Bob Bankinger, the remaining founding brother. Go to Documents and find his interview. Each yellow section is coded to one of the categories. Have we missed any insights from Bob? You can manually paint a paragraph if you want to capture the insight to one of the categories.
Let’s check if there are any differences between the countries next. Go to Visualisations to see different graphs based on your data and metadata. Navigate to the “Metadata” section and check the heatmap of countries vs. Categories. Do you spot a big red flag for the US? Investigate deeper by clicking the cell to see all the quotes of US people on the culture of fear.

Research tasks - after login

Let’s go back to the Category view. What you see was created automatically by Skimle, but might not be the way you want to structure your issue tree. To edit categories, simply click on the tick box next to them and pick an action from below. For example, is there a better way to organise the Priorities for AI deployment? Select the category and click “Recreate structure” to explore alternative logical structures.
Finally, let’s see how the data would answer our key research question. Go to Agentic Analysis and type our research question “Based on the facts and opinions from the people interviewed, what should top management do to get the AI transformation at BigBank back on track?”. Explore options to refine the analytical framework and then wait for Skimle to produce the report! This can be the starting point for your team’s recommendations.

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