Staying up-to-date with industry changes and innovations is critical for successful businesses.
Lately, artificial intelligence, or AI, has become one of the most talked about tools across industries. AI will continue to have an impact — both benefits and risks — now and in the future.
So it’s essential for SaaS founders to understand how best to use AI, the potential risks of AI, and upcoming trends.
Understanding AI
Artificial Intelligence began in the 1950s and continues to evolve. It’s now incorporated into various aspects of everyday life, from the maps on our phones to chatbots, virtual assistants, and more.
At its core, AI is the method computers use to perform tasks that normally need human intelligence. Instead, AI allows computers to act on information or data to learn, analyze, and understand through specific algorithms.
There are different technologies or methods that comprise AI, specifically machine learning, deep learning, and large language models.
Machine Learning
Machine learning is a process that allows machines to learn how to respond better. They learn how based on feedback from algorithms and humans and on large structured data sets.
Deep Learning
Deep learning is a type of AI machine learning inspired by the human brain. Deep learning models help computers recognize complex patterns in text, sounds, pictures, and data to provide accurate predictions and insights. It uses artificial neural networks to mimic how human brains learn.
Large Language Models (LLMs)
Large language models use deep learning techniques combined with large data sets to summarize, generate, and predict new content. For instance, LLMs have been used for text generation, chatbots, conversational AI, rewriting content, and more.
A recent, popular example of an LLM is ChatGPT. This natural language processing tool is driven by AI technology that answers users’ questions, writes emails, code, and more.
A helpful AI Framework for SaaS founders
Right now, there are essentially four primary AI use cases.
Here is a useful approach that Rob shared for thinking about when and how to incorporate AI in your SaaS app.
Generative AI - This is when you are essentially generate text or images from a prompt. For example, if you have a Facebook ad optimization tool, you could implement AI to help people come up with text or creative.
Categorization - This is where you can categorize or sort a big list of items. For instance, if you build helpdesk software, you can incorporate AI into your product to categorize support emails by topic or pricing tier.
Summarization - This is where you are turning text or videos into a summary. For instance, you can turn a Youtube video into a short summary.
Prediction - This is where you can use AI to analyze and model out data or trends. So, if you create dashboard software, you might use AI to help customers model out trends.
AI benefits for SaaS companies
AI tools can help SaaS companies in various ways, including assisting their customer support, marketing, and product development teams.
Understand customer trends
Many SaaS companies have used predictive analytic services to help monitor customer behavior, so they can understand what clients need and how to retain them.
Now, you can use AI tools to gather the information you need for effective predictive analytics. For instance, AI can create insights based on historical data and machine learning and help you identify products that are likely to be sought after.
Improve your marketing campaigns
AI tools can assist with data-driven marketing decisions and with helping you better understand your customers’ needs. For example, AI can analyze your client data looking for trends and patterns, allowing for a better understanding of your customers.
You can use this information to improve your marketing focus, including creating promotions and information that specifically appeals to your audience. For example, the analyzed data may provide insights into content and emails that most interest your customer base. Your marketing team can then use this information to develop campaigns that deliver the information your potential and current customers want.
Enhance your customer relationships
Meeting and exceeding customers’ expectations and needs is crucial to SaaS businesses. AI can assist with managing customer relationships by improving the efficiency of your customer service department.
For instance, AI-powered chatbots and virtual assistants could assist with and respond to frequently asked questions, freeing up customer service personnel’s time to deal with more challenging or complex issues.
AI-powered tools can also provide 24/7 assistance, allowing your customers to get the support and help they need when they need it, leading to increased satisfaction.
AI can also help improve customer relationships by providing a more personalized experience. AI can monitor the content customers engage with and what they do. Then, it can use the information to help guide or ensure the customer sees the information they need so they stay engaged.
Increase team efficiency
While AI alone isn’t a competitive advantage, AI can be an incredible accelerant within your company to help you and your team be more productive.
That’s because AI by itself has no moat. Anyone can do it. The real differentiation is if you don’t use AI, you will slowly fall behind.
