How SaaS can Benefit from the Generative AI Era

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How SaaS can Benefit from the Generative AI Era

“Embracing Technology Evolution is Key to Surviving and Thriving”

How will generative AI impact the SaaS industry?? Yes, this is a very big question with many unknowns though I am a big fan of the following quote: “There is no better teacher than history in determining the future”!

There are several articles and social media posts that opine on and debate the “Death of SaaS” which are referenced in the footnotes section of today’s newsletter.? The focus of today’s newsletter is not why SaaS is doomed, but rather the lessons that we learned from previous technology and software evolutions that provide insights that can serve as inspiration for the opportunities that are available to SaaS companies to embrace the potential and power of Generative AI. Over time, generative AI, AI and Machine Learning will be the fuel for the next growth wave of the cloud, including Software as a Service and Service as a Software.

Evolution of Software

Over the past 40+ years the evolution of software has taken dramatic shifts in how software is developed, deployed, sold and used.

The below image from Morgan Stanley from their “Revisiting Software 101” article highlights the evolution of software using a four component framework: 1) How Software is Built?; 2) Where is Software Run?; 3) How is Software Sold?; 4) What Can Software Do?

Below is a view on the evolution of computing and software over the last 30 years that I have personally experienced, and believe is relevant to how SaaS companies can embrace the potential, industry changing power of generative AI:

Megatrends in the software evolution include the Internet, SaaS, Mobile, Cloud, and now Gen AI. Each of these transition points served as a foundation for innovation and growth for start-ups AND mature companies alike.

Examples of established companies that innovated to surf the wave of new megatrends:

• Microsoft took on Netscape to capture the end-user internet access market. Their tactics can be questioned, but not the results, until Google changed the browser game again with Chrome

• Apple transitioned from a struggling computer manufacturer to become the leader in mobile computing far beyond the iPhone with the introduction of the App Store which introduced a new way to develop, distribute and use software
• The Oracle founder and CEO was the primary seed investor to back the first SaaS based accounting platform, NetLedger in 1998 which later became NetSuite, and was even branded and distributed by Oracle as their Small Business Suite

• Amazon was founded to change the way consumers purchased and then lead the transition to "Cloud Computing Infrastructure" which was emulated by both Microsoft with Azure and Google with Google Cloud Platform
• Netflix killed its core business model by taking the lead on streaming, effectively ending their DVD delivery business.? Do their stakeholders regret killing their legacy business?

SaaS companies are sitting on top of one of the most valuable assets that is foundational to the value of AI for the enterprise - DATA.?

The data residing in most SaaS applications can serve as a moat that existing SaaS companies can create against upstart native AI companies. There are significant challenges to unleashing the power of the transactional data they have gathered, but the juice will be worth the squeeze.

Many existing SaaS companies will need to overcome the structure of how they store the data to make it usable to train foundational large language models. At the same time, they will have to address the “data privacy” concerns of existing customers to leverage that data without compromising the confidentiality of their customer information.

Native generative AI start-ups are not burdened by the reality of existing customers mentioned above, but at the same time do not have the advantage of data and distribution.

Resistance to change is a challenge mature SaaS companies face to fully harness the internal resource productivity that is available from Gen AI.

This section is about existing companies leveraging the power of gen AI to increase employee productivity, rather than embedding AI into their core SaaS product. However, the concern about AI’s potential impact on white collar employment is foreshadowing regarding mature SaaS companies ability and appetite to quickly and fully embrace AI technologies and models that are not fully mature and proven in corporate environments.

How many LinkedIn posts and industry articles do you read that talk about and debate the fate of “insert role here”??

A recent Salesforce survey reported that about one-third of sales professionals use generative AI on a regular basis.

• 53% said they do not know how to get the most value from generative AI at work.?
• Almost one half said they do not know how to safely or effectively use generative AI?
• 39% percent of sales professionals worry they will lose their job if they don’t learn how to use generative AI at work

Gen AI benefits are still primarily being experienced at the "individual level" and not at the company level as companies struggle with embracing the change quickly due to legacy company concerns. Recent research conducted by Gainsight and Benchmarkit on the “State of AI in Customer Success” supports that individuals are gaining the early benefits of gen AI, with the largest cohort of research respondents (36%) saying that they initiated the use of AI.

A few brave risk-taking companies will materially increase employee productivity which will serve as a "funding source" for gen AI innovation and growth.

Short-term financial risks for legacy technology companies often win out over the long-term opportunities available by embracing the future… today.

Case Study: GE Information Services

My career started at GE Information Services, which in 1995 had the largest customer base and revenue (~$1B) in timesharing. The timesharing business model was built upon multi-tenant business applications hosted on mainframes, accessed via a global "private" network and priced as a subscription + usage model...SOUND FAMILIAR??

