How Artificial Intelligence is changing the SaaS Landscape

There is no one moment when SaaS Software as a Service was conceived, because SaaS as a concept has a host of components; all of which have had to come together in the right context in order to produce value for any sector or vertical market. Different sectors have moved towards SaaS models at different speeds.

In technical terms, SaaS relies on cloud delivery at scale, a minimum degree of widely available connectivity, and enterprise-grade security. If any of these are weak, SaaS drops off the agenda.

Then there’s the business model and the attendant marketing around it not only do SaaS businesses themselves have to sustain the transformation from a CapEx to an OpEx model, they also have to explain to their customers what’s going on, and how they can benefit from it. After all, the idea of renting rather than owning is challenging for some; and there are all sorts of (sometimes unresolved) questions about data security, ownership and power of redress. 

It’s therefore taken a decade or so for SaaS tools to become widely adopted, and in that time a set of characteristics of SaaS services has become clear:

  • Upfront pricing on a subscription basis;
  • Real-time, incremental service updates rather than monthly or even annual release;
  • Exceptional ease of use and self-serve customisation for every user (particularly on mobile platforms);
  • Device and use-case agnostic: learn once, use anywhere;
  • Strength in numbers: easy integration with other SaaS tools;
  • Information as the fundamental commodity of value data in, more valuable data out.


It’s been worth reciting that summary of what makes a SaaS service, because Artificial Intelligence is indeed going to revolutionise these businesses, and it will do so by improving precisely those parameters.


Let’s start with automation. AI allows more functions which may previously have had a manual component to be automated. This manifests itself in various ways typical examples include using machine learning to automate aspects of customer service (especially self-serve), but we can also imagine using AI to improve, say, the onboarding process of a SaaS product, or  refamiliarisation after a major update, through automation. 

SaaS is spectacularly successful  – consumers and businesses alike appreciate the simplicity and 24/7 availability of online services and their predictable, balanced business model. 

Given that the main user experience challenge of a SaaS product is remoteness (that’s the price users pay for the benefits of 24/7 access on their terms), AI can mitigate that remoteness and ensure that more customers get a more satisfying experience.

Continuing with automation, at a time when the doom-mongers are convinced that AI will replace jobs practically one-for-one, many analysts feel that AI will deliver most value when it is deployed in conjunction with human effort. This may go against everything that you think SaaS stands for, but perhaps the best way to view and profit from a market is to effectively triage interactions into those which can be handled automatically (the SaaS way) and those which require human intervention. 

Take, for example, the Lola app; created by Paul English, the co-founder of the Kayak travel website. Lola will deliver a combination of live travel agents and AI-augmented chat functionality. The purpose is to enhance the capabilities of travel consultants. With the help of AI, agents are productive to the tune of nearly twice as many journeys and make consistently better recommendations. There are still countless SaaS-only travel services, but Lola allows enough automation for human interventions to be viable, useful and deliver better experiences to the consumer. As a commercial proposition, AI-augmented human interactions drive SaaS interactions too.


AI will also progressively support the personalisation component of SaaS services, too. Previously, user interfaces have become exponentially more complex as each iteration of a piece of software crams in more functions and menus onto the screen. 

But app developers, Clearbridge, say that the future of interactions is context-aware computing, driven by AI. With natural language processing and voice control; along with dynamic controls designed automatically from previous interactions to present just the configuration a customer expects when they need it, using SaaS tools will become child’s play. This reduces adoption hurdles and allows more services to become a part of each user’s daily software suite.

Shipping code even faster 

On the developer side, customer and competitive pressures both mean that the development cycle for SaaS products has been crunched down from several months to (in some cases) minutes. With tools like Docker, new code can be deployed in seconds with confidence that it will scale perfectly to thousands of users. 

This near-real-time deployment means great competitive advantage, but bad code can be hugely and disproportionately costly. What if a SaaS service with 100,000 users crashes just for a day? Even before any legal remedy is considered, in terms of trashed reputation, the cost can be vast. AI is coming to the rescue, interpreting multiple code languages, assessing them for errors, learning with each new script analysed and improving the viability of each patch and update. 

Microsoft is not just using AI to test code, it is working on systems which will deploy AI to develop code itself soon we will train AI to write code in the same way as we might train a dog to sit. 

Enhanced security 

Finally, of course, there is the security aspect of SaaS. These services are online by definition, and whilst scaled-up SaaS services undoubtedly deliver more cost-effective and holistic security than a localised on-premise solution ever can, the problem is that every user is their own weak link in the chain. We are all fallible, and because life isn’t necessarily fair we expect our SaaS services to take responsibility for our own mistakes (have you changed that password recently? Didn’t think so…). 

AI’s expertise in pattern matching, human factors assessment and machine learning mean that these techniques are rapidly becoming the most effective security patrol a SaaS provider can deploy: because AI is capable of learning to replicate the actions of a hacker or a delinquent user, rather than just a dumb perimeter device. Oracle, for example, has just added machine learning to its cloud-specific security regime.

