Three years ago, during the height of the Covid pandemic, McKinsey conducted a global survey of 899 C-level executives. Titled How COVID-19 has pushed companies over the technology tipping point, the survey found that businesses had accelerated the digitization of their customer and supply-chain interactions and of their internal operations by three to four years. The share of digital technology products added to portfolios had been accelerated by seven years.
On the surface this sounds great. But could the speed be a problem? One company exec told me recently that his firm has five ways to access documents shared across the business. This is typical of workflows that rely on multiple communications tools, where files are too often moved between platforms via email links.
The rush for digital tools, in many cases relatively new software-as-a-service (SaaS) applications, has had drawbacks as well as benefits, according to Mark Simon, VP of strategy at iPaaS (integration platform-as-a-service) firm Celigo.
“What we’re seeing across the engineering industry is an explosion in SaaS usage,” Simon told engineering.com. “These applications tend to be highly specific, using best-of-breed technology to solve narrow problems that larger, more established platforms like Xcelerator or 3DExperience are not always able to address. The issue then becomes the data silos that are created any time a new application is added to the tech stack.”
Digitalization is often treated as a remedy for data silos, but if not approached carefully it can simply exacerbate the issue. Enterprises serious about digital transformation must be strategic about their tool selection—and keep an eye on AI.
Putting data security at risk
When digital systems don’t communicate with one another, Simon says, it often leads to increases in manual tasks. Employees have to pick up the pieces and move data to and from core platforms. This is inefficient, and it also opens the door to human error—which can lead to project error, delays and cost overruns.
It can also lead to data security breaches. A Thales Global Cloud Security Study earlier this year found that human error actually accounted for 55% of data breaches among its respondents. Even if individual SaaS apps are themselves secure, it’s clear that any SaaS strategy needs to be carefully planned and connected, even if that means taking a step back and revisiting past SaaS deals and policies.
“[SaaS] can minimize customization options and many teams have to change the way they work to fit with restrictions,” says Michael Corr, CEO and co-founder of Duro, a SaaS Product Lifecycle Management (PLM) platform for hardware development. Corr says he founded Duro specifically to facilitate digital transformation for design engineering and manufacturing processes.
It echoes an IDC report on enterprise application growth, which revealed earlier this year that while the SaaS market will see a five-year compound annual growth rate of 8% by 2026, SaaS demands a shift in approach.
Mickey North Rizza, group vice president for enterprise software at IDC, said in the report that “the digital world is completely altering the way software is utilized and incorporated into the organization, from modularity to APIs, to low code/no code, to business process automation.”
Towards a smooth SaaS transition
The necessity of SaaS changing the way people work is not bad, but it does need managing. As Simon at Celigo says, even the smallest issues can escalate and be felt across an entire organization, wiping out any productivity gains the application could or should have delivered.
“Businesses need to be thoughtful when adding SaaS applications and ensure they have an integration strategy that links these platforms back to their core, foundational platform of choice,” adds Simon. “Companies need their systems to be interconnected if they hope to maximize their SaaS investments—little good comes from a fractured network of applications.”
IDC’s 2023 SaaSPath Survey found that, across 23 applications and 2,875 respondents, 51% of organizations on average are keeping their current applications, while 46% are planning to replace their current systems within the next three years.
“Hold on to your hats folks, the enterprise application world is shifting and the best is yet to come,” says North Rizza in the report. But it’s not so much the scale of software options that is interesting, it is the new wave of AI-driven software tools—including generative AI-enabled applications.
Keeping AI in mind
For Rhonda Dibachi, founder and CEO of manufacturing pricing engine HeyScottie, there’s one main criterion by which SaaS applications need to be judged.
“By far the biggest factor—in my mind the only factor—in regards to choosing the right solution is to determine how serious they are about AI,” says Dibachi. “These systems handle your crown jewels.”
By “crown jewels” Dibachi means customer data. She suggests that AI can have a real impact in analyzing that data and delivering insights and ideas that previously would not have been possible. She gives the example of using AI for return merchandise authorizations (RMA) root cause analysis, with automatic updates to design rules and constraints to make sure mistakes are not repeated.
“How about tweaking designs for maximum margin?” asks Dibachi. “How about automatic design iteration to optimizing multiple design parameters? All these can theoretically be accomplished with AI, and are almost impossible to do efficiently without it.”
Dibachi is a big fan of SaaS. She sees it as the only real way to transform efficiently into a modern, viable engineering business, adding that the embedded design process standards and workflows help companies implement best practices in new product development and R&D. With AI doing the heavy lifting, it makes for a more efficient use of resources.
The human engineering element
Steve Massey, co-founder and CEO of engineering software developer Prewitt Ridge, warns about SaaS perception and reality. A major challenge in digital transformation, Massey says, particularly in hardware engineering, is that the human element is still extremely important when designing a product.
“No amount of software can replicate the level of attention and collaboration required for designing intricate systems,” he tells engineering.com. “When customers think of a SaaS product in the context of digital transformation, there’s a misconception that the product will instantly improve their work environment.”
Massey adds that unfortunately, many engineering platforms overpromise on their capabilities and often fail to meet expectations. He offers some advice: “When purchasing software, organizations should be clear about their expected results. No single platform can meet all engineering needs, so it’s important to identify inefficiencies within an organization and find the right software to address them.”
This applies to SaaS more than anything, due largely to its growing availability and proliferation within industry. SaaS is a software delivery model, not a policy. As the old saying goes, a good worker never blames the tools—but in this case, unless organizations scale back, avoid complexity and data silos and find the right software to meet their specific needs, there may be some truth to it.