AI consulting – Organisational adoption of Artificial Intelligence
AI consulting and training services
Everything you need for AI adoption and organisational integration, all in one place, from use case identification to daily operations.
AI adoption is rarely a single step. Some organisations are still trying to work out where the meaningful use cases lie within their own operations, while others have already implemented the technology, but roles, responsibilities and processes haven't kept pace with the change. The six areas below cover this entire journey, so that whatever level of maturity an organisation is at, we can help with tailored solutions.
AI use case discovery workshop
A one-day workshop in which we identify meaningful AI use cases, evaluate them, and build a working MVP.
AI strategy development
An implementation roadmap, scaling frameworks, and an AI vision aligned to organisational objectives. Everything required to keep the rollout from descending into chaos.
AI governance framework development
Who is responsible for AI, and which decisions do we never delegate to AI? Supporting deliberate, effective operation and use.
AI Information Security Consulting
Establishing frameworks aligned with and auditable under AI Act, NIS2 and ISO 42001. Supporting safe use and minimising the risk of data leakage.
AI leadership and employee training
AI use related and role specific trainings, for business analysts, project managers, Product Owners and Scrum Masters.
Post-AI Organisational Development
AI adoption is not purely a technology project. We help clarify new roles and processes, and support the development of organisational culture.
AI use case discovery workshop
A one-day, hands-on workshop built around the real processes of a chosen area of the business. The aim is that by the end of the day, you'll have a high-level AI process map, a prioritised list of AI use cases, and a live-built MVP demo that can be put into use straight away.
Preparation
- An approximately one-hour preliminary interview with 1-2 representatives from the team.
- Clarifying available AI tools and models
- Providing existing process documentation, where available
A workshop's 4 modules
1. Framework, objectives and common language - clarifying expectations and realistic outcomes with the team.
2. Process Discovery - creating a high-level AI process map, identifying where AI support is realistic and offers a genuine return.
3. AI use case identification and evaluation - building a prioritised AI use case backlog, based on added value.
4. MVP building and demo presentation - live-building an MVP for one selected use case, together with the team.
Results at the end of the workshop:
- High-level process map
- Prioritised AI use case list with evaluations
- A live demo of an MVP solution
- Optional: ready-to-use MVP version
Why does it matter how you go about starting an AI development?
It has become clear that, with the emergence of AI, a range of organisational tasks can now be carried out more efficiently. Whether it's analysing larger datasets, drawing on organisational knowledge assets, automating processes, or supporting decision making, artificial intelligence can quickly and effectively support employees in their day-to-day tasks.
This technology is developing at an astonishing pace, so it's essential that developments of this kind are implemented quickly, efficiently, in line with organisational goals, and not simply driven by individual "wishes". In the domestic market, we see organisations planning AI developments on timelines stretching beyond a year, which, given how fast the environment is changing, poses a serious risk.
Instead of siloed solutions built on assumed rather than real needs, we believe developments should be launched in a way that delivers clear, measurable and rapid results for the organisation, while ensuring proper protection of organisational knowledge assets and data.
What happens after you get in touch with us?
We believe that AI developments can only succeed when aligned with organisational strategy, delivered through rapid releases, and carried out within the appropriate security frameworks. Through our services, we're able to support the entire product lifecycle end-to-end.
1. Free consultation: understanding needs, goals, options
2. Preparing and presenting the proposal
3. Contracting
4. Support in line with jointly defined frameworks
Our Lead Experts
Szilveszter Pálya
Réka Pétercsák
Domonkos Végh
Zsolt Czimbalmos
János Szendi Joó
Leila Varga
Dóra Szabó
Hajnalka Berena-Erdei
AI strategy
To ensure AI developments create real value, it's essential that they're implemented in line with organisational strategy. A deliberate AI strategy also helps map out and validate AI needs, and within this framework, we can help with the following:
AI vision aligned to business objectives
What business decisions, processes or risks do we want to manage better with AI? Where does it make sense to use AI rather than automation?
AI decision-making framework
Who decides on AI adoption and its use, which decisions are never delegated to AI, and who holds what responsibility and authority.
Data strategy
What data is available, where is it accessible, what counts as critical or sensitive data, what data must not go to external AI, and how can consistently improving data quality be ensured?
IT security
Proportionate protection to minimise incidents arising from AI use. What do we allow colleagues to do, and how do we manage shadow AI (the use of AI tools by employees on their own initiative)?
Process priority
Not every area has the same level of "AI maturity". What can be considered quick wins, which are the business critical development directions, and where is further experimentation still needed?
AI Champions
AI is no longer a project, but an ongoing capability. It's important to have people in every area who understand current trends and rapidly evolving capabilities, and who can act as advocates for the use of AI.
Competency and leadership development
Stakeholders need to understand what opportunities AI offers, how to use it effectively, be aware of its limitations, and know how much they can trust the results it generates.
Metrics, OKR and learning mechanism
We link the concrete action plan and roadmap to the identified metrics, or more precisely, to achieving key results. This means the AI strategy answers the what and why, while the OKR serves as a structured learning mechanism to help us learn how, and how well, the solution works.
