Samuel Greengard, Contributing ReporterFebruary 25, 20255 Min ReadDenys Rudyi via Alamy StockOver the last decade, software as a service (SaaS) has reshaped the face of business. Low-cost and highly flexible applications have become the norm, and more agile and scalable IT frameworks have followed. Today, organizations large and small use powerful software that would have once been out of reach.Now, as artificial intelligence takes hold, the concept is evolving. Service as software is rapidly taking shape. It promises to add powerful capabilities. Service as software uses the core principles of both SaaS and business process outsourcing (BPO) delivery models. It blends them into a new, AI-powered framework, explains Fred Giron, senior research director at Forrester Research.Service as software, also referred to as SaaS 2.0, goes beyond layering AI atop existing applications. It centers on the concept of automating business processes through intelligent APIs and autonomous services. The framework aims to eliminate human input and involvement through AI agents that act and react to conditions based on events, behavioral changes, and feedback.The result is autonomous software. Traditional SaaS provides cloud-based tools where staff still do the work. Service as software flips that script. Instead of having staff do the work, you're making calls to an API or using software that does the work for you, says Mark Strefford, founder of TimelapseAI, a UK-based consulting firm.Related:The approach is particularly promising for handling niche, well-defined processes. This includes financial reviews, legal analysis, IT reporting, marketing and public relations reviews, and general research. Although service as software remains in its infancy -- and there are caveats about deploying it -- its likely to introduce further change to the enterprise. Giron believes that it could surpass the SaaS revolution.Beyond BotsDialing up productivity is at the foundation of any successful business. Yet, despite waves of software automation and increasingly sophisticated AI tools, manual processes still flourish within most organizations. Service as software aims to fill critical gaps by expanding the concept of cloud-based platform delivery.A growing number of vendors are stepping into the service as software space. The list includes Klarna, Moonhub, Thoughtful Automation, Crescendo AI, Converzai, Adept and Inflection AI. These firms typically provide pre-engineered agents designed to handle discreet tasks. Some include voice-enabled interfaces and interactions.Early adopters are already using these tools to tackle niche tasks that typically revolve around document processing, medical transcription, and automated invoice processing, Strefford says. These use cases frequently harness unstructured data that resides in documents, messages, images, and various types of forms and build it into structured, actionable information.Related:In other words, service as software does the work itself rather than providing tools for humans. It goes beyond simply scanning data and looking for matches or patterns. It determines what to do with the information, Strefford explains.For example, AI-driven accounting software can automatically categorize transactions, file taxes, and monitor compliance. AI-powered marketing and sales can identify leads, craft personalized messages, and autonomously schedule calls or demos with interested prospects. AI-enabled content creation can draft market research reports, legal summaries, or product descriptions based on raw data.SaaS 2.0 is possible because AI systems have advanced and converged in recent years. Although generative AI and large language models have grabbed recent headlines, machine learning and deep learning have also advanced. LLMs have enabled service as software, says Strefford, but traditional machine learning algorithms are still massively valuable, especially for predictive analytics and workflow optimization.Related:Not surprisingly, combining these separate AI components produces a sum greater than the individual parts. As Giron explains, AI continuously analyzes interactions, learns from successes and failures, and refines its performance over time. This continuous learning loop ensures that service delivery becomes more intelligent, personalized, and effective.Smarter AIA key benefit of a service as software model is that it can greatly simplify AI adoption -- while automating 50 to 70% or more of interactions, Giron says. Rather than building complex AI models in-house, an organization can turn to a pre-packaged solution that delivers pre-designed AI-driven workflows. As with conventional SaaS, updates and patches occur continuously.The result is an ability to access new features and capabilities as the service as software provider introduces them. This creates a continuous learning and optimization loop that promotes a more intelligent, personalized, and effective work model, Giron says. SaaS 2.0 also supports a strategic framework that prioritizes measurable business outcomes and performance metrics.Nevertheless, human oversight remains vital -- at least for now. Strefford promotes a three-tiered model, particularly as organizations become acquainted with the space and launch pilot projects. He recommends fully automating low-risk tasks; using human-AI collaboration for medium-risk activities; and maintaining human-led processes for high-value or high-risk operations.It all comes down to trust, Strefford states. You have to understand what the possible costs and repercussions are if a system makes an incorrect prediction or takes an incorrect action? Not surprisingly, these considerations vary by organization and industry, and business and IT leaders should factor in regulatory requirements, board confidence, geopolitical events, and overall risk tolerance.CIOs and IT leaders should start small and iterate, experts say. As an organization gains confidence and trust, it can expand the autonomy of a SaaS 2.0 component. More AI initiatives have failed from starting too big than too small, Strefford notes. Consequently, its critical to understand the entire workflow, build in oversight and protections, establish measurement and validation tools, and stay focused on outcomes.A few factors can make or break an initiative, Giron says. Data quality and the ability to integrate across systems is crucial. A framework for standardization is critical. This includes cleaning, standardizing, and preparing legacy data. Data labeling and annotation can be a time-consuming and resource-intensive task. It can demand specialized expertise and tools, he says. At the same time, its important to identify and address potential biases in data and focus on security and regulatory risks.Over the next few years, Giron says that service as software will reach into contact centers, IT services, human resources, supply chain, and other operational domains where service quality and cost efficiency matter. The business world, he says, will fully embrace SaaS 2.0. It will lead to managed services that arent merely offshored or outsourced but, instead, are continuously optimized, AI-infused, and laser-focused on business results.About the AuthorSamuel GreengardContributing ReporterSamuel Greengard writes about business, technology, and cybersecurity for numerous magazines and websites. He is author of the books "The Internet of Things" and "Virtual Reality" (MIT Press).See more from Samuel GreengardNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also LikeWebinarsMore WebinarsReportsMore Reports