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Much of our daily work consists of gathering information, searching through resources, rewriting the same ideas in different formats, or summarizing discussions. This kind of grunt work naturally makes people look for ways to save time, reduce repetition, and reach a useful outcome with less friction. And agentic AI presents itself as a powerful shortcut, capable of turning a rough thought into a polished email, a meeting into a concise summary, or a manual workflow into an automated step.

For many teams, that is precisely the trap: that AI chatbots and copilots are capable of the same kind of conceptual work people do instinctively when taking a vague goal, breaking it into meaningful parts, and moving it toward a useful outcome. And they, eventually, become lazy in the wrong way.

AI adoption starts with realistic expectations

The public promise around AI adoption suggests something close to a general-purpose thinking partner. A system that can understand goals, interpret context, and work through a problem much like an experienced colleague would. But that couldn’t be further from the truth.

AI is a predictor, not an understanding system

At its core, AI predicts likely continuations. It does not understand your business model, your operating constraints, or the relationships between the moving parts inside your organization in the way a person does. It does not know why one exception matters more than another, why a process exists in its current form, or what tradeoffs sit behind a decision unless those things are made explicit.

As a result, AI can be fast, but still shallow. It may generate something that looks complete while missing the mechanics that actually matter to the business.

AI can sound professional and still be wrong

One of the reasons AI is so persuasive is that it presents information with confidence. It writes clearly, and structures arguments well, but fluent output should not be mistaken for expertise.

An experienced professional in any domain will typically outperform AI where nuance, context, and consequence matter. They can spot what is missing, question what does not fit, and recognize when an answer is technically plausible but practically wrong. AI, by contrast, can hallucinate, or flatten important distinctions.

More context does not automatically make AI better

The model does not understand information the way a person does. It does not inherently know which detail is strategically important, which relationship between inputs actually drives the outcome, or which exception should outweigh the broader pattern. When too much context is added without clear structure, prioritization, or framing, important information gets diluted by secondary or weak signals, and the output reflects surface-level correlations.

This is why strategic selection still remains a human responsibility.

The human role in AI strategy and implementation

A properly structured and governed AI strategy is what turns agentic AI from a shortcut into a system. But that structure does not come from the model itself. It comes from the person using it. The one that determines what information matters, what can be ignored, what the actual goal is, and which constraints should shape the output. AI does not make those decisions well on its own, at least not reliably in its current state.

For this reason, every human-agent exchange should be guided by five essential elements:

1. A scope with boundaries

AI performs best when the task is defined clearly enough that the system knows what it is being asked to do and what sits outside its role. Without boundaries, AI tends to default to broad, generic responses that may look complete but lack operational relevance.

A system asked to support a service workflow, for example, needs to know whether it is drafting a response, classifying an issue, extracting information, or recommending a next step.

2. A defined process

AI is most effective when it supports a process rather than replacing one that has never been properly defined. If the workflow itself is unclear, inconsistent, or heavily dependent on undocumented workarounds, AI will reflect that ambiguity, not resolve it.

This is often where organizations overestimate the technology. They assume the model can compensate for process gaps, while in practice it usually amplifies them.

3. A definition of quality

If no one defines what “good” looks like, AI cannot reliably produce it. Quality has to be made explicit. That means deciding what level of accuracy is acceptable, what kinds of mistakes matter, what should trigger review, and where the cost of error is too high for guesswork.

In customer communication, “good” may mean clarity and consistency. In regulated documentation, it may mean traceability and compliance. In an internal support workflow, it may mean speed with a human checking exceptions. AI cannot infer those standards reliably on its own.

4. Curated context

Context is valuable only when it is relevant, structured, and timed correctly. That means selecting the inputs that genuinely influence the task, excluding what does not, and making the relationships between inputs as legible as possible.

In a manufacturing setting, that might mean prioritizing machine status, order constraints, and maintenance windows over general historical information, while in a commercial process, grounding the system in account history, product rules, and current workflow stage.

5. Controlled rollout

AI should not move from a promising output to full autonomy in a single step. Before a system is trusted in real operations, teams need to test it in practice, compare its output against human judgment, and define where review remains necessary.

Let AI run beside people first, supporting the workflow, exposing patterns, proving its reliability, and monitoring performance over time. Teams need to see where the system performs well, where it fails, what kinds of errors recur, and when changes in context or model behavior require adjustment. That is how trust is built.

A practical framework for AI implementation in business

Many AI implementation efforts stall because organizations move to agents before they are ready. At CLEVR, we often see this happen when the process is not yet clearly defined and trust has not yet been built into the workflow. That is why we have developed a six-step methodology for organizations preparing for the agentic future.

