IMA | Institute of Management Accountants

3 year term, July 1, 2025 - June 30, 2028

Strategic Planning Commitee/Enterprise Risk Management

The Institute of Management Accountants (IMA) is one of the largest and most respected global associations focused exclusively on advancing the finance and management accounting profession. IMA has more than 140,000 members in more than 160 Countries, spread over 300 chapters and ten global offices.

Member of the Global Board of Directors (Global Director - Non-Executive)

Specific-Group Austria

Managing Partner | Data

Partner | Head of Data Platforms

BAWAG Group

Division Head Data Office & Project Governance

BAWAG P.S.K.

Data Office Division Co-Lead

Head of Data Engineering & RegTech

Head of Data Engineering

IT | Group Data Warehouse | Data Lake | CRM

Head of Big Data, DWH and CRM

Allianz

Strategic Planning & Customer Insights | Market Management
Big Data Initiative | Project Manager

Senior Manager

Deloitte Financial Advisory

Deloitte Financial Advisory | Forensic and Dispute Services

Manager

Senior Consultant

Raiffeisen Zentralbank Österreich AG

Corporate Development | Strategic Controlling

Analyst

Managing Partner | Data — we are hiring! | C-level advisor

Love both legacy systems & the modern data stack and will help you navigate through both with my amazing team.

Helping enterprises build great data products, operate & modernize a solid data landscape. 

CEO of Specific-Group Data Solutions GmbH -- a dedicated Data & AI Engineering team within the 500 people strong and growing Specific Group that operates in 8 countries.

Amonga.com — our low-code API conversion product with great UI

LegacyLift.com — our complete SAS and COBOL legacy code migration product & service using AI workflows and the latest models

!HIRING! -- get in touch for roles in Data Engineering, Data Analysis, Business Analysis, Backend Development, Data Ops

On a personal note: father of two smart boys, private pilot, taekwon-do black belt

There’s no textbook for Data & AI Management that isn’t already outdated. The discipline is written quarterly by the practitioners in the field. 

I had the pleasure to lead the Certified Data & AI Management Officer training course at Hilton Hotels in Vienna  with Business Circle  certified by Austrian Standards. 

We took care to bring together a diverse set of lecturers to share their practitioner insights to a select group of course participants—also practitioners.

Some words of praise and recognition for the lecturers:

—Mario Meir-Huber  is the person to talk to when it comes to data products and it's always a pleasure to work with him
—Shahanaz Müller and Sascha Jung  are two expert Deloitte  partners who are among the top of the top in Austria in their fields
—Pascal Schmieding  is CDO of Santander Österreich  , well known in the community and brings a wealth of experience to everything related data management
—Alin Kalam  is an expert trainer in data analytics and a recognized leader in insurance and banking
—Sabine Gromer  is among the absolute elite CxO executive coaches in the German-speaking countries
—Eevamaija Virtanen  brings globally recognized expertise on the technology and human side of AI & data
—Marvie "Marvs" Demit  is pushing forward innovation with AI governance frameworks
—Rudolf Mayer  is a data analytics and data science expert with vast research and teaching experience
—Chris Tabb  is universally acclaimed as knowing how to show business value in data
—Christoph Praschl  and Damir Trtanj  bring extensive energy sector and leadership experience in large-scale data initiatives
—Ole Olesen-Bagneux needs no intro as one of the leading metadata experts globally and he recorded a data catalogs deep dive for this training course—Last but not least, special thanks to my own Specific-Group Austria  team Dr. Nikolaus Lipusch , Dr. Vadim Savenkov  as well as our GRC lead Mag. Dzevad Mujezinovic, CIPP/E, CISM  for providing their valuable teaching contribution to the group

Thanks to Markus Eigner  and Anna Maria Scherfler  and Business Circle for the organization!

P.S. We also did a separate AI training workshop with the brilliant Eevamaija Virtanen at our Specific-Group Austria office. Thanks to Eeva for making it all the way from Finland to Vienna!

