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Low code development has hit a turning point for enterprise companies. As your competition embraces the benefits of developing at speed, it’s no longer a question of whether your company should adopt low code tools, but which ones will form the foundation of your future.

Among the top choices, Mendix and Microsoft PowerApps both promise faster building and less need for old-school coding. 

But which is better for your needs? 

This comparison guide looks at platform strengths, cost issues, and considerations you should make to help you choose the right base for your online plans.

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Short on time? Here's a brief overview

• Platform positioning: Mendix is a top-of-the-line platform for complex applications. PowerApps is a smaller, developer-friendly solution within the Microsoft world.
• Cost dynamics: PowerApps often appears “free” with Microsoft 365 licenses, but premium connectors and Dataverse can create considerable hidden costs. Mendix offers clear, though higher, upfront pricing.
• Use case fit: Choose PowerApps for simple workflows within Microsoft spaces. Pick Mendix for complex, integrated multi-device applications needing company-wide controls.
• Decision framework: Platform choice depends on app complexity, system alignment, control needs, and total cost beyond first licensing.

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Platform overviews

Mendix: Top-grade development platform

Mendix works as a unified, model-driven platform designed for the complete software building process. Its focus is visual app modeling, where the model itself becomes the app, so little to no code creation is needed.

The platform supports creating responsive web applications, progressive web apps, and mobile applications from a single model. Professional developers can extend features with custom Java actions and React-based widgets while mostly working within the visual building space.

The platform targets organizations building business applications that need wide system connections, complex business logic, and multi-year lifecycle management.

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PowerApps (Microsoft Power Platform): Citizen-developer-friendly tool

Microsoft PowerApps forms part of the broader Power Platform suite. It focuses on access for business users and citizen developers.

PowerApps’s main advantage for many companies is its tight connection with the Microsoft ecosystem. Organizations already using Microsoft 365, SharePoint, Teams, and Dynamics 365 can tap into existing data sources and user login without extra setup investment.

The platform’s low code method uses Power Fx, a formula language similar to Excel. This makes it accessible to users familiar with Microsoft’s productivity tools. However, this simplicity can become limiting as app needs increase in complexity. It often requires technical knowledge and professional developer help for advanced scenarios.

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Usability & developer experience

Mendix 

Mendix provides a dual-IDE (integrated development environment) method targeting different user types. 

Mendix Studio offers a simple web-based space for business users to create mockups and work together on needs. Meanwhile, the pro development experience centers on Mendix Studio Pro, a complete desktop IDE where complex applications are built. It features built-in visual modeling for data domains, business logic (microflows), and user interfaces within a single space. 

There’s also MxAssist, Mendix’s AI-powered development helper, which offers real-time tips and automates routine development tasks. The platform includes native debugging tools, automated testing frameworks, and Git-based version control.

The learning curve here is steeper than PowerApps. However, this complexity enables the creation of company applications that can scale to handle thousands of users and complex integration needs.

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PowerApps

PowerApps puts ease of use first through familiar Microsoft design patterns. Canvas apps use drag-and-drop interfaces similar to PowerPoint, while Model-driven apps automatically create interfaces based on data structures in Microsoft Dataverse.

Development happens mainly within the web-based PowerApps Studio, but building complete solutions often requires working across multiple interfaces: PowerApps for UI, Power Automate for business logic, and the Power Platform Admin Center for controls.

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Use case fit & application complexity

When Mendix excels

Mendix is best used in medium to high-complexity applications needing wide system connections, smart business logic, and multi-channel deployment. The platform’s model-driven design handles complex data relationships, detailed workflows, and performance-sensitive scenarios well.

It’s also excellent for customer portals connecting to SAP systems, mobile field service applications with offline capabilities, and case management systems spanning multiple departments. Additionally, the platform’s strength in native mobile development using React Native delivers top performance compared to hybrid alternatives.

Manufacturing companies like Nel Hydrogen have also used Mendix alongside product lifecycle management (PLM) systems to modernize product development processes.

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When PowerApps shines

PowerApps excels in simpler scenarios where quick deployment and smooth integration with the Microsoft ecosystem take precedence over app smartness. Form digitization, approval workflows, and data collection apps represent ideal PowerApps use cases.

The platform’s strength lies in making the most of existing Microsoft investments. Applications that mainly consume SharePoint data, integrate with Teams for collaboration, or extend Dynamics 365 functionality can be deployed quickly with minimal custom development.

Organizations with established Microsoft 365 user bases often default to PowerApps for department applications, taking advantage of included licensing and familiar user interfaces.

