At the turn of the 21st century, as companies were lumbering with on-premise software systems, software-as-a-service (SaaS) arrived quietly on the scene.
The SaaS revolution that the tech world saw in the next two decades, fueled by the advances in broadband, browsers, APIs and other technologies, was anything but quiet. SaaS offered companies large and small a way to scale effortlessly, deploy software with a few clicks, and access powerful tools without the crippling costs of physical infrastructure.
As a result, the global SaaS industry is now worth more than $237 billion. And nearly half of venture capital funding goes to companies with a SaaS business model.
But, with SaaS well into its third decade, the summer of possibilities it ushered in gave way to the winter of discontent.
Revenue growth rates have slowed for all sizes of public SaaS companies. Customers are growing weary of constant price hikes and upsells. Competition has intensified in the SaaS domain—there are over 30,000 SaaS companies worldwide, by some estimates.
Compounding these challenges, the generative AI revolution has arrived at every doorstep, shaking up every corner of the tech landscape.
With companies scrambling to show profitability as the market goes through this recalibration, there is, as Jason Lemkin observes, “a bit of a malaise hanging over the SaaS world today.”
So, what happens next? If the SaaS we’ve relied on thus far is SaaS 1.0 and has reached the tipping point where on-prem was two decades, how will SaaS 2.0 evolve in the coming days? What role will AI play in helping SaaS meet sky-high customer expectations? Let’s take a look.
The 3 big achievements of SaaS 1.0
Let’s start with the positive side of SaaS to kick things off. After all, SaaS did solve some of the biggest problems that businesses faced and helped them grow exponentially.
Here’s a closer look at the top 3 achievements of SaaS 1.0 that helped businesses be where they are today:
Democratizing software access
Before SaaS, companies had to buy on-premise software that required heavy infrastructure investments—expensive servers, dedicated IT teams, and complex licensing agreements.
This meant that robust software solutions were often limited to large enterprises that could afford the significant capital outlay. For smaller businesses, these systems were often out of reach, leaving them to rely on less efficient manual processes or inadequate, small-scale software solutions.
Once SaaS came along with its subscription model, businesses could subscribe to software services on a monthly or yearly basis instead of purchasing hefty software licenses upfront.
This democratized access to software as it drastically lowered the financial barriers, making sophisticated software available to SMBs for the first time.
This accessibility encouraged rapid adoption of new technologies and fostered a wave of innovation across industries. Startups, often nimble and resource-constrained, could now experiment with best-in-class tools without worrying about significant capital expenses.
This ability to access advanced software on a “pay-as-you-grow” model helped fuel the growth of modern tech ecosystems, allowing startups to scale quickly and compete on a global stage.
Scalability and flexibility
In the pre-SaaS era, scaling software meant buying new servers, upgrading hardware, or even rewriting parts of the code to accommodate the increased load. For many companies, this was a logistical and financial burden that stifled growth.
With SaaS, that burden was lifted. The cloud infrastructure underpinning SaaS tools allowed businesses to scale without worrying about hardware or system limitations. The software simply adjusted to the business’s growing needs.
For instance, a startup running on Amazon Web Services (AWS) can access the same on-demand cloud computing power. as a global enterprise, without having to invest in a massive IT footprint.
The flexibility of SaaS platforms lies in the fact that businesses could use exactly what they needed when they needed it, rather than committing to rigid, one-size-fits-all solutions. This meant businesses could start small and expand their usage as their needs grew.
SaaS platforms also managed to roll out updates frequently without disrupting operations, saving hours of downtime and lost productivity that plagued on-prem software updates. With cloud-based architecture, SaaS providers can seamlessly deploy new features and patches in the background without any noticeable impact on the end user.
Driving remote collaboration
Till the end of the previous century, collaboration between teams was often limited by geography, time zones, and clunky communication systems. Teams in different locations struggled to stay in sync, relying on email chains and occasional conference calls.
With the rise of SaaS, real-time remote collaboration became a possibility. No matter where teams are on the face of the earth, they can work together seamlessly in real time using SaaS tools.
