Actors as equals
A chat assistant waits until you ask. A genuine human-AI organisation distributes work across clearly defined actors that work together as a team:
- HU-Actor (human): judgement, accountability, leadership, meaning.
- AI-Actor: an AI-based actor that appears outwardly as a single actor; internally it can be one AI agent or an ensemble of several AI agents. Brings speed, scale, research.
- HY-Actor (hybrid): a HU-Actor and an AI-Actor as a duo that appears outwardly as a single actor. The human keeps responsibility; internally the roles stay clearly separated.
HU-Actor and AI-Actor are equal partners. A HY-Actor is a single HU-AI duo, not an automatic result of all of them. "As equals" means: every actor has a clearly defined role, and the collaboration is designed rather than left to chance.
In the market this is often called "building workflows and processes with AI agents". We draw the arc wider: from strategic direction to concrete processes and workflows, together with AI-Actors, responsibly and human-led, and in the end it belongs to you, not to a black-box automation.
Roles from building blocks: RCB-C
The Role & Competence Building-Block Concept (RCB-C) builds roles from reusable role building blocks — each bundles responsibility, tasks and the competencies required (with proficiency levels). New roles build on existing blocks and develop only the delta, instead of defining everything from scratch. The same role can be filled by an HU-, AI- or HY-Actor — the competencies are realised differently depending on the actor. Because competencies have proficiency levels, development becomes steerable in a targeted way — this is where ARM comes in.
This makes it clearly negotiable what a role needs to be able to do, and who fills it best. That is the basis for distributing work cleanly between HU-, AI- and HY-Actors.
Managing actors: ARM
ARM builds on the RCB-C — it takes the role building blocks and uses them to manage HU-, AI- and HY-Actors across the entire lifecycle, including competence development across the proficiency levels.
Actor Resource Management (ARM) extends classic HR management (HRM) to all actor types — in effect, HR for humans and AI-Actors. When the RCB-C gap analysis surfaces a need, it can be met in two ways: onboard a new actor or develop an existing one. Then come deployment and evaluation; if a gap remains, the need is re-assessed, otherwise an orderly offboarding follows — above all for AI-Actors (e.g. licence end, security reasons), never the "dismissal" of people. Ultimate responsibility stays with the human.
This keeps it traceable which actors fill which roles, how they develop and when an orderly offboarding is due. That is the prerequisite for an organisation that works with AI for the long term.
Strategy as a continuous loop (IHMF)
For us, strategy, innovation and transformation are not a sequence but a continuous loop: day-to-day operations generate design and innovation impulses, which are placed strategically and fed back into operations — and the cadence tightens. The Integrated Holistic Management Framework (IHMF) holds these three fields together as one integrated, learning framework.
For the loop to hold, it rests on a learning organisation: shared understanding and learning, psychological safety, interdisciplinary collaboration and a whole-system view. This makes continuous development a core capability, not a project.
AI-Actors amplify this loop — more options and more speed, from broader analysis and scenarios to fast delivery towards the customer. Amplify, not lead: the loop stays yours. The concrete place for this is an AI lab, a protected space to practise, experiment and build capability, across all fields.
Process Orchestration
Rethinking processes with AI — human-AI develops human-AI processes.
Many companies start with their processes — and that is exactly where our approach becomes tangible. Analysing and redesigning processes is nothing new (consultants have done it for decades, and so do we). What is new is how we do it: together with AI-Actors.
AI-Actors help analyse the processes and suggest where human-AI collaboration genuinely makes sense. From this we derive roles, staff them with the right actor (human, AI-Actor or HY-duo) and steer them across the lifecycle — down to the question of which model works behind them. Human-AI shapes human-AI — in a learning loop that develops actors and organisation together.
Even an as-is analysis with our OPIA method (Organisation, Processes, Information objects, Applications) makes visible where things get stuck — media breaks, unclear responsibility, missing integration — and whether a pain point holds real potential or needs rethinking (up to the business model). This makes the real value assessable — before you invest. We build in measurability and monitoring from the start — a PDCA loop whose transparency also feeds the strategy loop (IHMF).
For every problem we first look for the root: often that means not "optimising" but differentiating or redesigning — sometimes this gives rise to entirely new, AI-enabled business models. And responsibility is built in: what a role may do is defined process-specifically and built into the target system (e.g. ERP, down to module level) by design — fail-closed: only what the role is explicitly allowed to do.
This is how strategy, processes, roles, actors and governance interlock into one coherent picture. Because everything works together, we can start at different points — with a small process as readily as with strategy or governance — and deliver impact there, always with the whole picture in view. In our role as enabler, we develop it together with you.
Leadership in the AI era
Collaboration is at the centre. The new leadership task is to steer the whole human-AI team (HU-, AI- and HY-Actors): setting goals, distributing responsibility, reviewing results, building trust. We make this capability learnable instead of leaving it to chance.
Not a one-way street
Human-AI collaboration runs both ways: AI must be made human-compatible, and people develop with it. This calls for a learning organisation and psychological safety, which we help build.
Learning happens by doing
This collaboration is not learned in theory but by doing, accompanied and enabled, on real solutions across all areas of the business. Together we identify a portfolio of concrete use cases and opportunities and work through them as part of the enablement, step by step towards independence.
How we work: agile human-AI project work
We apply our own method: short, cadenced loops with built-in quality assurance. It is exactly this way of working that we enable your team to take over.
Deliver in cadence
Short iterations with a clear goal; HU- and AI-Actors see a result through together.
Clarify quickly
Focused thrust into an open question before investing heavily, low-risk.
Shape together
Workshop format in which HU- and AI-Actors work through a topic side by side.
Cross-check independently
Cross-vendor review: results are critically reviewed across several AI providers before they count.
Optional: practise safely with synthetic data and simulations
New ways of working are learned safely in a protected space, individually tailored to your company and context, not off the shelf. With synthetic data (also complementing your own and researched data) your team trains with AI-Actors without risking real business or personal data. With scenario techniques and simulations we work on strategy, innovation and transformation and see their effects before they cost real money or trust.
Governance by design and at runtime, across the entire lifecycle. Responsibility, compliance and security are built into every step: into the roles (RCB-C), into the lifecycle (ARM) and into the way of working (counter-check via AAL). They take effect not only at design time but also in live operation, across the entire lifecycle. For us, governance is the responsible "how", not a box ticked afterwards on a list.