By Femke van Engen, project lead, and Olivier Lamers, data scientist at Rewire.

Let’s play make-believe.

You manage enthusiastic non-technical teams who interact regularly with technical teams. For example, to improve workflows, flesh out new ideas, or analyse data. The problem is that there is only so much your technical teams can handle.

Still, you'd like to improve collaboration between technical and non-technical teams and, more broadly, accelerate your organisation's innovation capabilities.

Well, vibe coding might be part of the answer.

But what, exactly, is vibe coding?

What vibe coding means

Put simply, vibe coding means describing what you want in plain natural language, and an AI tool turns it into working software in minutes. No coding skills needed. No briefing an engineer who sends you back to the drawing board until you’ve figured all the unknowns. No begging a tech lead whose next free slot is in six weeks.

The result? An idea that would have taken a few sprints to render visible can become a working prototype in less than an hour.

Still on the fence? Check out this web app that was put together by a senior exec in one of our recent vibe coding workshop. She had never coded before, yet built a Property Selector in under an hour — a desktop app that automatically scores, filters, clusters and assigns scraped real estate listings into optimised scout-visit schedules, helping investment analysts identify the best opportunities and plan field visits more efficiently.

The best part: no particular setup was needed. Just a Lovable account and her laptop.

Figure 1. From vague idea to detailed prototype in under an hour.

Why vibe coding accelerates innovation

The practical benefits of translating an idea into a working prototype that fast are straightforward:

• Speed to output. An idea becomes something your non-technical teams can show and react to in minutes instead of weeks. The distance between thinking and doing collapses.
• Better conversations. A working prototype beats a slide deck or a written brief. Fuzzy ideas become concrete. Requirements and variables are better defined. Your non-technical teams are better prepared and the translation gap from the person who understands the business problem to the person who can render it in code is vastly reduced.
• Lower cost of being wrong. If the concept does not hold up under scrutiny, your non-technical team finds out in an afternoon, not after a development sprint.

What vibe coding won't do

Ok. We’ve convinced you since you made it this far. There are, however, a couple of caveats.

First of all, the constraint now is conceptual, rather than technical: the quality of what you generate depends almost entirely on the quality of your thinking before you start. Define what you want (and don’t want!) clearly and a single well-constructed prompt will produce something impressive. Let’s be very clear: the best results tend to come from spending roughly 80% of the available time on definition and scoping, and 20% on generation. The AI is not a substitute for thinking.

Second, do not rush to make your IT team redundant. Vibe coding is great to build prototypes running on a laptop that overheats when you have more than 3 browser tabs open. But don’t expect to put this in production within hours. There are simply too many issues that need to be addressed first: cybersecurity, data sovereignty, governance, integration with legacy systems, to name a few. So stay friends with your technical team, and use vibe coding to improve the quality of the discussions between your technical and non-technical teams.

The main takeaway

Vibe coding won't replace good thinking, or your technical teams. It will show you and your business teams, whether an idea is worth their time, faster than ever.

The best prototypes start with the best thinking.

You don't need to know or write code. You need to describe what you want in terms that are clear to an AI. That skill alone will let you and your teams turn ideas into working prototypes, improve discussions with technical teams, and accelerate innovation.

Find out more

Across banks, insurers, and asset servicers today, a similar picture emerges. On the one side, AI initiatives running in parallel without ever being absorbed into daily operations. On the other, initiatives that reliably create value. The difference between the two groups lies in the approach.

Let's focus on asset servicing in particular, because the industry tends to assume regulation is what holds it back. The opposite is closer to the truth: Fund administration, transfer agency, and the middle office already run as a sequence of controlled steps with defined owners, defined inputs, and a check at every gate. That structure is precisely the one in which AI, and agentic AI in particular, does its best work.

When AI really delivers in asset servicing

Before examining what separates success from failure, it is worth starting with what actually works. Across our projects, three factors consistently drive AI value.

• First, a clear goal inside the process. For example, "cut the time from a complete subscription pack to a cleared investor onboarding by half, so the transfer agency team can absorb a fund launch without adding headcount" is a goal a team can tackle immediately, and one against which success can be measured. On the other hand, "use AI in onboarding" will fail on both counts.
• Second, the right data, understandable and reliable. Asset servicing sits on rich, structured data: investor records, subscription and redemption flows, fund accounting ledgers, custody positions, prospectuses, etc. Here the regulated firm has a quiet advantage, because reporting obligations and audit requirements have often already forced a level of data discipline that less regulated industries lack.
• Third, results where the decision is made. In asset servicing the point of decision usually sits at a control gate: the oversight check before a NAV is released, the four-eyes review on a payment, the compliance sign-off on a new investor. Good AI does prepares the decision inside the tool the reviewer already uses, surfaces the exceptions that need a human eye, and leaves a trace of how it got there.