It is the same framework we use in our advisory process to assess AI maturity, identify structural gaps, and define the right next step on the path toward more advanced agentic systems:

1. Understand the work

Look at how the process actually runs in practice, including manual workarounds, exceptions, and informal steps that never appear on an org chart.

2. Find the real opportunities

Identify where AI can create meaningful leverage without introducing unnecessary risk (e.g. processes that include repeated effort, predictable bottlenecks, valuable time being lost on manual work, etc.)

3. Design deliberately

Decide what the AI is supposed to do, what information it needs, and what should remain within human scope.

4. Define what “good” means

Determine what acceptable performance looks like, what kinds of mistakes matter, which outputs require review, and where the cost of error is too high for approximation.

5. Run beside people first

That makes it possible to compare outputs, identify failure patterns, and learn where human review still adds the most value.

6. Monitor and improve

The strongest AI systems improve because they are observed, adjusted, and scaled from proven use cases rather than assumed to be finished from day one.

Make your AI strategy the real leverage

AI can create a unique competitive advantage, but only when it is applied with structure, context, and a clear definition of success. That is especially true for organizations looking beyond prompts and toward agents, automation, and more autonomous systems. With 30+ years of experience in digital transformation, and a strong portfolio in AI agents, AI solutions, and automation, CLEVR helps organizations take those first steps with clarity.

From understanding where AI can create value, to designing the right use case, and building a roadmap toward more advanced agentic systems, we help turn early ambition into practical progress. One step, one use case, and one trusted outcome at a time.

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Every day when I wake up, I open my laptop, read my emails, and check the news (also the AI news). And every day I see new models, new research papers, and new projects. There's a lot of things happening.

I feel haste. I feel urgency. I have the feeling that I have to do something with this information and also a little bit of FOMO. I see other companies taking actions and I think maybe we should do too.

All this creates a kind of pentup energy that I don’t really know where to put. It makes me feel like I should do something. And like every person in business I fall back on the most familiar reflex when something becomes too big, too fast, or too complex to handle: outsource it, hire help, make it someone else’s problem.

And with AI I think that it's the wrong way to look around about it.

Outsourcing AI thinking is dangerous

We see this with many of our clients. They bring in external teams like us to build software, just like they hire plumbers to fix blocked pipes. They don’t train plumbers internally because it’s inefficient, and they don’t stand up full development teams from scratch because it takes enormous time, cost, and organisational effort. In most cases, outsourcing is simply the fastest and least disruptive way to keep the business running.

But the moment you hand it off, you also hand off the learning that comes with it. The thinking, the decisionmaking, the conversations you should be having internally about AI, those end up happening somewhere else, with someone who isn’t living your organisation’s reality.

And that’s the real risk. AI is topic simply too big, and it’s going to change the way we work too deeply, for any organization to outsource the understanding and the learning to an entity outside your own walls.

Why AI is different from every "disruptive" technology before

When we talk about technology, we often throw around the word “disruptive,” but AI genuinely earns it. Not because it’s louder or faster, but because it changes where work happens and who can do it. So the question becomes: why is AI different from all the other technologies we once thought would change everything? For me, it comes down to three simple but profound shifts.

1. Humans work inside systems, AI works across them

We all work in systems. Whether it’s CRM, email, development tools, ERP (you name it) our daily work happens inside these structured applications. But the real effort, the part no system truly handles, lives between those tools.

Whenever something is too complex or too unstructured to automate, we put humans there. They make judgment calls, chase information, talk to multiple teams, fix issues, and move processes from status A to status B. In practice, people act as the connective tissue that keeps all these systems aligned and moving.

They are the glue between applications, and that’s exactly the space where AI is starting to make an impact.

Those inbetween roles, those loops are now increasingly automatable. Five years ago this simply wasn’t realistic. Today, AI can take over more of that glue work, the work currently done by people, and in the future this will only accelerate.

2. AI automates what was previously not automatable

The AI market can be sliced in many ways, but the distinction that works best for me is this:

On one side, you have tools, the more traditional, incremental form of software development. A new feature here, a small improvement there, something that makes a product 5% better or a bit nicer to use. In the AI world, that’s things like translation features, summarisation buttons, or a smart autocomplete that fills in a few fields for you. Useful, but ultimately just extensions of what software has always done.

Then you have agents. And I’ll be honest, I don’t even like the word, because everyone calls everything an “agent” these days, and 9 out of 10 times it isn’t one. Because if you look carefully at what a true agent actually is, it’s something very different.

