At Rōnin Consulting, we specialize in helping clients with large, complex codebases. One of our biggest challenges is efficiently onboarding these clients, basically getting our engineers up to speed on intricate systems so we can deliver value quickly.

That’s where Claude Code shines, especially with its “Codebase Q&A” feature, which has become one of my favorite tools for speeding up this process.

My identity crisis in the age of AI 

As a software engineer with 30 years of experience, I’ve witnessed the evolution of our craft, from punch cards and BASIC on a Commodore 64 to the rise of AI-driven development.  

The tools have changed, but the core of why I became an engineer has always been the same: the thrill of building something with my hands, mind, and code.

A wake-up call from Annie Vella

So, when I read Annie Vella’s article, The Software Engineering Identity Crisis, it hit me hard. It’s a wake-up call about how AI is reshaping not just how we work, but who we are as engineers. In her article, she says:

Many of us became software engineers because we found our identity in building things. Not managing things. Not overseeing things. Building things. With our own hands, our own minds, our own code. But that identity is being challenged. AI coding assistants aren’t just changing how we write software – they’re fundamentally transforming who we are.”

This resonates deeply.

For decades, I’ve taken pride in crafting elegant solutions, debugging gnarly problems, and seeing my code come to life. But now, AI tools are taking over chunks of that process, turning us into orchestrators, or dare I say, managers, of systems rather than hands-on creators.

Loss of identity or a shift in scope?

It’s not just AI, though. Vella points out an irony that stings:

For years, we’ve said that software engineering transcends mere coding… Yet the industry pushed us in the opposite direction.”

She’s right. Over my career, I’ve seen so many roles fragment. Where once I might have owned a project from concept to deployment, specialization carved up those responsibilities.

Now, AI threatens to commoditize the one piece we could hold on to: coding itself.

But there’s hope: reclaiming the role

Don’t worry because Vella doesn’t leave us in despair.

In her follow-up article, Dear Software Engineer: It’s Time to Reclaim Your Role, she offers a path forward.

She notes that AI is already reducing the time we spend coding, with 78% of 165 engineers surveyed across 28 countries reporting they spend less time on it due to AI tools. She discussed the new paradigms that are emerging like CHOP (Chat-Oriented Programming) and BATON (Bot-Assisted Task Orchestration).

In these new models, we collaborate with AI to build software rather than writing every line ourselves. We work together.

For someone like me, who’s seen the industry through multiple transformations, this feels like both a challenge and an opportunity.

But the challenge is real, and after 30 years doing what I love, the idea of stepping back from coding to “orchestrate“ AI feels like losing a piece of my soul.

I didn’t sign up to be a manager. I signed up to build.

And yet, Vella’s argument that AI could let us reclaim the broader scope of engineering is compelling.

By offloading routine coding to AI, we can focus on:

• Designing resilient systems,
• solving complex problems,
• engaging with stakeholders and
• creating solutions that truly matter.

Staying relevant in an AI world

This shift also raises questions about relevance.

Vella warns that engineers who remain solely focused on coding, or “traditional engineers,“ are at risk.

In contrast, “product engineers“ who participate in all aspects of development are better positioned to thrive.

Over the last decade, specialization led to roles like Product Owners, Business Analysts, Designers, etc., leaving many engineers focused only on coding, making them vulnerable to AI.”

As a veteran, I’ve been lucky to work on projects end-to-end, and I see now how that breadth of experience is a strength. If anything, it’s a reminder to younger engineers not to get pigeonholed.

So, what does this mean for us?

For me, it’s a call to adapt, but let me be clear: I’m no stranger to adjusting on the fly. I’ve learned new languages, frameworks, and methodologies over the years. AI is just another notch in my belt.  

Tools like Codeium’s Windsurf or Cursor don’t replace our skills; they amplify them. I can still be a builder; I now use AI as a partner instead of writing every line by hand.

A divisive shift and a path forward

This isn’t without controversy.

On platforms like X and LinkedIn, debates rage about whether AI will augment or replace engineers.

My take?

It’s both a tool and a shift. The key is how we respond.

We can resist AI and cling to the old ways. Or we can lead from the front and show the world how to blend human ingenuity with AI’s power.

To my fellow veterans and the next generation

For my fellow veterans, this is our moment. We’ve navigated Y2K, the dot-com bust, and countless tech waves.