Where these three elements come together, impact emerges that is measurable and scalable. And it is the pattern behind the AI work that actually takes hold in regulated operations.

The structure is the advantage

It helps to be precise about why asset servicing is such fertile ground. A fund's operational life runs as a chain of distinct stages, each with its own rules, its own data, and its own responsible owner. Investor onboarding and the KYC and AML checks that gate it. Subscription and redemption processing. NAV calculation and the oversight that validates it. Reconciliation across custody and accounting. Regulatory and investor reporting at the end. Between every stage sits a handover and, almost always, a control.

This is the same architecture that multi-agent AI systems are built around. Specialized components do focused work within clear boundaries, and they hand off to one another at defined points. A single, monolithic AI asked to do everything across this chain tends to drift and to hallucinate. Specialized agents with narrow remits and clean handover points behave far more predictably, which is exactly what a regulated process requires.

The regulatory apparatus that asset servicers sometimes experience as friction is, in this light, scaffolding. The four-eyes principle is human-in-the-loop, written into the operating model long before anyone spoke of agents. AIFMD and MiFID II define who is accountable for what. DORA, which has applied to financial entities across the EU since January 2025, has pushed firms to map their operational and ICT dependencies in detail, which is the same map an agent needs to act safely. The EU AI Act, now phasing in, asks for exactly the documentation, oversight, and traceability that a well-run fund administrator already produces for its regulators. Under the CSSF and its peer supervisors, governance is not a feature to be bolted on. It is already in the building.

The constraint, then, is coordination: getting the right specialized capability to the right step, with a clean handover and a human at the gate. That is an architecture problem, and it is one this industry has been solving by hand for decades.

Tap or click a milestone for the detail behind it.

Each rule that once felt like a constraint installed a guardrail: accountability, audit trail, human oversight, a mapped environment, delegation control. That is the exact control set agentic AI needs, and the regulated firm already operates it.

Four patterns that mark successful delivery

However different mandates and maturity levels may be, the firms making visible progress attend to the same four themes.

1.  Enable the right people, not only the technologists

A common assumption is that AI competence is mainly a matter of hiring more data scientists. In practice the larger leverage comes from the people who truly know the work: the fund accountants, the transfer agency officers, the oversight and quality managers who can tell a genuine exception from a benign one. They carry the context that makes an AI initiative take off. They know how a redemption will be processed, which investor data can be trusted, and where a NAV error occurs. When these people are deliberately enabled, in short, practice-oriented sessions on real cases, a technical experiment becomes a working tool. The reliable pattern is a tandem: domain expert and AI specialist working side by side, domain knowledge flowing into requirements, AI results flowing back into the process. The time from idea to first productive version typically halves.

2.  Focus on a few, genuinely viable use cases

The portfolios that work are deliberately lean. Three to five use cases with visible leverage on core processes, each with a named owner and clear success criteria. Equally important is the discipline of finishing things. When a pilot has exhausted its potential, it is closed deliberately, and it makes room for the next. An illustrative picture from operations: rather than spreading effort across onboarding, reconciliation, and reporting at once, a successful team concentrates on one defined step, for example pre-checking incoming subscription documents against the legal requirements and flagging what is missing before it reaches the reviewer. The benefit shows up less in a single headline number than in a team that is noticeably relieved and a queue that clears more consistently.

3.  Anchor AI directly where decisions are made

The last mile decides between AI introduced and AI in use. The firms that are consistent here build AI into the workflow: a flag inside the existing oversight tool rather than another screen, clear accountability for who acts on it, and a defined escalation for when the model is unsure. AI then becomes not an add-on but an extra pair of eyes and hands at the gate. An illustrative picture from oversight: a NAV review assistant recalculates key positions in parallel, compares them against the administrator's output, and presents only the discrepancies that exceed the NAV error threshold, with the underlying data attached. The reviewer still releases the NAV, as always. They simply see sooner where to look.

4.  Treat AI as a product, not a project

Software projects have an end. AI systems do not. Successful firms give their AI systems the attention they give any production capability: a responsible owner, an operating concept, regular quality checks, versioning, and cost control. The effect is that the system stays reliable as inputs and processes change, and trust in it grows steadily. This is familiar territory for a regulated firm, because model validation, ongoing monitoring, and documented oversight are already part of the vocabulary.