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Integration & ecosystem

Mendix 

Mendix is platform-neutral and designed for mixed company spaces. The platform delivers strong out-of-the-box connectivity to major company systems — especially SAP, Oracle, and Salesforce — through standard protocols such as REST, SOAP, and OData.

The Mendix Marketplace offers thousands of pre-built connectors and modules, too. These include a wide range of Amazon Web Services (AWS) and AI service connections. 

And for unique needs, Mendix’s Connector Kit enables professional developers to build custom connections using Java.

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PowerApps

PowerApps’s defining trait is its tight connection with SharePoint, Teams, Dataverse, and Azure services. This native connectivity enables quick development of applications that feel like natural extensions of the Microsoft productivity suite.

Standard connectors to Microsoft services are included with most Microsoft 365 licenses. The platform handles login, data sharing, and collaboration workflows without extra setup.

But this strength creates vendor lock-in issues. While PowerApps supports connections to non-Microsoft systems through premium connectors, these connections often lack the depth and performance tuning available for Microsoft’s own tools.

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Scalability, governance & CI/CD

Mendix 

Mendix embeds company controls as a base design principle. The platform’s Git-based version control enables pro development practices, including branching, merging, and team development workflows.

In addition, the built-in application lifecycle management (ALM) space includes Agile project management tools, automated testing frameworks, and CI/CD (continuous integration and continuous delivery/deployment) pipelines. 

Also, Mendix Pipelines offers native deployment automation, and the platform supports connection with external DevOps tools like Jenkins and GitLab.

Lastly, organizations can deploy Mendix applications to public clouds, private clouds, or on-site setups. This offers complete control over data location and performance tuning.

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PowerApps

PowerApps controls work through the Power Platform Admin Center and Data Loss Prevention (DLP) policies.

The Center of Excellence (CoE) Starter Kit offers control capabilities through PowerApps-built applications for watching adoption and managing the application portfolio. However, this needs significant setup and customization effort compared to Mendix’s built-in control tools.

With PowerApps, application lifecycle management relies on Solutions and external CI/CD tools, such as Azure DevOps. While powerful when properly set up, this method needs cross-platform knowledge and ongoing management of multiple tool chains.

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Cost & licensing models

Mendix 

Mendix uses subscription-based pricing starting around $998 monthly for single applications, plus per-user fees of $15 monthly. 

While some cite higher upfront costs as a drawback, Mendix’s pricing model provides a predictable total cost of ownership without usage-based surprises.

It also reflects Mendix’s positioning for pro development teams building complex applications. Organizations building multiple applications can benefit from unlimited application plans starting at $2,495 monthly, plus $15 per user per month. This makes per-application costs more competitive at scale.

Additionally, company customers can negotiate custom pricing for large deployments. Costs vary based on app complexity, user count, and deployment business requirements.

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PowerApps

PowerApps uses a tiered licensing model that appears accessible but can bring significant hidden costs as app complexity increases. 

Most Microsoft 365 licenses include limited PowerApps capabilities restricted to Standard connectors and SharePoint data sources. Premium connectors trigger per-user licensing needs for any app feature, regardless of how minimal the premium usage. Applications needing SQL Server connectivity, custom connectors, or Dataverse immediately require $5–20 per-user monthly licenses.

Further costs build up through Dataverse capacity ($40 per GB monthly for database capacity), API request overages ($50 monthly for 50,000 extra requests), and special features such as Power Pages for external portals. These usage-based add-ons can multiply initial cost estimates.

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Community & support ecosystem

Mendix 

The Mendix ecosystem centers on its curated Marketplace featuring thousands of pre-built components, connectors, and application modules. This marketplace enables code reuse and speeds development through shared community contributions.

In addition, Mendix Academy delivers free training materials and certification paths for developers at different skill levels. The platform’s AI-assisted development tools (MxAssist) also help developers follow best practices and accelerate everyday development tasks.

Mendix Pro support includes dedicated customer success teams and technical account management for company customers. This ensures successful platform adoption and ongoing tuning.

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PowerApps 

PowerApps benefits from Microsoft's massive ecosystem, with 56 million monthly active users across the Power Platform. This scale creates wide community resources, training materials, and third-party consulting knowledge.

Microsoft's support model varies significantly based on licensing options. Basic support is included, but premium support requires extra investment.

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Gartner & Forrester ratings 

Gartner analysis

Both platforms hold Leader positions in Gartner’s Magic Quadrant for Company Low Code Application Platforms. 

In 2024, Mendix scored highest on “Ability to Execute” for the second consecutive year. This reflects strong customer success and platform maturity.

