Are market forecasts missing the AI factor?

The market forecasting industry is underestimating how quickly artificial intelligence is reshaping industry growth. Forecast models often focus narrowly on historical trends within a single sector, without fully accounting for how advances in AI disrupt assumptions about productivity, adoption rates, and competitive dynamics. As AI capabilities mature and embed themselves into workflows, industries may grow far faster and in fundamentally different ways than expected.

Additive manufacturing (AM) illustrates this risk. Current projections place the global AM market at about $22 billion in 2024, with forecasts ranging from $84 billion to $145 billion by 2034, depending on adoption scenarios and forecasting firms. These figures reflect healthy momentum. Yet, they assume traditional constraints such as incremental progress in design, planning, and material development will dictate the pace of adoption. 

AI is already loosening those constraints. Accelerating product design through generative optimization, streamlining production planning, and shortening qualification timelines for new materials are all happening now. Forecasts that fail to incorporate these accelerating forces risk underestimating the true potential of the AM industry.

What traditional forecasts miss

Market forecasts are typically built on a foundation of historical data, trend analysis, and incremental assumptions. Analysts look at past growth rates, adoption patterns, investment cycles, and macroeconomic indicators to project future performance. These models often assume that constraints seen in the past will continue to act as friction on growth, even as technologies improve. In AM these include skill shortages, production bottlenecks, or qualification timelines.

This approach works reasonably well in stable or mature industries, where new capabilities take years to diffuse through supply chains, workforces, and customer bases. It also helps manage risk by presenting a conservative, defensible picture of likely outcomes. However, it tends to underestimate the impact of transformative technologies. AI is the quintessential disruptive technology; it is already altering the very constraints forecasts assume to be fixed.

When a technology doesn’t just improve outputs but removes a bottleneck entirely, the forecast quickly diverges from reality.  An example is replacing weeks of manual engineering work with minutes of algorithmic design. The more disruptive the technology, the more likely that traditional methods will miss its acceleration effect.

Examples of how AI changes assumptions in AM

In additive manufacturing, several entrenched assumptions shape most forecasts:

• Design processes are slow and resource-intensive, limiting how quickly AM can move beyond prototyping.
  
• Production planning and scheduling are complex and often prevent full utilization of AM equipment.
  
• Material discovery and qualification are lengthy, iterative processes that delay the introduction of new applications.
  
• Quality assurance for AM parts remains expensive and inconsistent, further slowing adoption.
  

AI is already beginning to address these assumptions, with concrete examples emerging across the AM ecosystem:

• Generative and topology optimization tools: Software platforms like nTopology and Siemens’ NX with generative design modules use AI-driven algorithms to produce highly optimized, manufacturable AM-ready designs. These tools reduce engineering time and expand the design space beyond human intuition.
  
• AI-driven production planning and scheduling systems: Companies such as 3YOURMIND offer AI-enabled software to identify, prioritize, and schedule parts for AM, helping manufacturers integrate AM into their workflows more efficiently.
  
• Machine learning applied to materials science: Research initiatives like the U.S. Department of Energy’s Materials Genome Initiative and companies such as Xerox PARC have demonstrated ML models that predict material behavior and optimize material formulations for AM, reducing qualification timelines.
  
• AI-enabled inspection and simulation tools: Quality assurance solutions from companies like Sigma Additive Solutions (formerly Sigma Labs) employ machine learning to analyze in-process monitoring data, detecting defects in real time to improve yield and consistency.

These advances, while still maturing, are no longer hypothetical. They are being deployed in pilot projects and early production settings, with measurable effects on cost, speed, and quality. Yet most forecasts continue to assume the old constraints remain in place. As a result, projections for AM’s growth may already lag behind the pace of change AI is enabling.

Implications for Decision-Makers

For executives, investors, and engineers in the AM space, the gap between traditional forecasts and AI-enabled reality is more than an academic concern; it carries real strategic risk. Underestimating the pace at which AI removes traditional constraints could result in missed opportunities, underinvestment, or a failure to build competitive advantage in time.

Several specific implications stand out:

• Investment timing: Organizations that plan capital expenditures based solely on mid-range forecasts risk falling behind competitors who recognize how AI accelerates adoption curves. Early investment in capacity and capability can position companies to capture growing demand sooner.
  
• Business model evolution: Service bureaus, hardware manufacturers, and software providers must reassess their offerings in light of AI-driven workflows. As design and planning become faster and less labor-intensive, demand patterns may shift, particularly for services.
  
• Talent planning: AI tools reduce the need for specialized human expertise in certain areas, but also create demand for new skill sets: data-driven design, AI-assisted quality control, and integrated production planning. Workforce development strategies will need to adapt accordingly.
  
• Competitive landscape: The companies that most effectively leverage AI to eliminate bottlenecks will be best positioned to capture market share. Waiting for forecasts to “catch up” to this reality risks ceding leadership to more agile players.
  

In other words, leaders who use forecasts as static guides rather than as scenarios bounded by assumptions risk making decisions on outdated premises. Recognizing and challenging those assumptions is key to staying competitive. 

In all of this, Joy’s Law is the constant: “No matter who you are, most of the smartest people work for someone else.” 

The final word

Forecasts are useful tools only if their assumptions hold. In industries like additive manufacturing, where near-term AI is already erasing traditional constraints, leaders should treat forecasts as starting points, not endpoints. The real risk lies not in overestimating what AI can achieve, but in underestimating how soon it will change the rules.

Email me to uncover what your forecast misses: randall@consiliavektor.com