Agentic AI as the natural next step

These four patterns lead naturally to where the technology is heading. The most useful way to picture agentic AI in asset servicing is a small set of specialized agents working in parallel within human decision gates. The point is that this division of labour decouples the depth of analysis from the number of people available to do it. Routine, synthesis, and scale move to the agents. The human contribution concentrates where it has always created the most value, in judgment at the gate.

Between every stage sits a control. Agents do the preparation, a person decides at the gate, and nothing passes without an audit trail. This is the decision-gate architecture, drawn straight from the way fund administration already works.

Notice the feedback loop: regulation shapes the architecture of the AI system; the system, in turn, strengthens compliance — producing a more complete audit trail, faster.

It's not the tools. It's the capability

The firms that stay ahead build capability rather than acquire tools. Teams that understand and steer AI. Data products that are maintained and trusted. Systems that run in everyday operations instead of a test environment. And an operating logic that secures the value tomorrow as well. The most regulated corner of finance does not need to wait for the rest of the industry to work out how to govern AI. It already knows. The handover points, the four-eyes checks, the audit trail, and the documented oversight that can feel like weight are, for agentic AI, the blueprint.

Footnotes

* A further milestone reinforces the onboarding gate: the EU's 2024 anti-money-laundering package. Its single, directly applicable Anti-Money Laundering Regulation (AMLR) takes effect across the EU on 10 July 2027, and a central supervisor, AMLA, has been operational in Frankfurt since July 2025. In agent terms it sharpens a guardrail the chart already shows: verified, structured client due-diligence data and one harmonised standard for what must be checked before an investor passes.

About the authors

Dr. Philipp Diesinger is a data science executive with over 15 years of global experience driving AI-powered transformation across industries. He has led high-impact initiatives at Boehringer Ingelheim, BCG, and Rewire, delivering measurable value through advanced analytics, GenAI, and data strategy at scale.

Dorthe van Waarden leads organizations in creating tangible impact by developing and implementing advanced algorithmic systems. She is passionate about combining the power of humans with the potential of AI to achieve the best outcomes and doing this in a responsible way. Dorthe holds a MSc in Mathematics from the University of Amsterdam.

Agents reward organisations that ask harder questions first.

The organisations getting compounding value from agentic AI share one trait: they invested in getting the foundations right before scaling.

We've built agent systems across financial services, energy, telecom, and public sector, from early proofs of concept to production systems running at scale. We've seen what separates the deployments that deliver from the ones that don't.

Let's find the answers for your situation

By Marcel Mol and Job van Zijl, Rewire.

Companies looking to scale agentic systems quickly run into a limitation: AI is only as intelligent as the knowledge it can draw upon. While LLMs let organisations open up sources that were previously hard to utilize, such as PDFs and Word documents, the fact is that businesses aren’t documented the way they need to be to fully leverage today’s technology. 

Three problems arise:

• Knowledge is tacit, internalized by employees and never written down.
• Knowledge is imperfect, with out-of-date information, discrepancies between sources, and so on.
• Knowledge is missing.

To solve these problems, we introduce the knowledge engine methodology, underpinned by a broader vision: the codified enterprise. This is how we define the winning organisations of tomorrow — where knowledge compounds rather than churns, and where agents can be deployed reliably at scale.

How the codified enterprise develops asymmetric competitive advantage

Today, organisational expertise is siloed by design: it belongs to individuals and teams, rather than the organisation as a whole. People do most of the work, and AI augments specific processes as and when needed.

Agentic AI flips is set to flip the equation around, with agents doing most of the operational work, while people oversee strategy, creativity, and edge cases. But this is only possible with a governed knowledge layer that agents and humans can query and maintain. Knowledge — rather than churning out of the door whenever someone leaves — becomes a compounding asset.

Think about what this means operationally: the codified enterprise can onboard a new agent in hours rather than weeks, because the knowledge it needs exists in structured form. It can scale a process across markets with improved institutional memory. It can respond to regulatory change by updating a knowledge graph and propagating the change systematically, rather than hoping that the right people update the right documents and that those documents get read.

The non-codified competitor, meanwhile, is still dependent on specific individuals, still recreating knowledge from scratch, still debugging agents that behave inconsistently because their prompts are patching over gaps in an unstructured information environment.

Building tomorrow’s codified enterprise

Building a codified enterprise starts with addressing three challenges:

• Making all knowledge machine-readable. The issue here is that most business knowledge lives in people’s heads, unstructured documents, or process tooling that agents cannot access. None of it is governed or current.
• Knowledge extraction taking human effort. Even where documentation exists, it is incomplete and ungoverned. The decision rules that experienced people apply intuitively — when to escalate a claim, how to handle an exception — are rarely written down. Structuring this for autonomous agent use still requires people to validate and sharpen what gets captured.
• Knowledge is never finished. Processes change. Thresholds shift. Regulations evolve. A knowledge system not designed to update continuously will decay — and agents grounded in stale knowledge become unreliable.