We have the experience to:

• guide teams
• mentor juniors 
• shape how AI integrates into our craft

There has never been a better time to be in our shoes.

Now is the time to step up as leaders and bridge the gap between what was and what’s next. We’ve worked our way through the thick of it, and it’s only made us wiser. Now, we can help chart the course, ensuring AI doesn’t just rewrite code but rewrites it with purpose.

For you newer engineers, it’s a chance to learn from us veterans. Soak everything up like a sponge, and push the boundaries of what’s possible. Because the more you push, the more opportunity opens up. Challenge assumptions, question the defaults, and don’t wait for permission to innovate.

Just do.

What’s next for us in this age of AI?

Vella’s call to “reclaim your role“ is a rallying cry. It isn’t just for one group of engineers but for all of us.

Let’s broaden our skills.

Let’s re-engage with the full software lifecycle.

Let’s use AI to enhance, not replace, our creativity.

Let’s stop writing and start doing it.

I’ll start.

Become indispensable infrastructure (tool/API provider).

Your SaaS should ensure it offers robust APIs or other machine interfaces because agents will be operating on behalf of humans and need programmatic access​. A product that is not easily accessible to an autonomous agent will be skipped. Forward-thinking SaaS companies are already moving this way. For example, Stripe maintains a world-class API for humans and machines alike. They also built an infrastructure for AI consumption alongside their human-facing app to stay competitive.  

This dual approach – one product for human users and one for agent use – might become the standard. SaaS firms should evaluate how to expose every significant feature via API or plugins, even those that historically required a GUI. By positioning themselves as the best “tool” in a particular domain, a SaaS can ensure agents favor it for tasks, preserving usage. 

Embracing the infrastructure/tool role means that a SaaS must let go of the notion that users must see your interface. Some vendors fear losing customer engagement if automation replaces their UI. But trying to trap users in your UI is a mistake. 

In practice, this might also involve offering new integration formats (e.g., being part of popular agent frameworks, providing SDKs, supporting open agent standards) so your SaaS becomes a default building block in AI-driven workflows. 

Embed intelligence or provide it.

SaaS companies can also move into the intelligence layer by deeply infusing AI into their products. We can already see instances of this with the recent wave of built-in assistants (e.g., Salesforce’s Einstein GPT and Adobe’s Firefly in Photoshop).   

Offering an AI copilot inside your SaaS can serve two purposes: it makes your product AI-enhanced for end-users and trains the company to operate an LLM in its domain. Over time, a SaaS provider could develop proprietary models or fine-tuned AI that become its own competitive advantage.  

For instance, a SaaS with years of specialized data might fine-tune an AI that outperforms general models for specific tasks. This could effectively become a niche “intelligence” provider in its domain.  

Moreover, AI-first design is key. Rather than bolting AI on as an afterthought, rethink your application as if an AI is a primary user. Ask yourself these questions: 

• How would you redesign workflows so an agent can easily navigate them? 
• Are there internal optimizations or data pipelines you can expose to AI? 
• Can you structure your application to seamlessly collaborate with AI, allowing it to make proactive decisions and enhance user interactions? 

SaaS teams should refactor rigid logic into AI-accessible modules. This might mean transitioning some functionality from code-based rules into model-driven policies that an agent can adjust. As a venture technologist advised, “Companies must evolve from traditional architectures to AI-first platforms, enabling agents to interact seamlessly with their tools.”​ 

Own a piece of the control layer. 

Another strategy is to build the agent or orchestrator for your domain. Suppose your company has deep domain knowledge (say, project management or marketing operations). In that case, you might create an AI agent that orchestrates tasks in that domain, essentially offering “X- Automation-as-a-Service” powered by AI.  

This move is risky but potentially disruptive: it means moving from being one SaaS tool among many to being the brain that coordinates multiple tools (including possibly your competitors’ tools!).  

For example, a SaaS project management company could release an AI agent that takes high-level project goals and automatically uses Jira, Confluence, Calendar, Slack, etc., to execute the plan. Doing so, you reposition from a tool provider to a workflow orchestrator for that vertical. 

Incumbents with broad product suites are exceptionally well-placed here. They can integrate an agent across their ecosystem (e.g., Microsoft’s 365 Copilot spans Office apps). However, startups are also attempting this in niches, essentially launching “agent-native applications” that directly compete with legacy SaaS by abstracting them.   

