Sandeep Sakharkar, former CIO of GXO Logistics. Previously was in tech leadership roles at J&J and Footlocker.
Since the term was coined roughly two decades ago, technologies and applications in the B2B software as a service (SaaS) space have become pervasive across organizations of all sizes spanning every industry.
Almost all organizational process areas have a vast choice of B2B SaaS products available in the market. The choice and proliferation of these technologies are immense and have resulted in the convergence and M&As of SaaS companies over the years.
These SaaS products have almost always been sold as prepackaged applications that deliver “best practice” processes. Vendors differentiate through features and functionalities or by catering to specific industry domains.
The notion of “configuration vs. customization” to get the best from these applications became popular. Users had to conform to the best practices these solutions defined. One question has always been asked: How can different organizations, even in the same industry, work in the same manner and follow the same processes using SaaS? At a tactical level, things can be done in similar ways. However, isn’t there a unique working process DNA that makes one organization different from the next?
A new possibility is now emerging to challenge the notion of SaaS as we know it: “service as software” (S’aaS). It is more than just wordplay; it’s the fast-emerging possibility that every organization can define, create and establish unique business processes that instill their uniqueness into how business functions.
This evolution has started quietly but strongly due to significant advancements in AI, particularly GenAI and LLMs over the recent years. Unlike its predecessor, this new paradigm emphasizes AI-driven workflows that adapt to the unique process needs of organizations—bringing together software, data, people and machines evolving toward an autonomous enterprise.
The Shift From SaaS To S’aaS
Cloud-based SaaS has been a way to deliver ready-made solutions where customers pay for use without having to deal with installation, infrastructure or maintenance. These solutions, while scalable, often require businesses to conform to the software’s capabilities. In contrast, S’aaS flips this model by offering a flexible platform where the core is not prepackaged but a highly adaptive digital framework.
With S’aaS, the technology adapts to the business—not the other way around. This approach typically takes form through data and process-centric agentic AI capabilities that drive process automation specific to organizations’ needs.
S’aaS is best described as a hybrid approach where transactional applications, data and business process improvement initiatives converge to create tailor-made enterprise solutions. At the core, the focus is on two pillars: purposeful data management and continuous business process management using autonomous AI technologies.
Organizations leveraging this approach implement a digital framework that integrates their business processes, with AI-driven workflows and agents working behind the scenes to continuously optimize operations. A digital nervous operating system for the organization.
Establishing strong data governance is the core foundation, which then couples with process automation using AI agents. The opportunities for where agentic process workflows can be used need holistic exploration first followed by subprocesses and tactical tasks. The key question to ask is what value generation is expected from the autonomous AI capabilities, with “human in the middle” where needed.
Aspects of using the right models (LLMs or SLMs), getting the right approach to retrieval, augmentation and generation (RAG), and rules around exceptions and risk management need to be outlined up front. Also, thought must be given to design principles that will take into account the need for data persistence and having a system of record.
If deployed correctly, such digital platforms can reduce or even eliminate the need for some costly packaged solutions—replacing them with agentic workflows and processes. This approach can also eliminate the disconnect between data management and process improvement initiatives.
The use of AI/ML, GenAI, LLMs and SLMs is central to the S’aaS paradigm. These technologies allow for platforms to “learn” from business processes and suggest changes that improve efficiency, agility and quality of work. AI isn’t just about automating tasks anymore; it’s about understanding the data that the business generates and making the insights from that data work harder to bring positive change—contributing directly to speed, efficiency and ultimately profitability or revenue.
GenAI is already playing an increasingly significant role in use cases such as document processing, natural language interactive bots and computer vision-driven automation. These AI models can automate repetitive tasks and offer recommendations based on real-time data, continually refining business processes. These capabilities are now quickly progressing to more complex use cases of agentic AI.
The Future Of The Enterprise
As with any technology-driven disruption, the technology itself is the somewhat easy part as it matures. However, how does one get started with managing this transition in the enterprise? Four principles work well:
1. Enhancing the digital (AI) IQ of the organization across the board. From management to functional leaders and operators, it pays to understand the latest shifts that AI and LLMs bring. Having an AI evangelist role helps—someone who understands the business as well as they understand AI.
2. Generating use case ideas from across the organization, and collating and governing them centrally. The best ideas can come from any part of the organization; however, they need to be stack ranked for value evaluation and validating against strategic goals so that the best ideas get picked first for execution.
3. Have a clear data strategy that encompasses elements of how organizational data is managed—including master data, data classification, taxonomy and metadata, needs around synthetic data, and data security.
4. Focused execution and change management. Ensure agile execution with testing and value realization measurement. AI-driven processes will impact the way work happens as their use gets adopted for more complex processes. This calls for careful change management and communication to bring along employees. It helps to have a skilled vendor partner organization to support.
As businesses adopt S’aaS models, the innovation potential is vast. The capabilities of these platforms can make them attractive to enterprises facing complex process challenges. Adaptive, intelligent platforms that become the autonomous operating systems for business look poised to define the coming decade.
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