Looking Back at 2018: Did AI in Additive Manufacturing Deliver on Its Promise?

In 2018, we explored how AI and machine learning were poised to revolutionise additive manufacturing. We looked ahead with optimism - predicting smarter, faster, and more autonomous production workflows enabled by real-time data, AI-powered monitoring, and tighter human-machine collaboration.

Seven years later, it's time to revisit those predictions. What came true? What surprised us? And what has Authentise learned along the way?

What We Got Right: AI Is Here — and It’s Working

✅ Real-time in-print monitoring became reality: Back in 2018, we cited research from Kansas State University on machine learning-based print monitoring. Today, systems like those are not only real - they’re essential. At Authentise, we’ve embedded similar capabilities into our platforms, helping customers detect anomalies, track deviations, and drive continuous improvement directly from the build chamber.

✅ AI augments human work, not replaces it: We also challenged the fear that AI would displace the manufacturing workforce. Instead, we suggested a collaborative future - one where AI supports human decision-making. That vision has held strong. From automated quoting and job scheduling to predictive maintenance and digital thread creation, AI is accelerating productivity by working alongside people, not replacing them.

✅ Supply chain roles evolved - not vanished: As the HBR piece from 2018 predicted, supply chain roles haven’t disappeared. In fact, they’ve become more strategic. Today’s professionals are managing AI-augmented data flows, sustainability mandates, and complex global logistics. At Authentise, our tools now help track materials and production data across entire supply chains - ensuring traceability and transparency at scale.

What Surprised Us

🧠 AI adoption was slower than hype predicted: While AI made strides, widespread, integrated use in manufacturing came slower than expected. Many SMEs struggled to scale pilot projects or lacked the infrastructure to deploy AI meaningfully. That’s why we've focused on building modular, scalable, and API-driven tools that help manufacturers transition at their own pace.

🔄 The need for contextual, connected data became paramount: What’s become crystal clear is that AI without context is noise. It’s not enough to monitor data - you need the ability to trace it back to intent, design decisions, and quality outcomes. That realisation is what led us to develop Authentise Threads: a platform designed to contextualise every manufacturing decision and link it across the product lifecycle.

🌍 Sustainability emerged as a driving force: AI’s role in sustainability wasn’t a major talking point in 2018. But in 2025, using AI to reduce waste, energy use, and production cycles has become a core business strategy. From material reuse planning to digital twins that track environmental impact, we’re proud to be part of that shift.

What We’ve Learned

1. Digital transformation isn’t a single leap - it’s a journey. Manufacturers who started early are now scaling AI confidently. Those who waited can still catch up, especially with modular platforms like ours that connect the dots without overhauling entire systems.

2. Human expertise remains irreplaceable. AI shines when paired with skilled engineers, designers, and planners. It’s the augmentation, not automation alone, that unlocks real gains.

3. Data is your most valuable asset - if you can trust and trace it. From monitoring to compliance to optimisation, data drives everything. But without the right systems to manage its flow, it can do more harm than good.

   
   

What's Next for ai in additive manufacturing?

The next chapter of AI in additive manufacturing isn’t about replacing workflows. It’s about making them smarter, more agile, and more collaborative than ever. With advances in edge computing, multi-source data fusion, and automated compliance, the road ahead looks more promising than ever.

And Authentise will be right there - helping you write the next chapter!

Want to know how we’re using AI to streamline the AM workflow today? Check out: How Software is Streamlining the AM Workflow