The knowledge engine methodology uses process documentation as input, and allows expert validation of the acquired knowledge. But there’s more to it. As a video is worth more than a thousand words, check out the video demo below. You’ll see how the knowledge engine in action, as well as:

• How knowledge can be extracted following an expert-defined ‘knowledge skeleton’, applied to building a knowledge graph from internal policy documents
• How to the extracted knowledge can be visualized, and gaps and contradictions can be made visible
• How AI can help closing these gaps by suggesting fixes, which can be approved or changed by experts

Watch the video

Recent AI agent performance data is revealing: on focused tasks (two-hour sprints), top agents score four times higher than human experts. But stretch the same agent to a 32-hour task, and humans outperform it two-to-one. Agents achieve near-perfect success on tasks that take a human under four minutes; below 10% success on tasks that run over four hours.

The problem is structural. Making the agent smarter will not fix it, but redesigning the task will.

Source: Kwa et al. (2026). Measuring AI Ability to Complete Long Software Task. https://arxiv.org/pdf/2503.14499

Why the monolith fails, especially in pharma

The temptation to build one generalist agent and load it with context across regulatory, scientific, commercial, and clinical domains is inefficient. A generalist agent trying to manage regulatory compliance and synthesise scientific evidence and model reimbursement pathways will simultaneously hallucinate across domains won't understand deeply enough. It might be 90% right on average, but you can never know which 10% is wrong. In biopharma, that's an unacceptable risk.

When a generalist agent fails, everything fails: no isolation, no fallback. One flawed reasoning step cascades through the entire chain. Software engineering learned this lesson in the 2000s. One giant codebase, impossible to debug, impossible to scale. The answer was microservices: independent modules, independent deployment, independent validation. The same logic now applies to agentic AI in biopharma.

The orchestration answer: specialised agents with human decision gates

If intelligence comes from the model, correctness comes from the architecture. To achieve this, this is what the architecture should looks like:

• Small, specialised agents, each excellent at one narrow task. A regulatory agent that only checks compliance. A science agent that only reviews the evidence. A market access agent that only models reimbursement. Each can be validated independently, tested against known outcomes, retrained without touching the rest of the system. Need to add a new domain? Add a new agent.
• Validation gates between agents. If the regulatory agent flags a problem, the pipeline stops before downstream agents waste cycles on an invalid pathway. Guardrails must be structural, as we explain here.
• Humans at the right moments: not reviewing every output, but sitting at decision gates between phases. The architecture defines where human judgement matters most, rather than distributing it thinly across every step.

Now, most industries adopting agentic AI have to build governance frameworks from scratch: who validates what, where humans sign off, how to create audit trails. But here's the kicker: Biopharma already has this.

The decision-gate architecture — specialised agents operating within human checkpoints — maps directly onto how pharma already works. Regulatory affairs, medical affairs, clinical development, market access: each function already has defined review cycles, sign-off requirements, and clear accountability structures. That is the multi-agent orchestration model, without the agents.

The vast sums of money that pharma spends on regulatory compliance, usually framed as overhead, is actually a head start: the governance infrastructure that other industries are scrambling to build, pharma already operates.

What this looks like in practice

Take medical content creation. A single piece of promotional content currently moves through approximately seven sequential review cycles across medical, legal, regulatory, and localisation. Each reviewer waits for the previous one. Average cycle: six to eight weeks.

Under an orchestrated multi-agent model, a drafting agent produces the initial version. Medical accuracy agents and regulatory compliance agents run in parallel. Localisation agents adapt for local markets simultaneously. Humans approve at two defined gates: after the initial draft and after the final review. The cycle collapses from weeks to days, while the quality controls remain intact.

The same logic applies across the pipeline: pharmacovigilance monitoring, regulatory submission drafting, protocol design, site selection, KOL mapping. In each case, the opportunity is not to replace human judgement, but to remove the coordination overhead that delays it.

The gap will be measured in pipeline years

Enterprise application of agentic systems is growing at nearly 50% compound annually, and the pattern is becoming the dominant architecture of enterprise AI. For pharma, the gap between organisations that get the orchestration layer right and those still experimenting with generalist agents will very soon be measured in years of pipeline advantage.

Philipp Diesinger leads the pharma Practice Area at Rewire.

Agents reward organisations that ask harder questions first.

The organisations getting compounding value from agentic AI share one trait: they invested in getting the foundations right before scaling.