Karl Ivo 🎧 Sokolov

Data & AI Leader | SPG Managing Director | Board Member | Speaker

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"Designing Data Products" covers both the technical and business architecture so well--go get it! But this is also an announcement!

Mario Meir-Huber  and I are working on a cool thing aimed at CIOs, CDOs and data & AI executives. 

We're building a framework that connects existing risk and finance disciplines to the messy reality of valuing, budgeting, and governing AI and data platform investments.

Here are some “meaty” bits of what we're working through, which we believe are novel takes: 

-Why NPV fails for AI projects and what options pricing tells you instead;

-How to upgrade COSO risk frameworks for systems that don't return the same answer twice;

-Why strategic decommissioning funds more innovation than any budget allocation;

-Quantifying the Dual-Run Tax - that cash bleed when you're running legacy and modern systems in parallel;

-AI Debt Amortization: treating model drift and data decay as actual liabilities with depreciation schedules;

-Governance as Derivative Pricing: scaling control overhead to match system volatility and exposure, not applying the same framework everywhere;

-Platform Yield Curve: why PowerBI dashboards pay back in quarters while ML infrastructure takes years, and why you need both;

-The Entropy Index: putting a number on data decay so your CFO understands why master data cleanup isn't negotiable;

-Regulatory Arbitrage in Data Platforms: how smart architecture actually reduces compliance burden;

-Why OODA loops beat agile for AI when you're learning faster than you're building;

The idea: we give CFOs, finance and risk people a way to understand platform decisions AND give data/AI leads a way to speak the language of the board. 

Those are modern twists to existing frameworks that we see to actually work when you're defending real budgets to people who've seen every consultant slide deck…

More soon.

In the meantime - "Designing Data Products" is a must read for data engineers, PMs, and anyone navigating the data products world.

AI infra bubble aside, CFOs know: AI profitability must be viewed as a return on overall business digitization *risk taking*, not a return on *basic chatbots*.

We do chatbots for clients. They are cool and if you don’t have one for the typical use cases by all means get one. But neither at this point nor in the past is this what people meant by saying “transform your business with AI”. 

Look. Removing FTEs with AI is cool but not really that cool (unless you’re in PE and chasing short term wind-down and cashflow optimization in which case at least you’re honest about what you’re doing).

BUT STOP. “Freeing up FTEs’ time for them to upskill and grow” -- we all say this in board decks, but is it real? The actual goal shpuld be:

-improving outcomes for your clients without them feeling you’re cheaping out, 
-growing your business and 
-seeing cashflow and profitability metrics fly 

All of that due to real process redesign that reduces operational variance. And all of that takes *risk taking* and THAT is what it’s about.

So my friend Carly Taylor’s $13,3B ABS point hits different when you realize most of us are seeing people doing chatbots in the field while calling it “AI transformation”.

Happy weekend everyone.

My thoughts on the AI bubble…

AI is not profitable yet. 

Big companies, like Oracle, and small companies, like Lambda, are borrowing money to fund their AI ventures. 

If AI becomes profitable, a lot of this debt will be paid back. But even if AI becomes profitable, some companies will fail, and some will succeed. This is normal. 

However, if AI fails to become profitable, asset backed securities (sound familiar? Think 2008 housing crisis) that are propping up some of these AI companies also fail. As companies default on their loans, these ABS will become devalued and can trigger other defaults. 

About $13.3B in ABS have been created and issued as companies race to build data centers. 

How widespread the effects of default could be depends on how this debt is being traded and who is holding the bag if things go south. 

Making AI profitable should be a key concern for everyone involved. But as we rob Peter (massive layoffs) to pay Paul (fund AI), the idea that consumers will consume enough for AI to be profitable seems pretty far fetched. 

I do believe there is a path forward for AI to become profitable, but too many AI strategies are to layoff employees to fund simple (and, quite frankly, unimaginative) chatbot projects which are NEVER going to move the needle towards profitability. 

Profitability requires novelty and innovation.

taplio

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