SaaS is spectacularly successful   consumers and businesses alike appreciate the simplicity and 24/7 availability of online services and their predictable, balanced business model. But those benefits also turn those businesses into commodities: customers can always choose to go elsewhere. AI, with its ability to integrate into any digital process, is set to be the essential driver of service, loyalty, stickiness and cutting edge functionality for the next decade of SaaS services.

Director of Brand and Communication. Born in New York, raised in Lisbon, forged in the fires of London. Often found being walked by Moses the Dog, drinking coffee, in the cinema, following Radiohead on tour, on his racing bike (sometimes all at once). Eternally curious, tells a good story.

1 Comment
  • Thomasoperm
    August 11, 2017 at 10:34 am

    I’ve been interested in data and analytics for a long, long time. As a ten-year-old, I remember creating Excel spreadsheets with my dad for his annual Fantasy Football pool. By the time I was 16, I was building Pivot Tables with my cell phone bill to figure out who I texted the most. (I promise I had other hobbies.)

    Since then, I’ve learned how much more there is to data analysis besides Pivot Tables and Excel. There’s a whole world of analytics out there — and I’ve barely scratched the surface.There’s always more to learn, so I’ve made it my mission to learn as much as possible about analytics by talking with people and, of course, consuming as much content as I can online. Here are my favorite go-to resources for continuing my marketing analytics education.

    1) The KISSmetrics Blog

    The KISSmetrics blog is a great resource to learn about marketing analytics, testing, and experimenting with your data. Content is posted daily and will teach you everything from A/B testing to growing your business with analytics to lead generation on your different marketing channels. No matter what company you work at, you’ll be able to learn something about data analysis by reading and following this blog.
    2) “Occam’s Razor” Blog

    Avinash Kaushik is known for his book, Web Analytics 2.0 & Web Analytics: An Hour A Day. “Occam’s Razor” is his blog, where he writes some of the best analyses, explanations, and analytical materials out there. From these posts, you’ll learn how to approach advanced analytics situations and take your data analysis to the next level from a true expert.

    Occam’s Razor by Avinash Kaushik

    3) HubSpot’s Inbound Certification

    HubSpot offers a free inbound marketing certification for anyone interested in learning about how to implement inbound marketing in their business. The curriculum covers many parts of inbound marketing, including analyzing your efforts to make smarter decisions on where to invest in your future. Not only is it free, but it’s available to anyone interested in learning these best practices.

    Academy Certification

    4) Google’s Analytics Academy

    Google Analytics is one of the most popular platforms for companies analyzing their marketing efforts. To help teach people how they should approach their data analysis, Google offers different classes through their Analytics Academy. In addition to courses to teach users how to use Google Analytics, there are also courses that teach about data analysis in general. From mobile analysis to digital analytics, users can take these free courses and learn a lot more about how they should approach their next analytics challenge.

    Google Analytics Academy

    5) Quora’s Analytics Section

    The Analytics section of Quora is one of the best places to get quick analytics help with little effort. In this section, you can search or browse through thousands of common analytics questions and skim through answers from analytics experts. Many of the most respected analytics experts are following and answering topics related to analytics on Quora. And if, you can’t find what you are looking for, you can always ask a question yourself.


    6) General Assembly’s Data Analysis Courses

    General Assembly is an online resource offering classes, workshops, courses, and on “the most relevant skills of the 21st century.” Among them are a ton of data analysis classes for all levels, including introductory classes on fundamental modeling techniques and making meaning out of large data sets, and some more advanced classes on data analysis through SQL.

    General Assembly

    7) “Online Behavior” Blog

    Google Analytics Advocate Daniel Waisberg is the founder of Online Behavior, a blog that focuses on marketing measurement and optimization and covers a wide range of topics. Most of the content focuses on how to conduct different types of analysis using Google Analytics. Some focuses on data analysis and reporting in general and provides best practices on these topics. No matter what your interest is, you’re bound to find helpful advice on this blog.

    Online Behavior

    8) The Moz Blog’s Analytics Section

    Moz’s blog covers topics about inbound marketing and SEO — but if you dig into the “Analytics” tag specifically, you’ll find a ton of helpful articles about topics ranging from general best practices to instructions for conducting experiments and analyses with your data. I find the advice on here very actionable, and I think it’d be helpful for readers at every level.


    9) Predictive Analytics World Conferences

    Predictive Analytics World is a series of conferences around the world that are focused on analytics. Each event in each different city is focused on its own theme. For example, in Chicago there are two simultaneous events: one focused on manufacturing and one focused on business. In Washington, D.C., the conference is focused on how government agencies use data analysis. Many of the other conferences are focused on business in general and can appeal to anyone interested in learning more about data analysis and upcoming trends in predictive analytics.

    Predictive Analytics World

    BONUS: HubSpot’s “How to Use Excel” Blog Post

    While this is a single blog post rather than an ongoing publication, I still think worth including here as a helpful resource. I get asked about my favorite Excel tips all the time, so I finally decided to compile some of the most common ones into a single blog post listing 14 simple Excel shortcuts, tips, and tricks.

    Excel Tips

    What are your favorite resources for learning more about analytics? Share with us in the comments below.

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