Gartner Peer Insights shows similar user satisfaction: As of June 2025, Mendix maintains 4.5 stars with 297 reviews, while PowerApps achieves 4.6 stars with 342 reviews.

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Forrester analysis

Microsoft is listed as a Leader in Forrester's 2025 Wave for Low Code Platforms for Professional Developers. It achieves top scores for strategy and current offering. Forrester recognizes Microsoft’s AI-infused vision and company-scale capabilities.

Mendix ranks as a Strong Performer in the same analysis. It has particular strength in data modeling, project management connection, and version control capabilities for professional development teams.

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Which platform is best for you?

Choose PowerApps if:

• Your organization maintains deep Microsoft 365 standardization with SharePoint, Teams, and Dynamics 365 as core productivity tools.
• Your primary use cases are simple workflows, form digitization, and approval processes
• Development speed takes precedence over app power. You can accept platform limits for faster deployment.

Choose Mendix if:

• Your needs include complex integrated multi-device applications with smart business logic and wide system connections.
• Long-term application lifecycle management matters more than quick initial deployment.
• Connection needs span multiple vendor systems, especially SAP, Oracle, or other non-Microsoft company platforms.

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When might you use both?

Many large companies adopt both platforms strategically. PowerApps can handle department productivity needs and citizen developer initiatives, while Mendix takes care of core system modernization and complex customer-facing applications.

Note that this hybrid method requires clear control boundaries and connection strategies between platforms to prevent data silos and maintain design coherence.

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How CLEVR can support platform implementation 

In summary, PowerApps can maximize existing Microsoft investments and speed up smaller-scale development for contained use cases. Meanwhile, Mendix delivers company-grade capabilities for complex, mission-essential applications needing smart controls.

Your app complexity, connection needs, and long-term care responsibilities will dictate which is the better option. You should further evaluate these low code platforms and others based on total cost of ownership, control needs, and strategic alignment rather than initial licensing costs alone.

To support this evaluation — and, eventually, your platform implementation — CLEVR offers platform-neutral guidance backed by deep Mendix expertise. CLEVR focuses on understanding your business requirements, not promoting specific technologies, to ensure that whichever solution you choose integrates seamlessly with your current systems.

Want to learn more? Get in touch with CLEVR today.

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Research methodology

This analysis combines data from Gartner Magic Quadrant reports, Forrester Wave assessments, verified user reviews from Gartner Peer Insights, and full platform documentation. Cost analysis reflects publicly available pricing as of June 2025, supplemented by actual customer setups across various industries.

Robotics, digital twins, advanced automation, and emerging technologies such as generative AI are attracting immense investment across the manufacturing sector. Organizations are building increasingly connected ecosystems of data, platforms, and cyber-physical systems in pursuit of seamless interoperability and end-to-end visibility.

Yet for many manufacturers, these initiatives struggle to scale beyond pilots, stall during enterprise rollout, or result in standardized technology stacks that lack the flexibility to adapt to the unique workflows of each plant and operation. Recent Deloitte research confirms this paradox, citing mitigating operational risk, addressing talent and skills gaps, and aligning IT and OT priorities among the primary culprits.

But if the technology works, then why doesn’t the smart factory?

Smart manufacturing requires more than standardization

Industry case studies consistently demonstrate that smart factories are both achievable and capable of delivering measurable improvements in efficiency, quality, and capacity. The digital backbone reliably manages engineering intent, planning, costing, and execution control. The execution layer provides real-time operational visibility from machines and shop floor systems. And emerging technologies such as digital twins, IoT platforms, and AI further enhance performance through advanced analytics, simulation, and predictive intelligence.

However, organizations progress at different speeds, shaped by varying levels of digital maturity, technical capability, and transformation readiness. The breakdown rarely occurs within individual systems. It emerges between them, where workflows must connect engineering, planning, execution, and optimization into a coherent, end-to-end operating model.

Standardized platforms, while essential, are not designed to accommodate the full diversity of workflows, product variants, and governance structures that exist across plants and business units, making smart manufacturing more than just a technological adoption problem.

Where manufacturing process optimization breaks down

When workflows are not fully aligned, symptoms becomes visible across PLM, ERP, MES/MOM, and the shop floor, creating operational friction, slowing decision-making, and undermining the consistency of day-to-day execution.

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1. Engineering-to-production misalignment

In manufacturing environments, engineering updates a design, variant configuration, or Bill of Materials in PLM, but the change is not automatically reflected in MES work instructions or on the shop floor. Operators continue building to outdated specifications, while ERP planning still references previous routings or components. The result is rework, quality deviations, and delayed deliveries not because systems failed, but because the digital thread between PLM, ERP, and MES is incomplete.