We've built agent systems across financial services, energy, telecom, and public sector, from early proofs of concept to production systems running at scale. We've seen what separates the deployments that deliver from the ones that don't.

Let's find the answers for your situation

Rewire LIVE started with a vision: to create an interactive event where participants from the business community could openly share their experiences with AI. Last week in Amsterdam, business leaders and innovators gathered to make that vision a reality.

The result exceeded expectations.

Through debates, workshops, and keynotes, attendees collectively explored what it takes to move from experimentation to meaningful impact with AI.

The next stage in the evolution of AI: introverted thinking

The day opened with a thought-provoking keynote by Wouter Huygen, Rewire CEO, who explored the paradox at the heart of Generative AI: it’s both incredibly smart and... surprisingly stupid.

Figure 1. ChatGPT, waxing digital.

So... “Are we building on shaky foundations and quicksand?”

His answer: no. Despite the limitations of current models, there is a massive opportunity overhang. With emerging toolkits and rapid innovation, today’s GenAI represents years of transformative potential.

Perhaps one of the most surprising insights was the emergence of “introverted thinking” in reasoning models – enabling transformers to refine their internal states before producing an answer. This leads to higher performance on complex reasoning tasks with smaller, more energy-efficient models. As Wouter noted, the bitter lesson of AI has, in the end, often tasted quite sweet.

Successful AI adoption: it’s all about speed and leadership

Moderated by Joe van Burik, tech editor at BNR Nieuwsradio, participants were invited to respond to a series of questions that revealed how they view AI adoption in corporate environments. Key insights included:

• 87% disagreed with the idea that AI is “just hype.”
• Most agreed that AI adoption should start with leadership, ideally from the boardroom down.
• The consensus was that it’s better to be a fast follower than a slow pioneer. Speed consistently emerged as a key success factor.

Participants then debated a range of topics, from build vs. buy strategies to regulation and productivity gains.

Figure 2. Joe submitting the audience to intense questioning.

The discussion concluded with a deceptively simple question:

What do you hope AI will bring to society?

We’ll leave you to ponder the resulting word cloud (below).

Figure 3. Every cloud has a silver lining.

Data & AI in action with hands-on workshops

The day featured four practical workshops, each tackling a different dimension of AI transformation:

• From GenAI demo to production, by Simon Koolstra & Mirte Pruppers (Rewire). Participants began with a GenAI demo and collaborated to solve the challenges that typically emerge when moving from demo to production. The exercise highlighted a hard truth: many can build demos, but scaling is where most fail.
• Reimagine your business with data, by Freek Gulden & Nanne van’t Klooster (Rewire). This session challenged participants to reimagine their businesses once data is viewed as a value stream rather than operational exhaust.
• Decentralized data leadership, by Daniëlle Bourgondië (PGGM Investments). Daniëlle led a lively discussion on what it takes for business domains to take ownership of their data and drive AI strategies. As she aptly put it: “This is not optimization. It’s reimagination.”
• Roles & responsibilities in digital transformation, by Sam van Kesteren (Royal Terberg Group) & Ties Cabo (Rewire). This workshop explored how to align business and technical roles & responsibilities to overcome some of the trickiest barriers to large-scale transformation.

Figure 4. Working out GenAI best practices: One team. One dream.

Real-world lessons from the corporate world

The day wouldn't have been complete without guest speakers detailing how AI is transforming their organizations from the inside out.

• Maarten Kramer, Chief Data Officer at a.s.r., demonstrated how agentic AI is reshaping complex insurance processes, starting with personal injury claims involving over two million documents annually. Their approach: treat AI agents like employees, with training, performance management, and oversight.
• Tim Prins, Program Director of Autonomous Operations at KPN, outlined how KPN aims to deliver the best customer service in Europe while halving costs. Agentic AI plays a central role in this ambitious transformation.
• Finally, Winifred Andriessen (VP of AI Excellence Center at KPN), Maarten Kramer (Chief Data Officer at a.s.r.), and Laura Brandwacht (Partner at Rewire) joined a panel discussion on AI’s impact on the workforce. They explored how AI is reshaping roles, the need for reskilling and upskilling, the enduring importance of human skills, and the leadership and culture required to make AI a success. The conversation underscored that AI transformation is as much about people and leadership as it is about technology.

Figure 5. Not just talk: the people who are transforming businesses with AI.

Key takeaway

The day concluded with drinks and conversations went well into the evening. Reflecting on the event, two themes stood out. First, we’re at a pivotal moment in history where the playbooks for leveraging AI are still being written. Second, collaboration is key. Joining forces is the best way to reimagine your business.