For CX teams in particular, remote collaboration became a real advantage. Whether the customers are spread globally, agents could provide 24x7 support, ensure faster response times, and handle inquiries from various channels—email, chat, phone, or social media. In the end, remote collaboration helped organizations drive customer satisfaction and retention rates.
Of course, SaaS didn’t pull this off in a vacuum. These collaboration capabilities were the result of several key advances in internet and communication technologies that the world witnessed in the past couple of decades.
For instance, high-speed internet became the norm rather than a luxury in the 2000s. Web browsers were designed to handle complex applications. Most importantly, the rapid development of cloud infrastructure provided businesses scalable, secure, and cost-effective environments to deploy their software.
The impact of remote collaboration was profound. McKinsey’s American Opportunity Survey revealed that when people have the chance to work flexibly, 87% of them take it.
SaaS made remote work not just possible, but productive. Teams weren’t bound by office hours or location anymore—they could operate fluidly, responding to customer needs, collaborating with colleagues, and managing projects across borders and time zones.
4 challenges that push SaaS 1.0 against the wall
SaaS was hailed as the hero when it replaced on-prem systems and made software accessible, scalable, and flexible for businesses worldwide. But today, SaaS has been around long enough to see itself become the villain.
What was once a solution to complexity has itself become complicated. What was once seen as the cutting edge of innovation is struggling to keep up with modern demands. Now, businesses are navigating a SaaS landscape that’s riddled with inefficiencies, operational burdens, and mounting costs.
One theory put forth by Winning by Design is that the SaaS industry, driven by a “grow-at-all-cost” philosophy, prematurely scaled its go-to-market (GTM) approach without paying attention to effective customer acquisition, retention, and expansion motions. Whatever the root cause may be, the truth is that SaaS isn’t what it used to be.
Here are 4 main challenges that the SaaS industry faces today:
Challenge 1: Tool sprawl stifling cross-team collaboration
Today, the typical enterprise juggles an overwhelming number of SaaS applications. A recent study points out that organizations now use an average of 112 different SaaS tools, and Forrester’s Tech Pulse Survey shows how 77% of US technology decision-makers report moderate to extensive levels of technology sprawl.
While this might seem like progress, the reality is that it has created a tangled web of disconnected platforms, as there’s a separate solution for every department.
The sales team might rely on a sales CRM, the marketing department on a marketing automation platform, customer support on a ticket management platform, and product management on a project management tool.
Individually, these tools are powerful, but they weren’t built to work together seamlessly, and therein lies the rub.This siloed nature of SaaS 1.0 stifles cross-team collaboration.
The 2 major fallouts arising from this lack of cross-team collaboration due to SaaS tool sprawl are:
- Data fragmentation: Each platform holds its own set of information—customer interactions in the CRM, campaign metrics in the marketing automation tool, product feedback in the support system—but these data sets rarely speak to each other. The result is data silos, where valuable insights are trapped within individual platforms and difficult to access across teams.
As a result, businesses end up with fragmented pieces of the puzzle instead of gaining a 360-degree view of the customer. This hampers their ability to make informed, strategic decisions.
- Workflow redundancies: Given the disjointed nature of SaaS tools, multiple employees might end up performing the same task due to lack of context. For example, when a customer contacts an organization’s support team with a complaint about a product, they would have no visibility into the fact that the same customer received an email about the very same issue from the product team just a few days prior. In many cases, support agents might reach out to customers for information that is already stored in another system but not easily accessible. This leads to unwholesome customer experiences, which can lead to an uptick in customer churn.
Challenge 2: Operational and maintenance burden
While SaaS solutions were originally intended to offload infrastructure and maintenance, the sheer volume and variety of SaaS tools in today’s typical tech stack have brought their own set of challenges. The more tools a company uses, the greater is the operational burden.
