Why consulting in 2025 can no longer just deliver PowerPoints
The role of management consulting is changing radically: strategies alone no longer convince anyone. In many companies consulting ends on presentation slides that may look smart but have little impact on operational reality. Decision-makers today want answers to two questions: Does the idea work technically? and Will real users adopt it? If the answer doesn't exist in the form of software, data, or measurable user behavior, the strategy remains a hypothesis.
At Reruption we take a clear position: real consulting delivers products. Not someday, but quickly. Only with tangible prototypes, live demos and real usage data do we create the basis for sustainable decisions. This is not only a technical change but a cultural one: consulting becomes operational, integrated and measurable.
The limitations of classic consulting approaches
Traditional consultancies have strengths — large teams, methodological expertise, market analyses. But their models hit limits when it comes to implementing digital and AI-driven solutions. We repeatedly see three typical limitations:
- Abstraction instead of reality: PowerPoints abstract risks and assumptions. They reduce complex technical questions to words instead of code.
- Work-in-progress between strategy and product: Handing strategy to IT creates friction and delays. Important details get lost or become politicized.
- Decisions without data: Political debates replace validation with user behavior and rapid experiments.
These limitations lead to dangerous inertia: projects that look sensible on paper die in internal approval processes or fail to scale because technology and usage risks were never practically tested.
Our Co-Preneur approach: temporarily in the product team, permanently accountable
When we enter a company, we don't act like external observers but like co-founders. Our Co-Preneur approach means we embed temporarily into the product team, take responsibility in the P&L and actively work on delivery. That changes the dynamic: we don't just listen, we build.
What that concretely means
We take entrepreneurial responsibility: from prioritizing commercially relevant use cases to delivering a technical prototype that can be operated close to production. This proximity to the product team enables fast decisions, fewer political debates and a clear metric-oriented language: conversion rate, time-to-value, cost per transaction.
In practice this also means respecting existing company structures while accelerating them. In projects like the AI-based recruiting chatbot for Mercedes Benz we worked closely with HR and IT, put NLP models into production and ensured 24/7 availability. That wasn't a workshop result — it was an integrated product delivery.
How AI engineering and AI strategy converge
Strategy without engineering remains theory; engineering without strategy remains a workshop. We combine both: AI strategy defines the right goals (competitive advantages, KPI focus, data protection constraints), while AI engineering proves feasibility and operability. This flow is iterative and data-driven.
From objective to feedback loop
Our typical sequence: C-level workshop defines goal and metrics → rapid feasibility assessment → rapid PoC → user data and analytics → iteration or scaling. Crucially, each step produces tangible artifacts: code, metrics, user feedback. Decisions become empirical, not political.
One example: with FMG we implemented an AI-supported document search. The strategy focused on time savings and quality assurance; the engineering delivered an internal tool that collected usage data and increased hit quality by several dozen percent across three iterations. Proof instead of belief.
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Rapid prototyping: how PoCs bypass politics and blockages
Fast technical prototypes are not just an engineering trick — they are a decision instrument. A prototype generates evidence in seconds to days: runtime, costs, quality, integration effort. This evidence reduces discussions to concrete questions: Is the investment worthwhile? Which architecture scales?
The typical PoC process
- Day 0–1: C-level or domain workshop — goal setting, constraints, success criteria.
- Day 2–5: Feasibility & data quickscan — models, data availability, privacy risks.
- Week 1–2: Rapid prototype — a working minimal product with real data or a synthetic fallback.
- Week 3–6: User tests & analytics — qualitative and quantitative validation.
- Week 6+: Decision: kill, pivot or production plan.
Our standardized AI PoC offering (€9,900) reflects this sequence: not as a marketing promise, but as an operational process that proves technical feasibility in a short time. A PoC cuts through politics because it delivers answers that are no longer negotiable.