It's a software system that can take unstructured information, turn it into its own todo list, execute that list (or ask other AIs to do it), move between systems, pull data from your CRM, make decisions, and then produce structured, meaningful output. That’s not a nicer tool. That’s a different category of software entirely.

Because the truth is, our work is really just a bundle of tasks. Some of those tasks are incredibly difficult to automate (like building relationships, reading a room, having dinner with a client if you are a salesperson). Human connection isn’t something AI can replace so those parts of the task bundle are, for now, safe.

But the small, repetitive administrative tasks? Current AI systems can already automate many of these or help you complete them much faster. And everything in between. Those mixed bundles of judgment, admin, and minor decisions, AI will become increasingly capable of handling. And that capability will only continue to grow.

But how will people experience these shifts? How will we guide them through it? How will we make sure this transition strengthens, rather than unsettles, the organization?

Navigating the human side of an AI-driven workflow

Certain tasks will naturally shift from humans to AI, we see that happening little by little everyday. One or two tasks here, a small process there, nothing dramatic at first. The work doesn’t disappear. It simply stops being done by people.

And that’s where the real conversation begins. Because while tasks may move, the people doing them don’t vanish. Their identity, their sense of contribution, and the value they bring to the organisation are tied to that work. So we need to start talking about these things now, openly and honestly.

The financial pressure

A little while ago, we visited one of our retail clients. In many ways, their organisation was wellstructured: each department ran efficiently within its own vertical, people knew what they were responsible for, and they solved problems quickly. But the moment work had to move between those verticals, everything started to slow down.

They had people manually moving information from one system to another. Typing data into Excel, copying it into Outlook, pulling information back out of Outlook, adjusting formats, fixing small inconsistencies (“this should be five numbers instead of six”), and repeating that process dozens of times a day. None of it was strategic work. All of it was essential work.

And this is the reality for many organisations. These manual gluetasks easily cost €50,000 per person per year. Now imagine an AI system that can do 80% of that work for €500 a year.

What would you do then? What would your customers do? What would any business do if they had a hundred people performing those types of tasks?

This is where the financial motivation becomes impossible to ignore.

People need to be part of the plan

This is where the human side becomes just as important as the financial one. If you’re not actively planning for how AI and automation will be introduced in your organisation, how people will be trained, how their roles may evolve, and how this new technology will find a place that feels fair and comfortable, then people simply get left out of the story.

Because if the discussion reaches the board without that human context, it turns into a numbersonly decision. On a spreadsheet, €550,000 versus €500 is not a dilemma; it’s a conclusion. And when that comparison involves dozens or hundreds of people, the choice becomes even more obvious.

That’s why it’s essential to build a human plan alongside the financial logic. People need to understand what’s coming, how it affects their work, and what their future looks like in an AIenabled organization. This shift is happening whether we want it or not but how people experience it is still very much in our hands.

The first steps every company should take

We need to start having real conversations about AI, not because it's trendy, but because the world around us is moving whether we participate or not. Two years ago, for some organisations, “AI” meant buying a chatbot or automating a single workflow. But every day I open my laptop, read the news, or check new research, and the capabilities have grown again. Things we thought were impossible last year are suddenly standard.

Other companies are already acting on this. And if we aren’t even aware of what’s becoming possible, we can’t expect our organization to generate the ideas or innovations we’ll need to stay competitive.

The best ideas always come from people. But only if those people are informed, involved, and part of the conversation.

1. Remove the fear around automation

Automation is already happening all around us, and one of the most important things organisations can do is make it a topic people feel safe discussing. It doesn’t have to be a scary word. In many industries (manufacturing is a great example) automation has been evolving for decades. Work that was once done with hammers, chisels, and manual effort is now done by robots, and often done better.

So automation itself isn’t the problem. The real challenge is helping people understand what it means for them. You need a plan for how your organisation will adapt, how roles might evolve, and how people will be supported through that change. When automation is part of an honest, structured conversation, it becomes something you manage, not something you fear. And that brings me to the second point.

2. Be transparent

Transparency becomes critical the moment you start moving toward AI adoption. People need to understand what is happening, why it is happening, and how it will affect the way they work. When organisations stay quiet or vague, uncertainty fills the gaps. And uncertainty quickly turns into fear.

That’s why you need a clear roadmap. Not a perfect one, but one that shows direction, intent, and honesty. Let people see how you’re approaching this project, what decisions are being made, and where they fit into the story.