The 3 burdens related to SaaS operations and maintenance are:
- Management of licenses and subscriptions: With each new tool, companies are faced with multiple subscription plans, each with its own renewal cycle, pricing tiers, and usage caps. When businesses scale, so does the need to track who is using which tool, how many licenses are active, and when renewals are due. Many companies end up overpaying for licenses they don’t need, or worse, underpaying and facing interruptions in service when they exceed their subscription limits.
- Troubleshooting integration failures: In theory, the ability to integrate SaaS tools should provide a seamless flow of data between platforms. In reality, software integrations often fail, break, or produce conflicting data. This leads to operational bottlenecks and requires manual intervention to identify and fix issues, leading to a significant drain on teams’ time and less room for strategic initiatives like innovation and growth.
- Security and compliance risks: The addition of each new SaaS tool introduces potential vulnerabilities. Any lapse in security updates or failure to implement patches can expose sensitive employee data or customer information to cyber threats. Also, with businesses around the world using SaaS apps to enhance collaboration and productivity, the risk of sensitive data being shared between teams and getting inadvertently leaked is always high.
Challenge 3: Incompatibility with an AI-led future of work
The generative AI revolution has fundamentally changed how decisions are made, how processes are automated, and how value is delivered. And it can upend SaaS in its current form.
As Umesh Sachdev, Co-Founder and CEO of Uniphore, argues in this Forbes piece, AI takes time to develop and deploy because it requires large amounts of data to train on. This includes not just publicly available data, but also internal enterprise data like product and service data and contextual customer interaction data.
Here’s where SaaS becomes incompatible with AI. The top 3 aspects of incompatibility, according to the article, are these:
- The data that AI models need are either in silos thanks to disjointed SaaS apps or with third-party vendors. This poses a hurdle to data extraction.
- Even if successfully extracted, these data would be unstructured (like free-flowing documents, knowledge base data, and call recordings). So, this unstructured data must first be converted into structured data.
- Since the SaaS business model is built for speed in delivery, and training AI models is a time-demanding process to produce good results, SaaS companies could fast-track software development at the expense of quality.
Challenge 4: Gap between SaaS 1.0 and modern customer expectations
The way customers interact with technology has fundamentally shifted. Today, they are immersed in seamless, intuitive experiences through consumer platforms that have set the bar incredibly high by delighting the customers in every step. As a result, customer expectations for B2B SaaS platforms are through the roof.
Sample these insights about the rise in customer experience:
- 66% of B2B customers expect fully or mostly personalized experience while purchasing software, as per a Forrester study.
- 67% of customers expect the ticket resolution time to be less than 3 hours, according to HubSpot.
- 59% of consumers agree that automated self-service options improve customer service, as per Nuance Communications.
What makes SaaS 1.0 incapable of delivering seamless customer experiences is that SaaS in itself isn’t seamless. SaaS 1.0 tools are often fragmented, requiring users to switch between different applications or modules that don’t integrate seamlessly.
Businesses use one platform for running email campaigns, another for social media, and a third for customer analytics. Each tool requires separate logins, and data doesn’t flow naturally between them. This naturally slows down decision-making, increases the operational burden, and fails to provide the quick and personalized experiences that today’s customers look for.
What should SaaS 2.0 bring to the table?
The SaaS of yore, or SaaS 1.0, forces you to juggle fragmented systems and spend more time managing tools than growing your business. And it can no longer help you keep up with accelerating customer expectations.
So, the next edition of SaaS, or SaaS 2.0, has to unify fragmented data and disconnected teams, simplify operations and maintenance, and make AI its lifeblood.
SaaS 2.0 should be AI-native
SaaS 2.0 must have AI built into the core of the system from the ground up, rather than being an additional layer or feature. AI-native platforms are designed to ingest, analyze, and act on data at scale, enabling them to learn continuously, automate decisions, and generate insights in real time.
This is not about retrofitting AI on top of an existing platform but creating an infrastructure that revolves around intelligent automation and proactive problem-solving.
In contrast to SaaS 1.0, where users had to input data and manually extract insights, SaaS 2.0 should be designed to operate independently, reducing the need for human intervention. It doesn’t just respond to user commands but learns from user behavior and data patterns to anticipate needs, optimize processes, and make decisions autonomously.