Examples from our practice: at Internetstores ReCamp early prototypes helped automate quality inspection processes for used equipment. The result: concrete cost savings and a valid basis for scaling. At STIHL we developed prototypes for training tools (saw training, saw simulator) that provided usage data in a short time and shifted the product roadmap discussion from “what if” to “we measure this now.”
C-level workshops: from vision to sprint board
C-level workshops are where visions are operationalized. Our goal in these workshops is not to produce more slides but a concrete sprint board with priorities, risks and a clear PoC plan. We work with leadership teams to set the right conditions:
- Target metrics: Which KPI moves the lever in the business case?
- Resource commitment: Who provides data, infrastructure and product ownership?
- Exit criteria: When is a PoC considered successful or failed?
A workshop at BOSCH, for example, resulted in a display-technology project not getting bogged down in long alignment rounds but being converted into a validation engine within a month, whose measurement data later formed the basis for a spin-off decision.
The role of analytics and real user behavior
Analytics are not a nice-to-have; they are the backbone of every AI decision. Only real user behavior answers the question whether a product works. Tracking, telemetry and A/B experiments are tools, not buzzwords.
What must be measured
- Product metrics: activation, retention, conversion, depth of use.
- Model metrics: inference time, cost per request, accuracy, bias indicators.
- Business metrics: time saved, cost reduction, revenue contribution.
We instrument prototypes so they deliver meaningful data from day one. A fast iterative cycle with real users reduces assumptions and provides indisputable decision bases. At Eberspächer, for example, we used production data to optimize noise reduction algorithms in real time — not through simulations, but through measurements on the shop floor.
Fast iteration cycles: how small experiments trigger big change
Large programs often fail because of their scale. We recommend small, frequent experiments: short hypotheses, measurable KPIs, rapid deployments. Each iteration delivers learnings that adjust the product, architecture or strategy.
Practical tips for iterations
- Use timeboxes (1–3 weeks) instead of perfection goals.
- Use feature flags to run experiments safely in production.
- Automate telemetry so metrics arise without manual effort.
- A/B tests let you quantify value directly — internally and externally.
This method frees organizations from the illusion of long-term predictability and creates real progress through learning velocity.
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Governance, security & compliance: operationalize, don’t block
A common objection is: “What about security and GDPR?” Good point — but governance must not be an excuse for inaction. We build compliance into the process: privacy-by-design, audit trails, access-restricted sandboxes and clear data contracts between teams.
For sensitive use cases we initially develop isolated prototypes with synthetic or anonymized data. In parallel we create the processes for how real data can later be integrated securely. This way we remain agile without violating regulatory requirements.
A pragmatic playbook for decision-makers
For C-levels, heads of innovation and tech leads we have a compact approach that is immediately applicable:
- Prioritize: Identify 2–3 AI use cases with clear business impact.
- Commit: Provide data, infrastructure and product owners for 4–8 weeks.
- Proof-of-Value: Start a rapid PoC (1–6 weeks) with real users or representative data.
- Instrument: Measure product, model and business metrics from day one.
- Decide: Scale, pivot or kill — based on data and a clear plan.
This playbook eliminates unnecessary debates and anchors accountability. We support clients along these steps in the role of Co-Preneurs and bring experience from projects with MEETSE (Internetstores), Mercedes Benz, STIHL and other clients.
Conclusion: Why the change must happen now
Companies are under pressure: those who don't learn to build and evaluate AI solutions quickly will lose market share to more agile competitors. The future of consulting is product-oriented, data-driven and operational. Consulting that only delivers recommendations is becoming increasingly irrelevant.
Our advice to decision-makers: test the new form of consulting with a real prototype — not with another strategy presentation. We bring the team, the technology and the responsibility so your organization doesn't react but leads.
If you're interested in what a concrete PoC could look like in your context — from idea to live demo — talk to us. We accompany you as Co-Preneurs, deliver outcomes instead of slides and create the basis for data-driven decisions.
Takeaway: Consulting 2025 is software-first: rapid prototype, clear metrics, real user data. Only this creates reliable decisions and sustainable value.
