Agentic Shift

Redefining how businesses operate, compete, and create value

As AI moves from assisting humans to autonomously executing tasks and driving decisions, companies must rethink their processes, operating models, and organizational structures. The opportunity isn't just to automate what exists, but to fundamentally reimagine how businesses work.

Talk to an expert

AI ambition shared by your entire leadership team

100 %

use cases backed by a business case

Qualification

When your AI needs an operating model, not another pilot

Most companies do not lack AI ideas. They lack a way to decide which ones pay off.

You're adding agents to yesterday's processes
Replacing steam engines with electric motors didn't transform manufacturing. The same applies to AI: adding agents to existing workflows delivers incremental gains.
Every business unit has its own AI ambition
There is no shared target picture at leadership level. AI agents appear everywhere, without shared rules, owners or a cost model.
Dozens of ideas, no ranking
Nobody has a method to rank AI ideas by value, feasibility and risk. Pilots stay disconnected from the strategy and never reach the real business process.
Everything is called agentic
Use cases get labeled as AI agents, although a rule-based workflow or an assistant would handle them more reliably and at lower cost.
Nobody decides what an agent may do
There are no AI product owners and no clear split between IT and business. Autonomy is debated case by case, because there are no agreed levels.

You're adding agents to yesterday's processes

Replacing steam engines with electric motors didn't transform manufacturing. The same applies to AI: adding agents to existing workflows delivers incremental gains.

01.

Every business unit has its own AI ambition

There is no shared target picture at leadership level. AI agents appear everywhere, without shared rules, owners or a cost model.

02.

Dozens of ideas, no ranking

Nobody has a method to rank AI ideas by value, feasibility and risk. Pilots stay disconnected from the strategy and never reach the real business process.

03.

Everything is called agentic

Use cases get labeled as AI agents, although a rule-based workflow or an assistant would handle them more reliably and at lower cost.

04.

Nobody decides what an agent may do

There are no AI product owners and no clear split between IT and business. Autonomy is debated case by case, because there are no agreed levels.

05.

Capabilities

From AI ambition to measurable results

Your AI strategy stays yours. Your leadership sets the priorities, and we bring method, research and delivery experience. AI agents go only where they pay off, and a clear "not here" counts as a result.

AI maturity check.
See where your company stands with AI, from strategy and data to skills and governance.
One AI ambition.
Agree on one ambition at leadership level and translate it into targets the P&L can show.
Business cases before budget.
Fund use cases on business cases with KPIs, owners and stop criteria.
Value tracking after go-live.
KPIs, owners and a reporting rhythm the board can follow, long after the first pilot.

A ranked portfolio.
Workshops connect the leadership vision with ideas from the business units. Every idea is rated by value, feasibility, data readiness and risk.
Agent or not.
An AI agent fits where decisions, exceptions and several systems are involved. A workflow with an AI step fits known rules with unstructured input, an assistant fits knowledge questions, classic automation fits high-volume tasks that allow no errors.
The right build path.
Where an AI agent fits, you also get a recommendation on how to build it: in a low-code tool, as a purchased specialist agent or as a custom agent.
Start where value meets data readiness.
The first use cases go where value and data readiness meet. Shared patterns let each next use case build on the last.

An operating model that fits.
A central AI team, AI product owners in the business and a repeatable process for new use cases, so business units can bring the next ones themselves.
Decision rights in writing.
Who decides what between IT, business and compliance is written down, not assumed.
Graduated autonomy.
Suggest, act with approval, act within limits and report. The level is set per use case and risk class, and high-impact or regulated processes keep human approval.
One portfolio, one governance frame.
Agents built in your office and low-code tools are steered in the same frame as custom AI agents.

The works council as design partner.
Works council and data protection are involved early, so rules for AI agents are agreed before rollout, not negotiated afterwards.
AI skills by role.
Enablement is planned by role and use case, as measures that support AI literacy under the AI Act.
The AI Act as a timeline.
Use the time until the high-risk obligations apply, from December 2, 2027, to classify use cases and put governance in place.

We enjoy working with Cloudflight because we greatly value the personal and direct contact, the short communication channels, and the shared team spirit.

Robert Nachbauer

Project Manager Strategic LTS Initiatives (TGW Logistics)

Collaboration

From first workshop to operating model

No strategy deck without a next step. You know from the start what each phase delivers.

1
Align
In an AI strategy workshop, your leadership team and selected business units review running AI initiatives, agree on the ambition and shortlist use cases. You leave with the shortlist, open questions per use case and a proposal for the next phase.
2
Prioritize
Each shortlisted use case is rated by value, feasibility, data readiness and risk, with a clear recommendation: AI agent, workflow with an AI step, assistant or classic automation. Your leadership decides what goes ahead.
3
Realize
Business cases with KPIs, owners and stop criteria, and an AI roadmap aligned with your budget cycle. The first use cases move on to a measured result on your data.
4
Organize and scale
An operating model with roles, decision rights, graduated autonomy and value tracking, agreed with the works council and data protection before rollout. Built to be handed over to your team.

A woman leads a brainstorming session in front of a whiteboard covered in sticky notes, with four colleagues seated at a conference table with laptops.

Trust

An AI strategy grounded in delivery

Your AI strategy stays yours

Your leadership sets the priorities. We bring method, research and delivery experience, and enable your team to run the portfolio and the operating model itself.

Checked against reality

The people who prioritize your use cases also build data platforms and AI agents, so every priority is checked for data readiness, effort and operability. Behind them: 800+ experts, 600+ engineers and 3,000+ projects.

A clear "not here" is a result

Every candidate use case is weighed against simpler options. You get AI agents only where they pay off.

Measured, not demonstrated

Evaluations on your own cases, cost per agent and value tracked against the business case, for every AI agent in one portfolio.

Your AI strategy stays yours

Your leadership sets the priorities. We bring method, research and delivery experience, and enable your team to run the portfolio and the operating model itself.

Checked against reality

A clear "not here" is a result

Every candidate use case is weighed against simpler options. You get AI agents only where they pay off.

Measured, not demonstrated

Evaluations on your own cases, cost per agent and value tracked against the business case, for every AI agent in one portfolio.

FAQ

Questions about your AI strategy

Agentic Shift is our AI strategy and operating-model service for AI agents, part of Technology Advisory. Your leadership decides where AI agents create measurable value and where they do not. Together we turn these priorities into a use-case portfolio, an operating model and governance.

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Contact

Let’s find your first use cases

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Redefining how businesses operate, compete, and create value