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2. Planning vs. execution gaps

ERP releases production orders based on forecasted capacity and inventory assumptions, yet real-time constraints (like machine availability, tool wear, or labor allocation) are only visible in MES or on the shop floor. Without a synchronized workflow between ERP and MES/MOM, planners operate on outdated data while production teams manage exceptions manually.

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3. Shop floor visibility without enterprise integration

Sensors and machine data provide rich operational insight, but deviations captured on the shop floor do not consistently trigger structured workflows in ERP, quality management, or service systems. Maintenance teams may see alerts, yet spare parts planning, cost tracking, or customer communication remain disconnected.

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4. Service feedback not closing the loop

For machine builders in particular, insights from installed machines (such as performance data, recurring faults, configuration issues, etc.) are not systematically fed back into engineering in PLM. As a result, product improvements rely on informal communication rather than traceable, data-driven workflows across the lifecycle.

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5. IT/OT governance misalignment across systems

IT teams standardize architectures across PLM, ERP, and enterprise systems, while OT teams prioritize uptime and local production stability in MES and shop floor environments. Without clearly defined cross-system workflows, integrations stall, exceptions bypass governance, and digital initiatives lose credibility.

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Low code manufacturing workflow orchestration: connecting PLM, ERP, and MES/MOM and shop floor integration

Positioned on top of existing PLM, ERP, MES/MOM, and shop floor systems, low code enables manufacturers to connect their digital backbone, execution layer, and optimization technologies into one coordinated operating model.

By acting as the connective tissue between systems, low code transforms technical interoperability into operational interoperability, ensuring:

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1. Real-time decision activation across PLM, ERP, and MES

Engineering changes in PLM can automatically update ERP planning parameters and MES work instructions, enabling synchronized execution instead of manual reconciliation and delayed corrections.

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2. Closed-loop production and service feedback

Machine data, quality deviations, and field performance insights can trigger structured workflows back into ERP and PLM, creating a continuous improvement loop rather than isolated reports.

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3. Operational dashboards tailored to roles and plants

Low code enables plant managers, planners, and service teams to access unified, role-specific dashboards that combine ERP, MES, and shop floor data, supporting faster, data-driven decisions in daily operations.

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4. Exception-driven workflow automation

Instead of relying on emails or manual escalations, deviations in production, inventory, or machine performance automatically initiate traceable workflows across systems, reducing response time and execution risk.

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5. Variant and configuration management aligned with execution

For machine builders, product variants and custom configurations can be reflected consistently from PLM through ERP to shop floor systems, minimizing rework and delivery delays.

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6. Scalable integration without disrupting core systems

Manufacturers can extend ERP, PLM, and MES capabilities incrementally, adding new workflows and use cases as business needs evolve, without destabilizing their existing technology landscape.

Build your smart factory with the right strategic implementation partner

Low code does far more than connect systems. It enables manufacturers to operationalize data across the entire product and manufacturing lifecycle, turning insight into structured, measurable action.

From engineering and planning to production and service, low code strengthens how information flows across the organization. And at CLEVR, we partner with manufacturers to translate that potential into tangible business outcomes.

With 30+ years of experience in the Siemens Xcelerator portfolio and advanced low code application development, we bridge strategy and execution, connecting proven industrial platforms with the flexibility required to adapt to evolving operational demands. We begin by defining where value can be unlocked across the operational chain, then design and implement tailored workflows that connect PLM, ERP, MES/MOM, and shop floor systems. Rather than forcing your organization into rigid templates, we use Mendix—the leading enterprise low-code platform—to build orchestration layers aligned with your specific processes, governance model, and growth ambitions.

This approach allows manufacturers to:

• Align PLM, ERP, MES/MOM, and shopfloor processes around shared outcomes.
• Leverage existing Siemens Xcelerator components while extending them where standard functionality stops.
• Handle exceptions and deviations consistently across teams and systems.
• Evolve workflows incrementally as operations, products, and strategies change.

Smart factories are built on aligned workflows

Smart factories are not defined by the technologies they adopt, but by how well workflows align people, systems, and decisions. Until that alignment exists, even the most advanced digital initiatives will struggle to deliver lasting impact.

With the right strategic implementation partner, however, manufacturers can overcome these challenges, align systems with business ambitions, and tailor operations to the specific performance goals they set for growth, efficiency, and innovation.

If you are ready to move beyond isolated initiatives and build a truly connected manufacturing environment, contact us for a consultation to explore how your organization can unlock measurable operational value.

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 organisational 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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