Service-as-a-Software: Driving a shift from ‘software’ to ‘services’

Forward-looking companies used software for decades to streamline workflows, drive productivity, and fuel their business growth. Artificial Intelligence further made software more powerful and personalized.

However, now a new technological innovation is buzzing the market - Service-as-a-Software (SaaS). Unlike traditional Software-as-a-Service, the new SaaS enables companies to employ self-reliant AI agents powered by Agentic AI to take up functions which once required significant human effort.

Using these automation AI agents is poised to transform the future of work across industries. The early adopters will be able to build a competitive advantage by achieving increased automation and customer satisfaction while simultaneously reducing their heavy reliance on skilled talent.

Introduction

For decades, leading-edge companies have invested in cutting-edge software solutions to drive innovation, productivity, and business outcomes. The software enables these companies to automate streamline workflows and drive productivity. The move to the Cloud and the rapid development in the Software-as-a-Service market further unlocked new possibilities.

Now what’s really exciting about the technology landscape is it continues to evolve and come up with new solutions - this time, it is the emergence of ‘Service-as-a-Software.’

Unlike the old SaaS model, which is about tools to accomplish talks at a rapid pace and with reduced errors, the new SaaS, or Service-as-a-Software, is about delivering concrete outcomes with autonomous AI agents. These outcomes could take various shapes such as more human conversations with the website visitors, and scheduling appointments.

In other words, while the conventional Software-as-a-Service (SaaS) model helped companies streamline operations and drive productivity, the new SaaS (Service-as--a-Software) model significantly puts the software (the term is ‘AI agents,’ in charge of the entire process with minimal or no human supervision.

Let’s look at a few more examples to better understand what we are discussing. Let’s start with Sierra, an intelligent conversational AI. Forward-looking B2C companies increasingly use AI, especially Agentic AI apps like Sierra, to connect with customers on their websites. Sierra is not just any other chatbot—it’s much more. It talks with customers and helps them fix their issues.

Since these new-age agents are designed to be self-reliant and seamlessly integrate into a company’s core systems, such as CRM or ERPs, they become increasingly efficient at handling customer queries and resolving them without requiring human interaction. Other noteworthy self-reliant AI agents that are creating a buzz in the market include Harvey, Devin, Claude (Anthropic), and Nabla AI.

Let’s have a quick look at how some of these AI agents (there are many more out there) are altering the future of work.

Technologies behind Agentic AI

Agentic AI is ‘a lot of technologies working together to perform a range of tasks which were earlier done by only a skilled workforce.’ The core technologies powering the growth of Agentic AI include Machine Learning, Large Language Models (LLMs), and Cognitive architectures.

1. Machine Learning (ML)

AWS defines Machine Learning as “Artificial intelligence that performs data analysis tasks without explicit instructions. Machine learning technology can process large quantities of historical data, identify patterns, and predict new relationships between previously unknown data.”

Machine Learning is one of the core technologies that enable the birth and evolution of Agentic AI, and thus the overall shift toward ‘Services-as-a-Software.’

2. Cognitive architectures

A cognitive architecture is the sum of all the structures and interactions that define a human mind. In artificial intelligence and Cognitive Science, these architectures are used to develop more robust and intuitive AI models with the core essentials such as perception, memory, action, and reasoning.

3. Large Language Models (LLMs)

Large Language Models, or LLMs, are a class of foundational models designed to understand and generate human language. These models are trained on large amounts of data (such as text, video, images, etc.) and are capable of producing coherent and contextual relevant responses to human queries (which we call ‘prompts’). Though tech giants have long worked with these LLMs to build systems and applications, they gained unprecedented recognition with ChatGPT. Yes, LLMs form the core of these new-age GenAI apps and systems. These LLMs are now supercharging the ‘Service-as-a-Software’ revolution by enabling Agentic applications to more humanely and intelligently connect and converse with customers.

In addition, AI companies are tapping into a slew of other cutting-edge technologies, such as deep learning, computer vision, and robotics.

