A pilot can look convincing while the data source, integration path, security control, or operating owner is still unresolved. We examine the evidence one use case or enterprise capability needs across data, technology, people, governance, and operations, then show which next decision that evidence supports.

Some of the 500+ brands we've worked with. Our delivery runs on 100+ AI workflows in production.

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The 5 offerings assess one use case, the supporting data and architecture, or the organization as a whole. Each turns readiness evidence into a defined next decision.

Decide & Govern

Use-Case-Specific AIReadiness

Before a use case earns more budget, someone has to check what it actually needs. We examine one named case against its data, technology, people, governance, and operating conditions. You leave with a proceed, prepare, or stop recommendation and the gaps that deserve attention first.

Use the narrow readiness review when one named case needs a proceed, prepare, or stop recommendation across data, technology, people, governance, and operations.

Data Readinessfor AI

We follow the data for one AI task from its sources and access rules through quality, critical slices, lineage, and upkeep. Your qualified legal reviewer receives the available usage-right evidence. Your data owner decides whether the remaining gaps meet the acceptance conditions.

Data readiness fits when one AI task's sources, usage-right evidence, critical-slice quality, lineage, and maintenance ownership must support the same acceptance call.

Enterprise AIMaturity Assessment

AI activity spreads through a business faster than anyone's view of it. We build the baseline from evidence across strategy, data, technology, governance, people, and operations, so leadership can see where capability holds up, where dependencies drag, and who owns each remediation decision.

Enterprise maturity fits when leadership needs one evidence-backed baseline across six capability domains, with weak areas, dependencies, and remediation owners kept visible.

AI Technology &Architecture Readiness

Build debt starts as an unverified assumption in an architecture diagram. We review the prerequisites, integration boundaries, environments, security controls, and operating conditions behind the proposed system, and hand your architecture owner a blocker map and decision record before the build commits.

Review architecture readiness before build commitments harden, when prerequisites, integration boundaries, environments, security controls, and operating conditions still rest on assumptions.

AI Use-CaseFeasibility Assessment

A model demo answers one question. Whether the use case works as a business decision answers another. We test one specific case across value, data, model behavior, integration, risk, and daily operation, and the evidence lands on one of four calls. Proceed, run an experiment, prepare the missing conditions, or stop.

Feasibility assessment fits a promising use case whose value, data, model behavior, integration, risk, and daily operation need testing before build commitment.

A team can get a promising pilot working before anyone has settled the data source, integration path, security control, or owner for ongoing operation. Finding those dependencies early gives you a smaller problem to solve.

We inspect the evidence available across data, technology, people, governance, and operations. Every blocker stays linked to the use case it constrains, the evidence behind the finding, and the work needed to close it.

The boundary — one use case or an enterprise capability — is agreed before any evidence is gathered.

We agree the use case or capability, its expected outcome, the teams affected, and the person who will make or act on the final decision, then review data, systems, integrations, security controls, skills, governance, and operating practices, marking assumptions and missing evidence as we go rather than after the fact. Each blocker is tied to the use case it affects, the risk of leaving it open, and the owner or prerequisite needed to close it. The assessment ends by laying out the evidence for proceeding, preparing first, redesigning, or stopping, with prioritized actions and the criteria for checking them; the decision itself stays with your owner.

The assessment follows the use case, its evidence, and the people who must act on the result.

Set the boundary
We agree the use case or capability, its expected outcome, the teams affected, and the person who will make or act on the final decision.
Look at the available evidence
Data, systems, integrations, security controls, skills, governance, and operating practices are reviewed, with assumptions and missing evidence marked as we go.
Connect gaps to consequences
Each blocker is tied to the use case it affects, the risk of leaving it open, and the owner or prerequisite needed to address it.
Frame what happens next
We lay out the evidence for proceeding, preparing first, redesigning, or stopping, alongside prioritized actions and the criteria for checking them.

Every operational consultant at Zeo has secure LLM access and training, and AI sits inside the daily work. Five of them came through our AI Bootcamp and wrote down what they expect it to change.

I see generative AI having an enormous effect on daily life and on every industry it touches. As the technology develops, the range of uses will keep widening across creativity, problem-solving, and innovation. We can already see that range in realistic image, video, and music production, pharmaceutical research, and design. I expect the effect on industries to become profound. E-commerce, healthcare, finance, and many other sectors will be able to create more engaging, personalized experiences and make their processes more efficient.

The ability to produce unique content and solutions will open new possibilities and increase efficiency.

Ozan Ketenci

Generative AI has the potential to transform SEO, digital marketing, and many other sectors. I expect it to play an important role in our lives in the near future, with more personal experiences, more effective marketing, faster interpretation of data, and quicker action. Products and services will improve. Processes such as customer communication will become more efficient, and organizations that fail to keep up will fall behind businesses that bring AI into their work.

Organizations should start planning the AI applications that make sense for their sector now.

Samet Özsüleyman

We may be at a moment as significant as the computer revolution, with the potential to transform businesses and industries. Yet for many people, generative AI still means opening a tool such as ChatGPT for a task at work or in daily life. That is only the surface. Companies that integrate generative AI models into workflows and customer processes, and go beyond content production, will gain huge competitive advantages in the coming years.

I believe generative AI should be on the agenda of every board of directors as soon as possible.

Hande Parmaksız

I see artificial intelligence as the most exciting technology of both the present and the future. Its potential is unlimited, and we're still at the tip of the iceberg. AI is developing quickly, while much of what it could mean for different sectors remains unexplored. The effect on digital work is already substantial. In the years ahead, I expect breakthroughs that change how entire industries work.

AI's potential will keep expanding. No sector can afford to ignore the opportunity for efficiency and progress. We will keep discovering new dimensions, and I don't see a saturation point.

Can Mutioğlu

Work by major technology companies is likely to give generative AI a much wider role in the years ahead. It will create new dynamics in art and design, as well as in sensitive fields such as healthcare and finance. As the technology becomes part of daily life, the ethical and risk questions will grow with it. Being able to follow and experience those developments up close is what makes generative AI so exciting to me.

I look forward to seeing more uses of generative AI that benefit society.

Ezgi Gülsen Yaylı

Three speakers look at the pace of AI change and what it means for e-commerce and content teams.

The Future Will Come Faster Than You Imagined

Yiğit Konur, Wope

Effective Use of AI for E-Commerce Companies

Burak Pehlivan, Wope

Producing Content Compatible with Google Algorithms with AI

Mert Erkal, Stradiji

Agents, chatbots, and RAG systems at Zeo are built by senior engineers who keep operating them after launch. The consultants below are those builders, matched to the work this page covers.