As the sun set over the vibrant Chicago skies, the organizers of the International Sports Expo, unlike past years, were beaming with joy with this year’s seamless ticketing, security and fan engagement experience in the run up to the event.  Like each preceding year, organizers faced a formidable challenge- to coordinate multiple technologies across many vendors to ensure patrons get a good experience. Very often, the experience was rocky owing to frictions between disparate technologies and there was a latent need for a technology that could unify and orchestrate patron journeys to deliver delightful outcomes while abstracting this technology maze from the patrons.

Enter Agentic AI and the power of collaboration

Sometime back, the organizers of the “International Sports Expo” attended an Agentic AI conference and were inspired by the potential of this technology. They decided to partner with an Agentic AI firm to create a seamless experience that spanned campaigning, ticketing, engagement, security and merchandise promotion. Come the event, with Agentic AI at the forefront, disparate technologies and vendors, who so far only addressed a small portion of the holistic fan experience, came together in a unified system and were abstracted suitably from the end customers. The customers only had to deal with the agent and the agent used its autonomous decision-making skills to bring together the systems at the backend and serve the needs of the customers. Several AI orchestration platforms such as Google Cloud AI Platform, Pega platform, Microsoft Azure AI Orchestration and IBM Watson Orchestrate are enabling such unification of disparate platforms.

Such cohesive integration enabled the fans to enter the expo effortlessly with facial recognition ticketing, while IoT sensors ensured their safety and overall experience. Fans received loyalty points proportionate to the energy they exuded during the games. They received exclusive offers and customized notifications about upcoming events. The event offered a glimpse into the future of events and customer service, - where technology and emotions coalesced to give out the best experience. This was “Service as Software” (SaS) in action.

Shift from “Software as a Service” (SaaS) to “Service as Software” (SaS)

The advent of Agentic AI is orchestrating this tectonic shift in the technology industry, from SaaS toward SaS. While SaaS revolutionized software delivery, SaS focuses on providing intelligent, autonomous services through software platforms.

SaaS is a means to automate a portion of the journey toward a goal. For example, to open a bank account, a BPM tool streamlines the “Know Your Customer” workflow, a CRM tool takes care of customer relationships, and a digital signature tool ensures seamless data integration and secure document handling. In this example, each SaaS tool plays a finite role and contributes its bit towards the whole experience. It has no control on giving a unified, holistic experience. The experience is as good or bad as the integration between the systems.

However, in an Agentic AI powered, SaS world, the agent will abstract all these technologies under its hood. It is equipped to gather the customer data autonomously, verify identity through biometric identification and complete compliance checks in real-time. It can also provide personalized recommendations for financial products based on customer profiles. Such a service enhances the customer experience by reducing the wait times and providing tailored services while ensuring security and compliance.

This is where HCLTech’s pioneering work on automating KYC for a leading European bank using SaaS platforms can be transformed in the SaS world.

The power of Agentic AI

While SaS is powered by Agentic AI, the power of Agentic AI lies in its ability to subsume and abstract various technology components to provide a unified experience to the end users. Some of the salient capabilities that allow it this power are:

1. Autonomous decision making: Agentic AI systems use sophisticated algorithms to autonomously decompose tasks, design workflows and execute tasks, reducing the need for human oversight. E.g.: Autonomous Cars
2. Integration of modular components: They integrate real-time search tools, logic validators, and specialized databases to achieve complex, multi-step tasks with precision and agility. E.g.: Smart Thermostats, Smart Lights
3.  Contextual understanding and planning: They use advanced natural language processing to comprehend intent and context, allowing them to set goals, plan actions and execute them effectively. E.g.: In-car assistants, Siri, Alexa
4. Continuous learning and adaptation: They harness machine learning and reinforcement learning to continuously learn from interactions and feedback, optimizing their performance over time. E.g.: Netflix, Spotify, YouTube
5. Workflow optimization: They orchestrate various agents under the hood and streamline processes across domains such as retail, healthcare and finance, enhancing productivity and operational efficiency through intelligent automation. Agentic AIs can be trained in any domain, in the case of the above example, they were trained in sports event management, customer engagement, crowd management and security protocols. E.g.: Amazon’s AI powered Supply Chain

Opportunities for all key stakeholders in the technology ecosystem

Automated supermarket checkouts that allow customers to scan and pay without cashier assistance; smart shelving in retail where IoT and AI converge to ensure product availability, provide personalized recommendations and enhance the overall shopping experience are all good examples of SaS in action.  Likewise, Amazon’s “Just Walk Out” technology and automated passport gates at the airports are great examples of the growing transition from SaaS to SaS.

This evolution, which is primed to reach its optimal adoption by 2030 (Hfs Research), opens up opportunities for all key stakeholders. While enterprises can leverage SaS to optimize operations and reduce costs with autonomous systems, IT service integrators will do well to develop Agentic AI solutions and bake them into their services to enhance client services. Likewise, Technology vendors can rise from SaaS and address the use cases holistically by integrating their products with agentic capabilities for competitive advantages.

Looking ahead: Key considerations

While Service as Software has tremendous promise to amplify the service experience and boost enterprise efficiency, it will be interesting to track how the commercial models shape up and play an enabling role in its adoption. As this model scales up, it will be crucial to bring industry protocols and guardrails to standardize the solutions and keep them within the realms of ethics, responsible & explainable AI and privacy.

Optimal change management will be needed to transition from SaaS to SaS. It is here that HCLTech’s recent partnership with Inspeq AI is a notable case in point. Through this collaboration, Inspeq AI’s leading RAI Ops platform will synergize with HCLTech’s vast experience in IT infrastructure and enterprise solutions, enabling organizations to adopt AI solutions that are not only efficient but also ethically sound and compliant with global standards, effectively setting a solid foundation to adopt the SaS future. Approaches such as differential privacy, homomorphic encryption and a conscious “human in the loop” policy will ensure this ecosystem matures in a responsible and policy compliant manner.

You can delay it but not deny it. Embracing Services on Software with Agentic AI will be imperative to stay competitive in the digital age, offering a seamless blend of efficiency and experience.