Propel Software predicts AI will bolster SaaS firms

Propel Software has issued five business and technology predictions for 2027, focused on artificial intelligence, software and manufacturing.

It forecasts that AI will strengthen rather than displace software-as-a-service providers, while healthcare delivery will continue shifting from clinical facilities into patients' homes. It also argues that manufacturers will need to rebuild their technology stacks around cleaner data and stronger system connections if they want AI to deliver consistent operational results.

Ross Meyercord, Chief Executive Officer of Propel Software, outlined the forecasts as companies assess how AI is moving from pilot projects into routine business use. The themes span enterprise software, industrial operations and medical technology, with a common focus on how organisations manage data, workflows and decision-making.

AI and SaaS

One of Propel's main predictions is that concern about AI replacing SaaS platforms will fade as the two become more closely linked. AI providers are likely to gain more by working with established SaaS vendors, while those vendors use AI to improve customer-facing products and internal processes.

Propel draws a distinction between platforms with connected data and embedded workflows, and those that rely on isolated AI features added after the fact. In its view, providers that bring together information, process flows and AI planning are more likely to benefit from the next stage of adoption.

Human oversight

Propel also predicts a shift in the role of employees overseeing AI systems. Rather than keeping people continuously involved in each decision, many organisations are expected to move towards a model in which AI handles established, repeatable work and humans review records afterwards.

That approach depends on experienced specialists transferring their knowledge into systems and documented processes. Routine work is expected to be increasingly marked as AI-generated and supported by audit logs that allow periodic checks, while human staff continue to set standards, design new processes and deal with exceptions.

Home healthcare

Another area highlighted in the forecasts is healthcare, where the shift from hospitals and ambulatory surgery centres into the home is expected to continue. Propel links that change to ageing populations and wider use of wearables, remote monitoring, connected implants and diagnostic tools that patients can use outside clinical settings.

This is expected to put pressure on medical technology groups to design products for continuous use away from traditional facilities. Propel adds that such a model requires reliable product data, joined-up teams and a clear record of changes across the product lifecycle, particularly as incoming data volumes rise beyond what manual teams can easily review.

Manufacturing systems

For manufacturers, Propel forecasts a change in spending priorities. Instead of layering AI tools on top of weak foundations, companies are expected to invest more in the underlying systems and data structures needed to support AI in daily operations.

That includes cleaning data, linking different sources and strengthening systems of record such as product lifecycle management and application lifecycle management software. These platforms can give AI access to information such as bills of materials, quality records, product changes and service history, providing the context needed to complete practical tasks.

Propel also points to Model Context Protocol connections as one tool for linking systems, but says such connections cannot make up for missing data or gaps in core functionality. In its view, manufacturers will increasingly direct investment towards improving data quality and system structure rather than adding isolated AI products.

Everyday use

The final prediction is that AI will be judged less by high-profile demonstrations and more by its effect on day-to-day work. Manufacturers in particular are likely to focus on repetitive tasks that span multiple systems, including engineering hand-offs, verification work and delivery processes.

Measures of success will shift towards shorter cycle times, lower rework, smoother delivery and reduced cost per change. That would mark a move away from evaluating AI projects by the attention they attract and towards assessing them against operational metrics such as throughput, quality, risk and margin.

"The SaaSpocalypse narrative will give way to a new reality: AI and SaaS grow stronger together," said Ross Meyercord, Chief Executive Officer of Propel Software.

"Executive teams will judge AI by its impact on throughput, quality, risk, and margin, not the attention a pilot attracts," Meyercord said.