This move represents a significant shift, transforming the traditional service-as-software model—where software provides a service—into something entirely new. However, not every SaaS will pursue this, but it’s worth chasing if your core value could be delivered as an autonomous agent that works on the customer’s behalf. Even if you don’t build the agent brain from scratch, ensuring your product can plug into popular agents as a trusted executor will be necessary. 

Maintain dual experiences: human and agent. 

In the near future, leading SaaS companies must offer two seamless experiences: a refined user interface for humans and a machine-accessible interface for AI agents. Just as web apps had to evolve with APIs for mobile integration, AI now demands a similar shift. As seen with Stripe’s AI Agent model, industries must recognize and adapt to this emerging duality. 

This shift might mean SaaS companies investing in things like AI-specific documentation, sandbox environments for agents, or even a “virtual assistant mode” of their product. While this is operationally complex, it buys time to serve current customers while preparing for the agent-dominated future. It also keeps your SaaS in the loop regardless of whether the end-user is a person or an AI. 

Opportunities for incumbents and startups 

Both established SaaS companies and new startups have opportunities in this agentic future, though their playbooks differ. 

For incumbents  

Incumbent SaaS firms have assets to leverage – data, customer trust, and domain experience. These can translate into durable positions if used wisely. One significant advantage is the proprietary data context. An AI agent finely tuned to a company’s unique dataset and workflows delivers exclusive value that competitors can’t easily replicate or monetize.​ 

An enterprise SaaS with years of accumulated domain knowledge can build an agent that performs in ways a generic tool cannot. Incumbents should double down on their data moats. For example, a CRM company can train AI models on its own aggregated (and privacy-compliant) sales interactions to offer insights that no generic CRM API ever could. They can also provide enterprise-grade assurances such as security, compliance, and reliability for AI integrations, which many CIOs will demand. 

Incumbents can turn this threat into an opportunity by introducing their own agent platforms. We have already seen early moves here: Salesforce with its AI Cloud and Einstein agents, Microsoft with its Copilots, etc. An incumbent could offer an agent that prefers its own suite of tools, creating a ripple effect across their products.  

They also have existing distribution: they can bundle AI capabilities into their plans, upsell AI features, and educate their large customer bases on using these new tools. Far from being destroyed by AI, an incumbent who adapts can strengthen customer lock-in by becoming the orchestrator of how work gets done on their platform. 

However, there is a note of caution here. Incumbents will face an innovator’s dilemma. Today, their revenue often comes from seat licenses and the human usage of their apps. Shifting to agent usage (potentially fewer human logins) might upend their monetization. To account for this, they must navigate pricing models for AI usage.  

Despite these challenges, the cost of inaction is higher, and applications that don’t adapt might face declining usage – regardless. Like the companies that failed to go mobile, those who ignore the agent trend risk becoming the next cautionary tale. 

For startups  

The AI-agent wave is a classic platform shift for new entrants that levels the playing field. Startups are not burdened by legacy UI or business models – they can build an AI-native from day one. One clear path for these startups is identifying niches or vertical workflows that big SaaS companies poorly serve and creating AI agents to automate them. 

For instance, a startup could build an agent specializing in real estate lease management or biotech research assistance – domains where incumbents are slow to adapt. By delivering tangible results (time saved, higher output) rather than just software, these new businesses can win customers who care more about outcomes than brand names. 

Startups also have the chance to build a new agent ecosystem. Every new technology wave creates demand for supporting tools. We can anticipate needs like agent monitoring and observability, security layers, and interoperability standards. Just as past SaaS eras gave rise to monitoring tools and integration platforms, this agent era will need its tooling. 

Finally, startups can aggressively align with the new value chain. They can skip building full-stack apps and instead focus on being the best at one layer. They can also pursue creative business models – for instance, usage-based pricing for tasks completed or success-based fees – essentially selling results rather than software seats. This aligns well with how an agent delivers value. 

Both incumbents and startups should recognize that this isn’t a zero-sum game of AI agents versus SaaS. It’s about those who leverage the change versus those who resist it. The pie will continue to grow with new capabilities, but slices will be redistributed. In many cases, partnerships between incumbents and startups (e.g., an old-guard company adopting a startup’s agent framework) could accelerate adaptation on both sides. 

