Bull — Our Vision

The New Reality

Why the Old Rules No Longer Apply

The global AI race is reshaping what enterprises need — and who they're willing to pay for it.

Selling tech skill only is a declining market due to the global AI race — Cursor, Claude Code are making 1 senior developer infinitely more productive than a team of 100 developers.

Business people expect to pay less for solutions because everyone can use AI — and it is quite fair.

Generic AI services are rapidly commoditizing — IT freelancers, contractors, layoffs... With AI, every consultant can compete.

SaaS is under scrutiny too — why not "vibe-code" your own tooling rather than paying forever? Only real business impact solutions will prevail.

"It is now more expensive to Adopt & Operate, than to Develop."

David Vopelka

Business AI Cluster Lead

14+ years in Data/AI Consulting
8+ years in the largest CEE bank
Co-Founder of DataSentics

Reference Experience Erste Group, esure, NN, Manchester City, KBC, Moneta

Build vs. Partner

So Should You BUILD Your Own AI Solutions?

Development costs have dropped. But the real cost — Total Cost of Ownership — has shifted dramatically toward adoption and operations.

TCO before

TCO now

Do you really have/buy a team to long-term operate it?

Are you ready to take all this on yourselves? Pay the entire TCO?

Complexity & Risks	Are you ready to handle these?
AI Cost Management (LLM tokens)	People may start using AI heavily — what then? How will usage be monitored, limited, and budgeted? Can costs be forecast and capped as adoption scales?
IT Infrastructure Costs	How do you manage infrastructure costs and avoid over-provisioning? How are spikes detected? What happens cost-wise as users grow (10 → 100 → 1000)?
Product Development Capability	Are you truly experts in building software/user-centric apps? Are you faster than a startup focused only on this? How do you ensure quality, maintainability, and low technical debt?
Adoption & Organizational Support	What if organizational support is weak and 100+ bankers don't adopt it? What if it turns out to be a dead end? How will you drive adoption and training? What is the realistic path in 2–3 years?
Long-Term Support & Sustainability	Years 4–5 uncertainty: will you hire and retain people? What if the system starts breaking? What guarantees that 100+ dependent employees won't lose support? Is there a fallback/transfer plan?

Buy vs. Partner

So Should You BUY AI Solutions?

Buying software is easy. Getting your people to actually use it — that's where the real challenge lies.

Getting value out of AI is via changing the way your people work in your company = EMPLOYEE ADOPTION

Otherwise you just risk wasting money for tools, consulting, IT development...

Are your SaaS vendors responsible for adoption of the AI they sell to you?

Or do they just avoid the responsibility and leave it to you / consulting partners?

Shouldn't you pay them only if adopted?

Why should you pay for implementation & access to "Software" if there is no value?

Why is the entire business risk only on you?

The Dilemma

So What Are Your Options?

You need AI to stay competitive. But every path the market offers comes with painful trade-offs.

Build It Yourself?

Pay the entire TCO — development, infrastructure, operations, support... forever
Lose months (or years) before you see any business value
Carry all the risk — if adoption fails, that's your problem

or

Buy a Box Solution?

Solves only some of your needs — the rest you still have to figure out
Spend heavily on integrators & consultants just to make it work in your environment
Vendor lock-in + no adoption guarantee — you pay regardless of business impact

Both paths cost you time, money, and risk — with no guarantee of real business outcomes.

AI Is Changing the Game