There was once a time when a majority of companies hosted their own in-house, or enterprise, software and hardware infrastructure. But with the proliferation of the internet in the 1990s and early 2000s alongside cloud computing in the following decades, software became more efficient and cost-effective when provided to companies as a service. Software as a Service (SaaS) is a method of delivering applications over the Internet, allowing customers to bypass the installation and maintenance of complex software and hardware through remote servers at faster speeds, all with greater capacity, scalability, and flexibility. Popular examples of SaaS providers include Zoom, Salesforce, and Mailchimp.

It is estimated that 80% of companies¹ use at least one SaaS application, which in most cases replaces unwieldy enterprise software. While enterprise software has high upfront costs, SaaS has very low upfront costs and requires little to no maintenance or upgrading by the customer. This is because SaaS providers are responsible for the applications’ security, storage, access, and performance. The cost difference makes SaaS far more accessible to small businesses with flexibility and scalability across team sizes and company growth stages. With the adoption of remote and hybrid work brought about by the pandemic, SaaS offers access to anyone with an internet connection, anywhere in the world. Considering the many benefits of SaaS, it is unsurprising that the global SaaS market² is currently worth roughly $3 trillion and is projected to reach $10 trillion by 2030.

Because of its widespread adoption and centralized maintenance, SaaS makes for the perfect pairing with rapidly evolving AI innovations. Companies are able to experience exponential adoption across industries, scales, and use cases when SaaS providers integrate AI into their existing applications. AI is being used in SaaS³ to increase efficiency, automate repetitive tasks, and provide valuable predictive insights. When integrated with SaaS, AI provides better customer service, leads to more targeted marketing, responds to customer needs and demands, and optimizes SaaS functionality. It is estimated that over one-third of SaaS businesses⁴ are currently using AI, with AI predicted to be integrated into nearly every new software product and service by 2025.

It is far more efficient to integrate AI into multi-tenant SaaS applications than it is for every company to integrate AI into their own in-house systems. For example⁴, Slack’s AI-powered chatbot, Slackbot, streamlines workflows to enhance productivity by automating common tasks like scheduling meetings and sending reminders. By fully integrating AI into their SaaS platform, Slack allows thousands of users to stay in the app so companies don’t need to pay for, implement, maintain, and train their staff on a separate AI-enabled virtual assistant.

On one end of the tech chain, SaaS is employed by many consumer-facing companies that also might be implementing their own forms of AI. On the opposite end of the tech chain, SaaS providers rely on cloud infrastructure and cybersecurity providers to run their all-in-one software service. SaaS, while a strong secular growth investment opportunity in and of itself, is often a critical link in the chain between the backend tech enablers and the brands we know and use in our daily lives.

The TrueShares Technology, AI & Deep Learning ETF (LRNZ) is a concentrated portfolio of tech companies that apply advanced levels of AI within their businesses. Implementing AI up the tech chain allows advances and efficiencies to have a robust and exponential trickle-down effect across industries.

To get up to speed on all things AI investing, view our Investor's Guide to Artificial Intelligence.

1. https://www.saasacademy.com/blog/17-fast-growing-saas-startups
2. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-saas-factor-six-ways-to-drive-growth-by-building-new-saas-businesses
3. https://www.forbes.com/sites/theyec/2023/03/23/the-benefits-of-artificial-intelligence-and-machine-learning-in-saas-businesses/?sh=2a3b25fd5a85
4. https://www.saasacademy.com/blog/artificial-intelligence-saas-industry

The TrueShares AI & Deep Learning ETF (AI ETF) is also subject to the following risks: Artificial Intelligence, Machine Learning and Deep Learning Investment Risk – the extent of such technologies’ versatility has not yet been fully explored. There is no guarantee that these products or services will be successful and the securities of such companies, especially smaller, start-up companies, are typically more volatile than those of companies that do not rely heavily on technology. Foreign Securities Risk -The Fund invests in foreign securities which involves certain risks such as currency volatility, political and social instability and reduced market liquidity. Growth Investing Risk – The risk of investing in growth stocks that may be more volatile than other stocks because they are more sensitive to investor perceptions of the issuing company’s growth potential. IPO Risk – The Fund may invest in companies that have recently completed an initial public offering that are unseasoned equities lacking a trading history, a track record of reporting to investors, and widely available research coverage. IPOs are thus often subject to extreme price volatility and speculative trading. New Issuer Risk – Investments in shares of new issuers involve greater risks than investments in shares of companies that have traded publicly on an exchange for extended periods of time. Non-Diversification Risk – The Fund is non-diversified which means it may be invested in a limited number of issuers and susceptible to any economic, political and regulatory events than a more diversified fund.