Background – 2
Manav pointed me to two interesting columns. Decibel VC writes about “Service as Software, powered by AI Agents”:
For every dollar we spend on software, we end up spending 1.5x times more on IT services to get that software operational and successfully deployed. This includes areas like BPO, outsourcing and consulting, which take up most of that extra cost. You could argue that that number is potentially even understated since a lot of companies use FTEs to do the manual work around tuning, maintaining, integration and operational work. Bain & Company suggests that around 37% of IT tasks could be automated using genAI.
…With the rapid innovation around AI Agents, software applications as we know them are in process of getting a major upgrade. Agents are programs that can make decisions or perform actions based on its environment, user feedback or experiences. In short, they mimic the subtle human behavioral characteristics that make us humans quite effective at taking actions or fulfilling tasks. What this means in practice is that instead of software simply being delivered to us as a service, agents and LLMs will power the service to be delivered to us in the form of software. Thereby this ushers in the Era of Service-as-Software.
… these agents to truly be disruptive and enduring—ready to be deployed at scale—they must be built with a vertical-first and maybe almost role based first strategy (and potentially scale horizontally from there). This approach isn’t just about achieving early wins; it’s about solving specific use cases while also aligning with already existing budget line items in the enterprise.
When considering which ideas to pursue first, the guiding star should be the urgency of labor need—roles that are hard to recruit for would allow agents to be adopted at a higher rate and provide a faster journey towards product-market fit.
This graphic from Decibel VC tells the story:
Sarah Tavel writes in a post titled “Sell Work, Not Software”:
For the past 25 years, application software startups have had a singular focus: increasing company and employee (including developer) productivity. This looked like building software that increased productivity at the employee level, increased collaboration across employees and teams, and/or enabled better oversight and management at the leadership level. More often than not, this software has been priced on a per seat basis, in essence benchmarked against the cost of the headcount itself and increasing that headcount’s productivity.
Enter Large Language Models (LLMs). The first tranche of products and startups leveraging LLMs has kept within the mental model of selling software to achieve step-function improvements in end-user productivity. The “Copilot for [x]” trend reflects this mental model. While there are fantastic startups innovating to improve employee productivity, LLMs create an opportunity for startups to look beyond this way of thinking and discover surface area that previously was out of bounds for selling software given the required GTM and pricing limitations of software. To do this, rather than sell software to improve an end-user’s productivity, founders should consider what it would look like to sell the work itself.
More: “One of the core hypotheses behind “selling work, not software” is that when you sell a 95% productivity improvement (vs squishier productivity improvement you sell against with software), you are able to charge *substantially* more for your service than you would have otherwise been able to if you sold software on a per seat basis. This can mean that the same end markets could be 10-50x larger than your software-model intuition would tell you.”
Emergence Capital writes: “Service businesses that leverage both AI and humans to deliver holistic solutions to clients are poised to outgun and outpace the services behemoths that’ve dominated for the last 50 years. Legacy providers are ripe for disruption: their business models rely on human labor and hourly billing which can be turned on its head by AI-enabled vendors. Further, their “product” (humans) is very difficult to integrate AI into. While software incumbents are having a relatively easy time adapting to the GenAI wave, services incumbents will struggle. Founders, this is a boomtown moment to seize. For a long time now, founders pursuing software exclusively have been strip mining for the same, highly competitive gold. There’s a bounty to be had by the few companies building disruptive AI-enabled services.”
Collectively, these insights suggest a future where the lines between software and services blur, with AI playing a central role in redefining value delivery in the IT and software industries. This shift emphasises outcomes over outputs, and tailored, AI-driven solutions over generalised software tools, marking a significant evolution in how businesses and enterprises approach digital transformation and operational efficiency. Think of this as the new SaaS: software AND services bridged by AI agents.
