The Great AI shift: The rise of ‘services as software’

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Human effort has traditionally driven the adoption of technology services—programmers coding algorithms and business logic into executable programmes, analysts managing data, system integrators deploying and maintaining infrastructure, and consultants optimising enterprise workflows. However, with the advent of artificial intelligence (AI), a fundamental shift is underway: human-led services are becoming AI-led software. Instead of hiring large teams of human specialists, businesses will increasingly incorporate AI-powered tools and platforms that automate complex tasks once thought impervious to automation and digitalisation.

Instead of hiring large teams of human specialists, businesses will increasingly incorporate AI-powered tools and platforms that automate complex tasks once thought impervious to automation and digitalisation.

The AI-driven shift is compelling enterprises to reimagine fundamental work at an unprecedented pace. It raises several critical questions about what happens to human labour when AI replaces labour-intensive services, how AI-driven automation will redefine the relationship between enterprises and the software they adopt, and what new opportunities will emerge in a paradigm where enterprise-grade AI takes centre stage.

From SaaS to 'Services as Software'

The traditional Software-as-a-Service (SaaS) model disrupted enterprise Information Technology (IT) by replacing expensive, on-premise software solutions with cloud-based applications. Databases were maintained by the SaaS providers remotely, and the per-seat license model evolved rapidly to annuity payments and the rise of Annualised Recurring Revenue (ARR). SaaS has dominated the technology world for the better part of two decades. Today, AI is pushing the envelope by turning services built to be used by humans as ‘self-serve’ utilities into automatically-running software solutions that execute autonomously—a paradigm shift the venture capital world, in particular, has termed ‘Services as Software’

AI tools like Harvey AI are transforming the legal and compliance sector by analysing case law and generating legal briefs, essentially replacing human research assistants.

The shift is already conspicuous across industries. AI tools like Harvey AI are transforming the legal and compliance sector by analysing case law and generating legal briefs, essentially replacing human research assistants. The customer support ecosystem that once required large human teams in call centres now handles significant query volumes daily with AI chatbots and virtual agents. Tools like OpenAI's Code Interpreter catalyse industries operating on data analysis and business intelligence by incorporating AI-driven analytics that replace manual data wrangling. Unlike traditional SaaS, which assists human workers, these AI-driven tools replace labour-intensive processes entirely and generate outcomes with minimal human interventions. ‘Services as Software’ therefore eliminates the human + SaaS unit that serves outcomes and replaces this unit with a full-stack AI-led automation that delivers the same outcomes.

Convergence of a technological leap and urgent business requirements

Several tailwinds have converged globally to accelerate this shift:

1. AI's rapid advancement: The step-function accomplishments of large language models (LLMs) and generative AI have made it possible for software to organise, reason, and execute tasks once exclusive to human effort.
2. The democratisation of AI: Open-source AI models and Application Programming Interface (API)-based infrastructure solutions are making cutting-edge automation accessible to businesses of all sizes. Previously, this level of technology access was only available to mega-cap enterprises. Perplexity's launch of Deep Research, which offers near-enterprise calibre AI services at retail prices, is already challenging the AI industry's subscription and paywall formats by driving costs down even further.
3. Enterprise cost pressures: Businesses are actively adopting AI-driven tools to reduce operational expenses and vastly improve efficiency, particularly in high-cost service functions that usually incorporate a significant human labour component.
4. Talent churn and generational shifts: In particular, as the US Baby Boomer generation (born between 1946 and 1964) retires and the architects of today's systems leave the workforce, and as cross-border technical talent flows are challenged in a new regime of de-globalisation, enterprises are starting to use emerging AI capabilities to rebalance their human workforce requirements.

As a result of these aforementioned factors, there has been a global surge in AI-first companies building products and trained models that automate significant components of services-based businesses, and replace traditional approaches to work entirely. 

Businesses are actively adopting AI-driven tools to reduce operational expenses and vastly improve efficiency, particularly in high-cost service functions that usually incorporate a significant human labour component.