If we are upfront about the scale of the transformation, people can prepare, contribute, and adapt. But if we keep the process behind closed doors, AI becomes something that “happens to them” rather than something they are part of.

3. Enable organizational insight

Before you can do any of this successfully, you need a clear understanding of your own organisation. Your processes, your data, your people, and how work actually gets done. This has never been more important, because AI is now capable of automating the kinds of work that were previously considered impossible to automate.

Most companies have beautifully documented process diagrams and welldefined application flows. But everything between those flows, the real daytoday work, the unwritten parts of your job description, the informal steps people take to keep things moving? Those are rarely captured anywhere. And it’s exactly in that unstructured space where AI is beginning to make its impact.

Act or be acted upon

Are you going to be the kind of organisation that embraces AI intentionally? One where people are informed, aligned, and understand how the company plans to work with AI as its capabilities grow?

Or will you become the organisation where AI simply “happens” to you? Two years pass, competitors have embraced AI, costs have dropped, efficiency has soared, and suddenly customers are asking why you can’t keep up.

If you reach that point, you no longer have the time or space to create your own framework, your own human story, or your own way of adapting to these changes. You’re forced into action instead of choosing it. And by not acting, by not even beginning the discussion, you’re still making a choice.

You’re choosing to end up in the group where AI happens to you rather than through you. And that is a position no organisation wants to find itself in, yet it is the silent reality many companies are drifting toward.

AI is a train already moving

AI is getting more capable every day, and ignoring it won’t slow it down. It’s a train already in motion, whether we like it or not. The only real question is whether we choose to take control of how it impacts us.

That starts with getting informed, involving more people, and having the conversations that matter. And I genuinely believe we are already taking good steps in that direction at CLEVR. More people are engaged, more discussions are happening, and that’s exactly what we need.

So talk about it. Think about it. Discuss it with your colleagues. The more we share our thoughts and questions, the better prepared we become.

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Every year, Siemens Realize LIVE brings together manufacturers, engineers and technology leaders to explore where the industry is heading and how that future is taking shape in practice. Product updates, roadmap announcements, and technology highlights across sessions, customer stories, and partner discussions have shown this year that manufacturing is no longer about optimizing individual technologies. It is about connecting systems, intelligence and people into one coherent whole. This shift is particularly visible in how AI in manufacturing is evolving from isolated use cases into connected, operational systems.

This shift builds on a long standing ambition within the industry to create connected environments where data flows seamlessly across PLM, ERP, MES and supplier networks, improving efficiency, visibility and decision making. The introduction of AI further expands this landscape, not only increasing the potential for automation and insight, but also raising critical questions around how these capabilities can be translated into measurable and scalable operational value.

Here are the 4+1 ideas that defined Realize LIVE 2026 and what they mean for the future of manufacturing.

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1. AI is becoming operational, not experimental

For years, AI has been positioned as a powerful tool, supporting individuals through copilots, predictions and insights. At Realize LIVE 2026, it became clear that this phase is evolving toward execution at scale.

AI use cases in manufacturing are moving from assistance to orchestration within real workflows. Instead of responding to prompts in isolation, it is being embedded into processes where it can support decisions, coordinate systems and automate multi step activities across the lifecycle.

The introduction of Intelligence Center X signals this transition explicitly. By connecting enterprise data, lifecycle context and workflows in a governed environment, organizations can deploy AI agents alongside people as part of a hybrid workforce, moving from isolated pilots to production level execution with traceability and control.

The question is no longer whether AI works. It is how organizations embed it into connected systems and structured workflows, with the governance required to deliver consistent, measurable and scalable value.

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2. Connected systems matter more than isolated innovation

Despite continued investment in digital transformation, many manufacturers still face challenges when it comes to scaling innovation across the enterprise. The underlying issue is not a lack of technology, but a lack of connectivity, particularly across PLM, ERP and MES integration layers that are essential to enable scalable AI in manufacturing.

Across organizations, systems remain fragmented, data is distributed across silos, and AI initiatives are often introduced as isolated pilots. While these efforts deliver local improvements, they rarely translate into measurable impact across the full product lifecycle.

In manufacturing, design, engineering, production and service are inherently interdependent. As a result, optimizing individual components in isolation does not drive systemic improvement. Connecting their existing systems into a cohesive digital thread does.

This is exactly what our CEO, Tim Claes, emphasized in his keynote, framing this challenge and opportunity through what he defined as the Holy Trinity of Manufacturing:

• PLM as the backbone for product data and the digital thread
• Smart factory as the layer connecting IT and OT, enabling visibility and control across operations
• Low code and AI as the orchestration layer that connects systems, workflows and decision making

Individually, each of these domains delivers value. However, the real impact emerges when they operate as part of a connected system. This is increasingly becoming the approach that manufacturers adopt to remain competitive and relevant in a rapidly evolving market.