6 practical steps for SaaS companies 

Adapting to an AI-agent-driven future can feel abstract, but here are practical steps these companies should take now to position for this future:

Expose and enhance your APIs 

Make sure every key function of your product is accessible via API (or other machine interface) and prioritize API robustness and documentation. AI agents are going to drive demand for APIs through the roof. Audit your API coverage. If features are only available through the UI, ensure they are accessible via API as well. Consider joining integration hubs or agent marketplaces to increase your visibility to AI developers. Evaluate how effectively agents interact with your API.  

Identify potential obstacles such as rate limits, authentication challenges, or output formats hindering machine consumption. Optimize these aspects to ensure a seamless, agent-friendly experience. 

Develop an agent integration strategy 

Decide how your product will plug into the agent ecosystem. This could mean building a plugin for popular AI platforms (for example, a ChatGPT plugin that interfaces with your SaaS) or offering pre-built connectors for automation tools. The goal is to reduce friction for any AI agent using your service. Begin to treat agents as a new class of customers and court them by making integration easy. Some companies are creating agent SDKs or libraries to simplify how external developers can incorporate their SaaS functionality into agent workflows. 

Launch AI copilots & assistants  

Integrate an AI assistant within your UI to augment your human users. This serves a dual purpose: it differentiates your product today and builds your internal AI competencies. These copilots can also handle multi-step tasks internally, functioning as mini-agents within your app. By doing this, you keep your UI relevant (users enjoy new AI-driven features) while ensuring that if a user’s AI agent is using your app, it can potentially interface with your AI assistant (agent-to-agent communication).  

Adapt pricing and metrics 

Begin to shift how you measure and charge for success. If historically, you charged per seat or per active user, you might consider usage-based pricing to capture value from agent usage. Monitor metrics like a number of API calls by agent systems, tasks completed via automation, etc. This will help you understand adoption in the new model.

It also signals to customers that you’re aligned with their AI automation goals (e.g., offering an option to pay for outcomes or usage, not just logins). So, adapt your business model to incentivize and monetize heavy usage, regardless of whether it comes from people or AI. 

Educate and co-create with customers 

Many customers (especially enterprises) will be cautious about AI agents. SaaS providers should proactively help them integrate AI workflows with your product. This might involve publishing guides or best practices for using AI agents with your SaaS. 

This could also mean working closely with pilot customers to build successful agent integrations – a consultative approach to ensure your SaaS fits into their AI-driven processes. By doing this, you make your product stickier and learn real-world usage patterns to refine your offerings. You should also consider partnering with forward-thinking clients to create case studies. E.g., “X Company used our API and an AI agent to automate 50% of their workload – and here’s how.” Such stories will both validate your relevance and provide feedback for improvement. 

Invest in agent-era capabilities 

Finally, look internally at what new capabilities and talent you need. This could mean hiring ML engineers or prompt engineers to improve how your product works with AI. It might involve beefing up your infrastructure to handle an onslaught of API calls from agents working 24/7.  

Consider building monitoring tools that track AI usage specifically – e.g., flag anomalous agent behavior or errors when an agent interacts so you can troubleshoot. Ensure your security model covers scenarios like an AI agent with API keys (you may need more granular permission scopes or rate controls to prevent mistakes at machine speed). Gear up your tech stack for “always-on” machine clients that will be there alongside human users. This groundwork will pay off as agent usage scales. 

Embrace the change, don’t fight it 

The rise of AI agents represents a fundamental shift in the software landscape, but it’s not a death knell for SaaS – it’s a call to evolve. As SaaS once disrupted on-premises software, SaaS companies must now reinvent themselves for the agentic age. Those who seize this moment will find new growth opportunities, whether by powering the brains of AI workflows or by automating outcomes for customers in unprecedented ways. Those that ignore it, clinging to old UX-centric models, risk becoming obsolete as AI-driven workflows route around them. 

The urgency is real – but so is the opportunity. By rethinking strategies and repositioning within the AI agent ecosystem, SaaS businesses can ensure they remain relevant and essential in the future of software. 

Adaptation is the only path forward. The agent era will reward companies that provide value in whatever form – UI, API, or AI-driven service – and punish those that rigidly stick to yesterday’s playbook. Embrace the coming changes with an open mind and a proactive plan, and you can turn disruption into a new chapter of growth for your SaaS business.​ 

 

Inside the emerging AI agent ecosystem: infrastructure, intelligence, control

Artificial intelligence is redefining software architecture. It has moved beyond a supporting role to function as independent agents interacting dynamically with applications. These new AI-driven systems can analyze data, automate tasks, and coordinate workflows across multiple platforms, fundamentally changing how software operates and delivers value in the SaaS ecosystem.

