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SaaS security
Adaptive Access Technologies advanced technologies AI Advancements ai agents AI Models AI productivity AI technology Cyber Security LLMs saaS SaaS security

AI and the Rise of Service-as-a-Service: Why Products Are Becoming Invisible

 

The software world is undergoing a fundamental shift. Thanks to AI, product development has become faster, easier, and more scalable than ever before. Tools like Cursor and Lovable—along with countless “co-pilot” clones—have turned coding into prompt engineering, dramatically reducing development time and enhancing productivity. 

This boom has naturally caught the attention of venture capitalists. Funding for software companies hit $80 billion in Q1 2025, with investors eager to back niche SaaS solutions that follow the familiar playbook: identify a pain point, build a narrow tool, and scale aggressively. Y Combinator’s recent cohort was full of “Cursor for X” startups, reflecting the prevailing appetite for micro-products. 

But beneath this surge of point solutions lies a deeper transformation: the shift from product-led growth to outcome-driven service delivery. This evolution isn’t just about branding—it’s a structural redefinition of how software creates and delivers value. Historically, the SaaS revolution gave rise to subscription-based models, but the tools themselves remained hands-on. For example, when Adobe moved Creative Suite to the cloud, the billing changed—not the user experience. Users still needed to operate the software. SaaS, in that sense, was product-heavy and service-light. 

Now, AI is dissolving the product layer itself. The software is still there, but it’s receding into the background. The real value lies in what it does, not how it’s used. Glide co-founder Gautam Ajjarapu captures this perfectly: “The product gets us in the door, but what keeps us there is delivering results.” Take Glide’s AI for banks. It began as a tool to streamline onboarding but quickly evolved into something more transformative. Banks now rely on Glide to improve retention, automate workflows, and enhance customer outcomes. 

The interface is still a product, but the substance is service. The same trend is visible across leading AI startups. Zendesk markets “automated customer service,” where AI handles tickets end-to-end. Amplitude’s AI agents now generate product insights and implement changes. These offerings blur the line between tool and outcome—more service than software. This shift is grounded in economic logic. Services account for over 70% of U.S. GDP, and Nobel laureate Bengt Holmström’s contract theory helps explain why: businesses ultimately want results, not just tools. 

They don’t want a CRM—they want more sales. They don’t want analytics—they want better decisions. With agentic AI, it’s now possible to deliver on that promise. Instead of selling a dashboard, companies can sell growth. Instead of building an LMS, they offer complete onboarding services powered by AI agents. This evolution is especially relevant in sectors like healthcare. Corti’s CEO Andreas Cleve emphasizes that doctors don’t want more interfaces—they want more time. AI that saves time becomes invisible, and its value lies in what it enables, not how it looks. 

The implication is clear: software is becoming outcome-first. Users care less about tools and more about what those tools accomplish. Many companies—Glean, ElevenLabs, Corpora—are already moving toward this model, delivering answers, brand voices, or research synthesis rather than just access. This isn’t the death of the product—it’s its natural evolution. The best AI companies are becoming “services in a product wrapper,” where software is the delivery mechanism, but the value lies in what gets done. 

For builders, the question is no longer how to scale a product. It’s how to scale outcomes. The companies that succeed in this new era will be those that understand: users don’t want features—they want results. Call it what you want—AI-as-a-service, agentic delivery, or outcome-led software. But the trend is unmistakable. Service-as-a-Service isn’t just the next step for SaaS. It may be the future of software itself.
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Technology
AI document declassification AI in CIA AI in national security AI productivity generative AI government use Technology

US National Security Leaders Embrace AI to Declassify Documents, Boost Productivity, and Reimagine Talent

 

Artificial intelligence is quickly becoming a powerful force within U.S. national security agencies, streamlining time-consuming tasks and boosting efficiency—from document declassification to enterprise-level productivity enhancements.

“We have released tens of thousands of documents related to the assassinations of JFK and Senator Robert F. Kennedy, and we have been able to do that through the use of AI tools far more quickly than what was done previously, which is to have humans go through and look at every single one of these pages,” said Director of National Intelligence Tulsi Gabbard during the AWS Summit this week.

Gabbard noted that AI deployments are aligned with the agency’s broader objective: ensuring timely access to information. Besides declassification, she cited practical implementations of AI in functions like human resources and internal systems. Notably, an AI chatbot has been rolled out agency-wide, unlocking further possibilities for innovation.

“We’ve made progress, but there’s so much room for growth and more application of AI and machine learning,” Gabbard said.

Agencies remain cautious, however, implementing checks and human oversight to maintain accuracy, compliance, and effectiveness. While the technology shows great promise, it is still evolving, and leaders are aware of the risks tied to generative and autonomous AI tools.

“There are things to be concerned about,” said Lakshmi Raman, Director of AI at the Central Intelligence Agency, referring to potential issues like model or data drift, lack of tool explainability, and concerns around trust.

Private sector leaders share these concerns, according to a recent Cloudera survey. Respondents expressed a need for improved data privacy and security in current AI agents and noted integration and customization as ongoing challenges.

Despite these obstacles, AI adoption continues to rise. Across industries—from retail giants like Walmart to automotive leaders like Toyota—companies are turning to AI to supercharge operations.

In the intelligence space, similar excitement is brewing over agentic AI tools.

“With [concerns] in mind, being able to gather data from multiple spaces and leverage AI agents for cognitive aids is incredibly exciting,” Raman said.

The emergence of generative AI is also reshaping how agencies approach talent development and organizational transformation.

“We think of it at three different levels: our general workforce, which might be the most important user base with those people who are sitting side by side with AI practitioners,” Raman said. “Then we think about it for our practitioners, so they are really keeping up with the latest. And finally, our senior executives, who can think about how they can transform their organization with AI.”

She emphasized the need for talent that blends technological fluency with analytical skills.

“When we’re thinking about analysts, for example, we’re thinking about people who have critical thinking skills, who can demonstrate analytic rigor, who can think multiple steps ahead with incomplete information,” Raman said. “And we’re also looking for people who have digital acumen with understanding of cloud, cyber and AI.”

Gabbard added that this moment calls for employees to reassess existing procedures and identify areas for process improvement.

“A lot of this comes for some folks, who’ve been working in the community for a long time, with a change in culture and a change in … education,” Gabbard said.

One significant area of transformation is the accreditation and authorization process.

“You can imagine a cybersecurity analyst who traditionally has gone through network data very manually in order to block suspicious IP addresses or connections,” Raman explained. “Now, there’s an opportunity to do all of that really easily.”
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