Change management
AI adoption can easily meet resistance. It's advisable to communicate AI's organisational role and its impact on individual work, specifically highlighting what it may replace, and what it will NOT replace going forward.
Ethics
Giving leaders guidance for situations where AI is "technologically capable" of doing something, but its use is questionable from a business or human perspective.
AI governance framework development
The AI strategy is a one-off planning exercise, it sets out where the organisation is heading and why. Governance, by contrast, is an ongoing operational mechanism, applying to every AI initiative regardless of whether it featured in the original plans. It's not a document but a living system of decision making and accountability, one that still functions even if someone comes up with a brand new AI idea tomorrow.
What does AI governance help with?
- Clarifies who decides on introducing a new AI tool, and what is never delegated to AI
- Reduces the risk of shadow AI, where employees use AI tools on their own initiative, without oversight
- Defines who checks, and who is accountable for, the outcome of AI supported work
- Provides guidance for situations where AI is technologically capable of doing something, but its use is questionable from a business or human perspective.
What are the benefits?
Speed and safety
There's no need to renegotiate every new AI initiative from scratch, the decision making framework is already in place.
Clear responsibilities
Everyone knows who uses, who checks, and who is accountable for AI supported work.
Fewer blind spots for leadership
Leadership has real visibility into what's actually happening with AI use, rather than being confronted with it after the fact.
Auditable foundation
If AI Act or NIS2 compliance, or ISO 42001 certification, is also a goal, this lays the foundations for it.
What tasks does this involve?
- Developing an AI decision making framework, who decides, what is delegated, what isn't
- Defining roles and responsibilities within AI supported workflows
- Introducing and developing IT and data security processes
- Minimising shadow AI, what we allow employees to do on their own initiative
- Building data governance functions and processes
- Developing ethical decision making frameworks
- Supporting the scaling of teams and solutions
- Measuring results, ongoing review
Frequently asked questions:
Why might AI consulting be needed?
International experience and independent surveys show that although more than 88% of organisations use some form of AI based solution, only 5.5% see this deliver measurable, value generating business results. The aim of AI consulting is to ensure the organisation approaches day to day application in a deliberate way, with the right business and organisational readiness.
How does AI consulting differ from IT consulting?
While IT consulting supports technological implementation, in AI consulting we accompany the whole journey, from identifying opportunities through to embedding them in day to day operations. Use case identification, strategy, governance and actual development build on one another, we don't treat them as separate, disconnected projects.
Do we need to know in advance where the AI potential lies in our organisation, or do you help us find that too?
You don't need to know in advance, that's exactly what the AI use case discovery workshop is for. If, on the other hand, you already have a direction you'd like to think through more broadly at organisational level, that's more a matter for AI strategy development.
How long does an AI use case discovery workshop take, and what preparation is needed for it?
It takes one day (8 hours), typically with two experts involved. Beforehand, an approximately one-hour online interview is needed to get to know the team's main processes and challenges, and to clarify what AI licence you're working with, if you already are, since this determines what data we can work with during the day.
Is the AI use case discovery workshop classed as training?
No, in the formal sense the workshop is classed as AI consulting, since the outputs are produced through joint work.
What do we receive at the end of the AI use case workshop?
You'll receive a high-level process map, a prioritised AI use case list with evaluations, and a live-built MVP demo, handed over in a form ready for immediate use if required.
What is the difference between the AI use case discovery workshop and AI strategy development?
The workshop focuses on the concrete processes of a specific area, over the course of one day. AI strategy addresses broader, organisation wide questions, what business objectives AI should serve, what portfolio should be built, and how initiatives should be scaled.
Do we need a full strategic process for every AI initiative?
Not necessarily. If you'd like to see quick results in a specific, well defined area, the use case workshop and a pilot may be enough. The full strategic framework is worthwhile when the organisation wants to introduce AI in a coordinated way, across multiple areas, over the longer term.
What is the difference between AI strategy and AI governance?
Strategy is a planning exercise, it sets out where the organisation is heading and why. Governance, by contrast, is an operational mechanism, applying to every AI initiative regardless of whether it featured in the original plans.
Do we need separate AI governance if we already have an ISO 27001 system in place?
A general information security system does touch on AI specific risks, but it's worth developing this in line with AI Act requirements, and NIS2 requirements where relevant.
When is governance needed, right at the start, or only later?
The sooner an organisation clarifies who decides and who is accountable, the less internal friction there will be once the first AI initiatives get underway. There's no need to wait until it becomes a problem, but it's equally fine for it to be developed based on the experience of an already running pilot.
What if AI related decisions belong to a central or parent company function?
We build the solutions, for example during the workshop, so that the local team can use them without needing to involve central functions. Where an identified AI opportunity could also be relevant at organisational level, we flag it separately so it can be taken forward as a ready made proposal to the central function.
How do the AI consulting services described here connect to one another?
The use case workshop typically identifies the opportunities, strategy sets the direction and helps define the AI portfolio, and governance ensures safe and deliberate operation. This is complemented over the longer term by organisation wide embedding. They can be used individually, but together they form the complete journey, the content of which is always company specific in every case.