Businesses trying to operate within this environment must understand the AI Agent stack. This stack consists of three core layers, each representing a distinct role in the AI-driven ecosystem:

Those who understand this new ecosystem and adapt by providing robust APIs, embedding intelligence, or orchestrating complex workflows will position themselves for success in an AI-first future. The following sections will break down each layer of the AI Agent Stack, helping SaaS companies identify where they fit and how they can thrive in this agent-driven era.

Infrastructure (tools and data layer)

This is the bottom layer comprised of connectors, data sources, and execution environments that agents rely on. An easy way to think of it is that this layer is the “arms and legs” of an AI agent​.

This layer includes tools (APIs) for agents to act on (e.g., SaaS apps’ API endpoints), as well as memory stores and databases where context is kept. Many SaaS products will live here within the agent-driven architecture.

They will be stored here as specialized tools or data services that agents call upon. Traditional SaaS offerings must ensure their infrastructure layer is solid (scalable, secure, API-accessible) because agents will interface at this level.

Intelligence (AI/LLM layer)

The middle layer is the AI “brain,” which is the layer that does all the thinking. This layer is usually a Large Language Model (LLM) or similar AI model that can understand instructions, reason, and generate outputs. The LLMs within this layer provide the “conscious thought” in AI agents.

They interpret user goals (“schedule a meeting with client X and update the CRM”) and determine how to execute. This intelligence layer may come from third-party providers (OpenAI, Anthropic, etc.) or domain-specific models fine-tuned by SaaS companies on their data.

Either way, it’s a relatively commoditized layer. Powerful models are widely available, so most SaaS firms won’t win by only having an AI model.

Control (orchestration/agent layer)

The top layer is agent orchestration and autonomy. This is where the agent software plans actions and calls tools. The logic decides which tool (or which SaaS API) to use, when, how to combine steps, and how to handle errors or exceptions. Think of this as the new “operating system” that sits above individual apps. ​

This control layer manages complex workflows, such as the agent breaking a goal into steps, invoking the needed SaaS APIs, monitoring progress, and then adapting. Many new platforms and frameworks are vying to provide this orchestration layer, and it’s within this control layer that much of the traditional app-specific logic is migrating.​

These layers together form the emerging ecosystem of “agent software.” SaaS companies must determine where they fit in this stack. Some might supply the infrastructure/tools (e.g., best-in-class API for payments or data storage).

Others might contribute to the intelligence layer (e.g., train a domain-specific AI model). Ambitious ones might even build a control layer (e.g., an agent specializing in a particular workflow and orchestrating many tools, effectively becoming a new kind of SaaS).

Understanding how these layers function is essential, no matter which layer SaaS companies choose to focus on. These layers highlight a fundamental shift in the value chain. Competition is no longer just about complete applications but also about dominating specific layers within the AI-driven stack.

The landscape of SaaS in an AI-agent era

As AI agents become more capable, SaaS companies must rethink their role. The traditional competitive advantage of SaaS—offering the best UI or the most feature-rich application—is giving way to this more layered approach. Companies that fail to adapt risk becoming obsolete, while those that embrace this shift can redefine their value proposition in an AI-first world.

This transition is not just a technological evolution; it’s a strategic one. In Part 3, we’ll explore how SaaS businesses can thrive in an AI-agent future by repositioning their offerings, redefining success metrics, and ensuring they remain indispensable in a world where AI is the primary user.

Whether by becoming the best infrastructure, embedding intelligence, or taking ownership of the control layer, the key to success lies in adapting, integrating, and leading in this new paradigm.

A tipping point for SaaS

The software as a service (SaaS) industry is standing at a crossroads. For years, software companies have built their competitive edge around user experience, feature-rich platforms, and seamless workflows. But now, AI agents are rewriting the rules. AI agents automate tasks, streamline decision-making, and are fundamentally shifting the way businesses interact with software.

This isn’t just another technological shift; it’s a change. AI agents don’t just enhance software—they become the users of software. If SaaS businesses don’t evolve, they risk becoming invisible in the AI-driven economy.

Is it the end of traditional UI/UX?

In this part of the series, we’ll explore how AI agents are reshaping SaaS, why traditional UI/UX may soon be obsolete, and what this means for businesses that rely on SaaS platforms today.