Corporate examples

We must keep in mind that we are in the initial phase of Agentic AI or Service-as-a-Software era. Expect tremendous growth and advancements in the next couple of years - however, even at the current level of AI, there are hundreds of companies out there leveraging AI-based solutions to automate service and consulting work:

• Hilton: A global leader in hospitality, Hilton recently partnered with Be My Eyes to introduce AI-powered solutions for its customers who are either blind or have low vision. The AI-powered solutions are engineered to ensure a more accessible and welcoming stay experience for these customers across the US and Canada.
• DHL: DHIL is another global organization that is betting heavily on AI. Currently, it is leveraging AI-powered apps and solutions to streamline routes, predict service issues, and use insights to drive delivery times and operational efficiency. As AI becomes increasingly efficient and self-governed, DHL will likely invest more strategically to employ it to achieve desired outputs rather than just assist its manpower.
• Delta Air Lines: Delta is among the leading airline companies that have significantly invested in AI and a host of other cutting-edge technologies to boost its infrastructure and customer experience. Its efforts have been duly recognized as it has been named one of the most innovative companies worldwide by Fast Company for two consecutive years. Among the numerous initiatives Delta Air Lines has undertaken over the years, the most prominent ones include the first end-to-end biometric terminal at Atlanta’s International Airport. The airline recently introduced Delta Concierge, an AI-powered digital tool that will further improve the flying experience of its costumes.

How Agentic AI is enabling this shift toward ‘Service-as-a-Software’

Agentic means the ability to act independently or the power to achieve outcomes independently. And Agentic AI, a class of AI that enables autonomous decision-making, forms the heart of the revolution we call ‘Service-as-a-Software.’

The AI agents, some of them we mentioned early on, represent the specific implementations of Agentic AI and are the ground-level warriors in this new technological movement that’s attracting attention from all kinds of brands and businesses. These self-reliant agents leverage reinforcement learning and real-time feedback loops, continuously refining their decision-making processes to enhance efficiency and accuracy. For instance, a customer service AI agent like Sierra learns from each interaction, improving its problem-solving skills over time without requiring human intervention.

Interestingly, we’re in the very early phase of this groundbreaking innovation! Given the rapid advancements across data, Cloud, and AI, the near-future version of these AI agents will be a lot more powerful, thoughtful, and capable. And nearly every industry - from healthcare to insurance and manufacturing to retail - benefits from these independent and self-learning agents. 

However, each and every technology comes with its own set of limitations or challenges. And this is true for this new revolution as well. Despite its unprecedented advantages, Agentic AI also throws a lot of challenges. Some of the major challenges include ethical concerns around biased decision-making, data privacy risks, and the current limitations of AI in handling nuanced human interactions. To address these issues, today’s companies must implement robust governance frameworks and ensure ethical AI practices while embracing this technological shift.

Final thoughts 

The need for innovation never ends in the business world. Companies moved from on-prem to the Cloud to achieve increased agility, scalability, and resilience while benefiting from reduced costs. Today, these companies are increasingly riding the next wave of innovation - Services-as-a-Software, driven by Agentic AI and a slew of other cutting-edge tools and technologies.

Under the new SaaS model, a growing number of autonomous and self-learning AI agents are building a new future of work. Furthermore, these agents exhibit goal-directed behavior, meaning they can dynamically adjust their decision-making strategies based on real-time feedback. They employ reinforcement learning to continuously improve their responses and optimize for better efficiency over time. As businesses transition toward Service-as-a-Software, embracing AI agents can unlock unprecedented efficiency and customer satisfaction. At Kellton, we help companies navigate this shift with tailored AI & ML solutions. If you're ready to explore how AI agents can drive outcomes for your business, connect with our AI team today!

Please note: SaaS is a whole new ballgame for tech-driven organizations. To help companies move faster and generate more meaningful results while reducing operational costs, we plan to create many more content assets in different forms - think ‘webinars,’ ‘emailers,’ ‘videos,’ ‘whitepapers,’ and ‘blogs.’

We’ll update all these details right here.

So, stay tuned for more in-depth conversations on the NEW SaaS!