This is especially transformative in areas like sales, customer support, and marketing, where real-time decision-making can make or break customer experiences and business outcomes.
SaaS 2.0 should break data silos
In SaaS 1.0, individual tools for marketing, sales, customer support, and product management often operate independently, each holding its own repository of data. This fragmentation not only makes it difficult to access holistic insights but also leads to inefficiencies, as teams are forced to manually bridge the gaps between systems.
This is where SaaS 2.0, powered by AI, steps in to eliminate these silos by unifying data across departments. AI-driven knowledge graphs can connect the dots between marketing, sales, product, and support systems, breaking down the barriers that have traditionally kept this data separate.
This kind of cross-functional visibility empowers teams to work together seamlessly and address customer needs before they escalate into bigger problems. With all departments working from the same source of truth, teams can collaborate more effectively, make data-driven decisions, and resolve issues proactively.
SaaS 2.0 should foster seamless cross-team collaboration
In the SaaS 1.0 era, while individual departments thrived with tailored tools—CRMs for sales, project management for product teams, help desks for support—they used to operate in silos. Communication between these departments was manual, requiring lengthy meetings, follow-up emails, and fragmented workflows. This approach led to bottlenecks, miscommunication, and inefficiencies.
AI-driven SaaS 2.0 must change this dynamic entirely. These tools should make cross-team communication, break down operational barriers, and provide real-time insights that enhance productivity.
By breaking down these silos, AI-powered systems not only improve collaboration but also enable teams to make data-driven decisions more quickly.
SaaS 2.0 must reduce operational overhead
In the SaaS 1.0 era, operational overhead grew as companies adopted more and more tools to meet the specific needs of different departments.
While these tools helped automate certain functions, they still required significant human oversight to manage updates, maintain integrations, troubleshoot errors, and monitor security. The manual processes involved in connecting, maintaining, and updating these systems often resulted in time-consuming, error-prone workflows that taxed IT resources and limited operational efficiency.
So, SaaS 2.0 must be designed not just to automate basic, repetitive tasks but to foresee potential issues before they arise and take preventive action—automating everything from routine system updates to more complex workflows, such as data integration and security monitoring.
By eliminating much of the manual intervention that was necessary in SaaS 1.0, businesses can operate more efficiently, reduce costs, and free up IT teams to focus on more strategic initiatives.
Preparing for the paradigm shift of SaaS 2.0
What the Internet did to on-prem software, AI will do to complex, expensive, heavyweight SaaS 1.0 apps that were designed and built two decades ago.
The biggest opportunity that AI brings is the true convergence of disparate tools and workflows. An AI-powered knowledge graph can help different teams in your organization—whether its Sales, Customer Support or Product—speak a common language. This convergence is what SaaS 1.0 promised but didn’t deliver.
Ultimately, the real value of AI is this: If machines do more, humans can do less but better.
Less = better.
Businesses can actually focus on streamlining workflows across teams to deliver maximum value to customers—to make customer success the responsibility of the entire organization.
At DevRev, we’re engaged in the pursuit of making SaaS 2.0 a reality. For over 4 years now, we’ve been building a true AI-native software that ensures seamless data exchange between teams and scalability to grow with businesses.
Now, to bring a new dimension to the conversation around SaaS 2.0, we’re bringing together some of the best minds on the future of technology and business at Effortless 2024, our annual flagship conference.
DevRev Effortless 2024 is the only industry conference that brings AI and Design together, so machines work with humans seamlessly.
In this conference, we will make a case for the essentials to modernize the fractured enterprise. And how AI begins with end users and customer support. AI has to be reductive and clear the clutter in our tech stacks.
Now’s the time for SaaS 2.0 that’s simpler, faster, and sentient, where software does more so you can focus on fewer yet high-value tasks.
Ready to discover SaaS 2.0? Save your spot now for DevRev Effortless ‘24!