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3. Sustainability is becoming part of everyday engineering

Another strong signal from Realize LIVE was the shift in how organizations approach sustainability in manufacturing and compliance.

Regulation is tightening, particularly in Europe, where frameworks such as CSRD, REACH and emerging requirements like the Digital Product Passport are forcing organizations to provide detailed visibility into materials, sourcing and environmental impact across the entire value chain. Adding to it the pressure specific industries face such as aerospace and defense to improve their margin, and the stricter compliance requirements and growing expectations from OEMs and partners, sustainability can no longer be a separate reporting activity, but a factor that directly influences cost, risk and competitiveness.

During his keynote, Gerrit Kiefer, our Head of Solutions and Customer Success Management in Germany, demonstrated how this transition is already taking place in practice. Together with tec4U, he showcased how compliance and sustainability can be embedded directly into engineering workflows, enabling organizations to:

• Integrate regulatory requirements directly into PLM and design environments
• Ensure full traceability of materials, components and suppliers across the product lifecycle
• Reduce manual effort in compliance reporting through automated data capture and validation
• Identify risks and compliance gaps earlier in the design phase, where they can still be addressed efficiently
• Align cost, sustainability and engineering decisions by making all relevant data available in one connected workflow
• Accelerate time to compliance while maintaining control and auditability across processes

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4. The rise of the orchestration layer

With the introduction of Intelligence Center X, Siemens clearly articulated a strategic focus on establishing an orchestration layer that connects enterprise data, workflows and AI agents into a cohesive and scalable system. An orchestration layer acts as the architectural component that enables data orchestration, workflow coordination and AI execution across systems.

The implication for manufacturers is significant. Most of them have already invested heavily in core systems such as PLM, ERP and MES. So instead of building new systems, they can activate and connect what already exists.

At CLEVR, we have consistently been advocating that large scale rip and replace strategies are no longer sustainable, nor necessary. Over time, engineering logic, domain knowledge and process intelligence have become deeply embedded within existing systems. Replacing these foundations would not only introduce risk, but also discard valuable intellectual capital.

Instead, manufacturers can introduce an orchestration layer on top of their current landscape, enabling them to connect workflows, embed intelligence and extend capabilities without disrupting what already works.

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4+1. Technology is ready. Organizations are not

If there is one thing we can safely extract from Realize LIVE 2026, it is that that the challenge is no longer technological. Manufacturers today have access to advanced PLM platforms, connected factory systems, low code environments and increasingly powerful AI capabilities. The building blocks for transformation are already in place.

Yet many AI adoption initiatives in manufacturing continue to stall. At CLEVR, we see this pattern across organizations every day. Workflows not fully understood or mapped, decision logic remaining implicit, unstructured data, or connected in a way that enables reliable, cross-system execution.

Embedding intelligence into workflows requires more than deployment. It requires clarity on how decisions are made, where responsibility sits, and how humans and systems interact.

This means the next frontier to address is organizational in nature. Manufacturers need to design workflows that are clearly defined, decisions that are explicitly structured, and governance models that can be translated into executable logic. Only then can AI agents operate reliably, make autonomous decisions within defined boundaries, and scale across the enterprise with consistency and control.

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5 actions manufacturers can take today

First, the focus needs to shift from adding more tools to connecting existing ones. Most organizations already have the core systems in place. The real opportunity lies in linking them into a coherent digital thread across design, engineering, production and service.

Second, AI initiatives need to move beyond experimentation. Instead of isolated pilots, the emphasis should be on embedding intelligence into real workflows where it can deliver measurable impact. This requires clear governance, defined decision boundaries and a strong orchestration layer.

Third, transformation strategies need to become more pragmatic. Large scale, multi year replacement programs are increasingly difficult to justify. A leave and layer approach allows organizations to start with what they have, extend it intelligently and deliver value incrementally.

Fourth, sustainability and compliance should no longer sit on the sidelines. By integrating these requirements directly into engineering and product development processes, manufacturers can turn them into a competitive advantage rather than a constraint.

Finally, organizations need to rethink how people and technology work together. As AI becomes embedded into operations, new roles, responsibilities and ways of working will emerge. Designing this deliberately is critical to making transformation succeed.

Think big with a clear vision for your organization, start small with one workflow that delivers immediate value, and scale fast once the approach proves effective.

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