Case studies: AI replacing labour-intensive services

1. Customer service and the end of call centres: For decades, outsourcing customer support to human agents dominated the customer service industry. Today, AI-powered voice assistants and chatbots are disrupting the space. Bank of America's Erica AI handles over 2 million customer interactions daily, surpassing 2 billion interactions since its launch in 2018, dramatically reducing reliance on human agents. Airlines and e-commerce companies are increasingly using AI chatbots to resolve queries without human intervention. As generative AI conversational agents improve, which they are at an unprecedented pace, entire call centres could be replaced with AI-driven agents, making routine customer service a fully automated function with higher-order management entailing human effort.
2. Software development and AI copiloting: Software engineering was once thought impervious to automation, but AI-powered agents are rapidly proving this wrong. These tools today perform complex coding tasks. Examples include GitHub Copilot—which helps developers code faster and reduce manual effort, and Cognition AI's Devin—an AI software engineer capable of building and debugging applications on demand. It is unlikely that developers will be replaced entirely; however, AI will dramatically reduce the need for extensive engineering teams, instead transforming software development into a more automated workflow.
3. Legal and financial analysis: These services traditionally relied on high-cost white-collar professionals but are now disrupted by AI agent-driven automation. Law firms are utilising AI legal assistants for research and contract drafting tasks hitherto reserved for junior associates. AI-powered financial models can now analyse company balance sheets, detect fraud, and even generate investment analysis reports, all at a fraction of the time and cost of human professionals. As AI continues to learn, these white-collar jobs with high hourly costs will increasingly become automated.

The AI-driven shift brings into question the traditional notion of availing an ‘expert service’. Software development,legal, and financial services are all coveted industries where workers are considered ‘experts’ delivering specialised services. The human role will undergo tremendous redefinition and will require calibrated re-skilling.

The new enterprise stack: AI benches and agents running sprints

Historically, enterprises purchased software systems that human professionals then produced their work on top of, with these professionals being the primary drivers of work. These software systems became ‘Systems of Record’ (SOR), with enterprise knowledge and memory encapsulated within them, and business continuity preserved via their persistence. With AI now capable of directly engaging with these SORs, human work is becoming secondary, and this shift is fundamentally re-engineering how businesses consume technology.

Businesses will rely on AI-driven quality assurance and control instead of outsourcing software testing, Quality Assurance, and Quality Control.

Businesses won't simply replace SaaS with AI-powered tools; they will build the company's processes and systems around these new systems. Instead of hiring marketing agencies, companies will use AI to generate dynamic marketing and advertising campaigns. Businesses will rely on AI-driven quality assurance and control instead of outsourcing software testing, Quality Assurance, and Quality Control. Instead of manual IT support, enterprises will deploy AI-driven monitoring and cybersecurity agents. AI agents will run Human Resource processes and travel bookings. Every business computer will host dedicated AI agents to monitor human productivity and run real-time coordination of enterprise resources, and calendars.

The human role in an AI-First future of the enterprise

Despite AI's rapid rise and the potential churn in enterprise workflows, processes, and onboarding of labour, human contribution will continue to remain essential in several key areas:

1. AI oversight and governance will remain a human domain. AI systems must be monitored for ethical concerns, regulatory compliance, and bias correction. Humans will have to ensure responsible AI implementation.
2. Creativity and strategic thinking remain human domains. AI can generate insights, but human leaders and collaborators will drive innovation, interpret business contexts, and make strategic decisions.
3. Humans and AI will collaborate in specialised fields. Doctors will use AI for patient monitoring and diagnosis but will continue to oversee patient care. Lawyers will leverage AI for research but still handle litigation. Scientists will incorporate AI into research discovery, hypothesis generation, data analysis, and simulation but define research objectives and experimental paths.
4. Customer experience and relationship management encompass complex problem-solving and trust-building. AI cannot replace these functions but can handle transactional interactions and customer attention acquisition, while escalations will still require humans.
5. Human supervision is necessary for AI training and maintenance, including debugging, updating, and refining data to prevent outdated and incorrect outputs.

AI will reshape roles instead of replacing human expertise entirely, just as prior technologies like desktop computing, cloud, mobile, and app-led SaaS did. Human-AI collaboration will become the new normal.

AI will not just assist service-based industries, but also replace traditional workflows with automated alternatives.

The next phase of AI-led automation

The shift from labour-intensive services to AI-driven software is already underway with profound implications. AI will not just assist service-based industries, but also replace traditional workflows with automated alternatives. However, rather than eliminating human roles, AI will redefine them, and possibly adjunct them. The most successful businesses will combine AI's efficiency with human judgement, creativity, and oversight.

As AI becomes the core of enterprise technology, businesses that embrace automation will gain a significant competitive edge. The question is no longer if services will become software but how quickly industries will adapt—and what new opportunities will emerge. The companies that lead this transformation will define the next era of global technology—one in which AI and human expertise work in tandem to push the boundaries of innovation.

Nisha Holla is a Visiting Fellow at the Observer Research Foundation

• Cyber and Technology
• Artificial Intelligence
• India
• AI SaaS
• ai saas for business
• ai saas model
• arificial Intelligence Software As A Service
• saas model
• software as a service model

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