AI agents are set to transform the SaaS landscape massively, changing how businesses operate, buy software, and engage with customers​. Adapting to this new reality is not optional or “down the road,” but SaaS companies must start now. This article explores why adopting this technology is critical for SaaS and offers strategies these businesses can follow to thrive in this AI-agent-driven future.

The software world is entering an agentic era where AI agents autonomously perform tasks across applications. This shift is poised to upend the traditional SaaS model – so much so that “the notion that business applications exist” could “collapse” in the agentic AI era.​

The urgency: AI agents obscure traditional interfaces

AI agents are transforming the way users engage with software. Rather than humans clicking through menus and forms, intelligent agents can increasingly handle those interactions behind the scenes. As one AI & SaaS venture investor observes, “The UI of the future will be a departure from traditional SaaS tooling, with humans manually inputting things in boxes.”​

Instead of complex dashboards or long lists and title tags, we’ll have simple prompts or chat interfaces for humans while agents take direct actions in workflows on our behalf​. Essentially, the AI agent becomes the new “user” of the SaaS.

The disappearance of UI: a new challenge for SaaS

Unfortunately, a SaaS product’s beautiful UI/UX, a long-time competitive differentiator, may be largely bypassed. AI agents don’t need pretty dashboards; they consume raw functions and data. They can “seamlessly interact with SaaS platforms through their existing user interfaces and APIs, essentially acting as highly efficient digital users.” Soon, many applications might only be seen by agents and not humans, shifting the focus from user-centric design to optimizing backend functionality and AI accessibility.

The impact of this shift is significant: as customers delegate tasks to AI agents, SaaS front-end design and brand presence may become invisible, hidden behind the agent’s interface. User loyalty could pivot toward the agent that delivers results rather than the specific app running in the background.

These events pressure SaaS businesses to remain visible and valuable in an agent-mediated world. Failing to adapt could mean their platforms fade into the background while AI agents take the spotlight.

AI agents don’t need pretty dashboards; they consume raw functions and data.

Soon, many applications might only be seen by agents and not humans, shifting the focus from user-centric design to optimizing backend functionality and AI accessibility.

From differentiator to commodity: the risk to traditional SaaS

If AI agents can plug into any software and achieve a goal, then SaaS products risk becoming interchangeable utilities. For example, the agent doesn’t “care” which CRM or project tool it uses. It will ultimately care about whatever the user tasks it cares about, which could be anything from results to speed and cost.

If your service lacks a unique interface or workflow (since AI agents bypass them), it will compete solely on functionality, pricing, and ease of integration. This shift threatens to commoditize traditional SaaS offerings. As one industry report noted, the future software landscape may resemble today’s API ecosystem, where numerous players compete based on utility rather than user experience.

Eroding moats and increased competition

Traditional moats are eroding. Large user bases, habit-driven workflows, and UI tricks lose power when an automated agent clicks the buttons. If a better option arises? An AI agent can switch between tools in milliseconds, leaving legacy SaaS providers struggling to retain users.

This increases switching risk and reduces differentiation for SaaS providers. A CIO could easily replace an expensive SaaS tool with a cheaper competitor if their AI agent can perform the same operations via API. If that same CEO can use AI to solve their business problems, then why wouldn’t they choose it?

Microsoft’s CEO, Satya Nadella, echoed this trend, noting that modern SaaS applications primarily function as databases with embedded business logic. He noted that AI will increasingly handle these rules across multiple platforms, potentially streamlining or eliminating traditional backends.​ In such a scenario, the “AI tier” becomes the new control point, and individual apps become modular backend components.​

This commoditization could be devastating for SaaS companies that do nothing. Their products risk becoming replaceable background utilities that agents swap in and out.

Next in the series: The AI agent stack – where does SaaS fit?

The message is clear: the era of easy SaaS differentiation is ending. In the age of AI agents, usage is becoming even more critical than UI, and SaaS vendors must be sure their services are being used (by humans or agents) or that they face irrelevance.

In Part 2, we’ll explore how AI agents are restructuring the SaaS stack and where businesses can position themselves in this evolving landscape.

Generative AI tools have emerged as a powerhouse for enhancing business efficiency and effectiveness. Microsoft’s latest report, Generative AI in Real-World Workplaces, provides a fascinating insight into how AI tools like Copilot transform our work.  

However, while Copilot offers significant benefits, a tailored approach that weaves generative AI into a company’s specific line-of-business applications can amplify these gains to an unprecedented level.