GE’s management prowess was world renowned.? The management ranks were comprised of smart, innovative and experienced business leaders. In fact GE was often granted more patents in a year than any company in the world. When the internet was first evolving, GE leadership understood the potential and risk to their legacy B2B eCommerce business model - the precursor to SaaS.

As a strategy to “experiment” with the potential of the internet and B2B eCommerce, they created a joint-venture with Netscape, the internet software company that started the internet era. This joint venture developed the first browser based application software for buy-side, sell-side and intercompany B2B ecommerce.

Being in the middle of this “experiment” and traveling around the world for three years (1995 - 1998) and not wanting to BORE you with all of the details that led to failure of the experiment - the top lessons I learned that are applicable to the transition of SaaS to the gen AI era include:

1?? Fully embrace technology evolution megatrends even at the risk of short term?financial impact

2?? Understand the competitive assets and advantages you currently have (data,?distribution, customers, etc.) and build your company’s future around them

3?? Accept and exploit the analyst insights that say Gen AI will grow from a $25B to?$200B industry over the next 4 years - a 68% Compounded Annual Growth Rate

4?? Eat your own lunch before someone else does

Generative AI software growth will be faster than any previous phase of software evolution and the rewards are available to native gen AI and existing SaaS companies alike.

On a recent SaaS Talk with the Metrics Brothers podcast episode, Dave Kellogg and I discussed an article written by McKinsey and Company entitled “Navigating the generative AI disruption in software”.?

One of the highlights of the article and Dave’s and my discussion is the predicted distribution of generative AI fueled software investment growth as exhibited below:

A few of the data points that jump out from the above chart and the rest of the article include:

• Gen AI spend will increase from $15B in 2023 to $175B - $250B in 2027

• 35% - 45% of planned enterprise spending on Gen AI will be with gen AI within broader software platforms - a real opportunity for existing SaaS companies

• 30% - 40% of enterprise spending will be on Gen AI point solutions which is still a great target addressable market considering growth of $160B - $235B of spend over the next 4 years

SaaS companies have a unique yet challenging opportunity to capture the largest share of gen AI software applications market share - though that opportunity is huge!

Per the recent Bessemer Venture Partners “State of the Cloud 2024 Report”, vertical Gen AI companies are hitting $4M ARR much faster than traditional SaaS businesses ever did (goodbye T2D3) with a burn multiple ~ 1.1x at a 65% gross margin.? These numbers are amazing, and are being accomplished without the benefit or the baggage that a legacy SaaS company possesses.

I let my mind imagine a few examples of the potential for legacy SaaS companies to leverage generative AI trained by the data they uniquely have such as the ideas below. Please note, I did this without knowing if any of these are possible, being worked on, or already being pursued:

• DocuSign has 1.5M customers and with an average of 100 contracts per year per customer (SWAG) that would mean they have the details on 150 Million agreements signed.? Consider the possibilities of being able to leverage data like percentage of agreements with limitation of liability, use of arbitration clauses, termination for convenience clause language, remedy clauses or mutual indemnification language

• Salesforce has over 150,000 customers and more than 2 million users (estimates) - how much insight and value could they provide regarding sales cycle time, average contract value, number of contacts involved in a deal, support tickets, case resolution time, email open rates, etc. all segmented by industry, by product type and/or by contract value?

• Mailchimp has over 13 million users which easily translates into billions of emails per year being sent even when using a very small number of 80 emails per year per customer.? Imagine the insights they could provide by understanding and providing ideas on which titles get the highest open rate, or what length of emails result in the highest click through rates.

The SaaS industry represents approximately $200 Billion in total spend (2023), is projected to grow 18% in 2024, and there are ~ 31,000 SaaS companies around the world.

I’m confident that with the amount of innovation, capabilities and expertise that many of the SaaS market leaders possess, our industry can successfully make the transition to Gen AI enable SaaS and continue to lead the industry in the Gen AI era!

I will be tracking legacy SaaS vendors and their gen AI announcements closely and will highlight one every week with a LinkedIn “Legacy SaaS AI Story of the Week”. Please connect with me on LinkedIn (@rayrike).

SaaS is not dead - it just needs to be injected with the fuel of the next software era - Data + Generative AI!!!

Footnotes and Resources:

McKinsey and Company: Navigating the generative AI disruption in software (June, 24)

Bessemer Venture Partners: State of the Cloud 2024 (June, 24)

Next Big Teng: SaaS: Have reports of my death been greatly exaggerated (June, 24)

Chris Paik: The End of Software (May 31, 2024)