So, you can free up your team’s time by using AI to handle repetitive tasks, like automating data entry or analyses. As a result, team members can focus on more challenging or strategic tasks, including product development or services.
As discussed, AI can also aid your customer service and marketing teams, allowing them to focus on tasks AI can’t do.
Additionally, using AI for more repetitive tasks may also allow you to hire fewer employees, saving you money.
AI risks for SaaS Companies
While AI and machine learning provide many potential benefits, there are also potential concerns that should be considered.
Data security
Generative AI systems gather, store, and process large amounts of data. To ensure your data stays safe, you will need strong cybersecurity measures to protect against data breaches or data privacy violations.
Additionally, some concern has been raised about some types of AI tools being vulnerable to certain types of attacks, such as model poisoning. This happens when malicious code or data infiltrates the AI system. This event could cause corruption in your system, resulting in inaccurate information generated. Open-source AI tools may be more vulnerable to these types of problems.
Lastly, you’ll want to ensure employees using any AI tools are doing so appropriately and in compliance with your business’s guidelines. Otherwise, you could have employees accidentally disclose sensitive or proceeded information.
For instance, an employee could unknowingly input information into a third-party AI tool that then can lead to data risks or revealing information that your company doesn’t want exposed to competitors.
Privacy concerns
Machine learning and AI need lots of data to be effective and to learn. Some generative AI LLMs may be trained on data that includes personally identifiable information. Prompts can occur that accidentally elicit this sensitive data.
Another concern being raised for some types of AI tools is how programs are incorporating work that is copyrighted, watermarked, or contains signatures. For example, artists have expressed concern about whether AI-assisted programs creating images are “stealing” their art, citing examples of AI-generated images that are incorporating artists’ signatures.
This can lead to ethical concerns regarding the privacy and ownership of the data that may be used by AI. Depending on how the data is used, it could also open a business up to plagiarism or copyright infringement issues.
So if you’re building or fine-tuning generative AI programs, you’ll need to ensure that sensitive, copyrighted, or similar information isn’t being embedded in your model and any such information can be removed if found.
Technical complexity to build new AI features or products
Adding AI to your SaaS products can get complicated. While AI can be helpful, you’ll need to consider whether it will add enough value to justify the development of the AI features or products.
Building new AI features or products can be challenging. You’ll need high-quality data, updated infrastructure, and the ability to integrate it into existing systems — while ensuring you have the storage, processors, and more needed for everything to function sufficiently. You’ll also need to find the right experts with the experience and skills to develop your AI features or tools.
Adopting an existing open-source product may help reduce some of the technical complexities. However, you’ll need to ensure that the AI complies with laws and regulations, including gathering data responsibly.
AI algorithm bias
AI algorithm bias has always been a problem. Bias occurs when AI algorithms are trained on data that isn’t fully representative of a group or is skewed. Unfortunately, it can be challenging to reduce bias or avoid it from entering the algorithm in the first place.
Bias can happen in AI in different ways. For instance, data bias may happen if the data used to train the AI system isn’t representative of the population, causing skewed results. Another bias may occur if the algorithm contains prejudices that are not accounted for in the data or if the model is overfitted to a specific subset of the data. This could lead to incorrect predictions.
When building AI algorithms, bias ideally should be considered at every stage of development to help reduce or minimize it.
Competitor risk
First, if you are a bootstrapped or mostly bootstrapped company, you should avoid competing directly with the biggest AI incumbents like Google, OpenAL, Microsoft, Facebook, IBM, or Amazon.
That means leaving the horizontal AI and LLM plays to the biggest brands with the biggest budgets.
A much better strategy is to figure out how to leverage AI to solve an existing problem in your product better and faster.
There will still be competitor risk, but that’s mostly due to losing your competitive edge if you don’t integrate with AI or integrate too late.
In addition, you also have to balance this with making sure you don’t spend too much on a new paid AI feature that ends up being integrated into other products for free.
Regulatory and legal compliance
If you use AI, you’ll also need to ensure that your use of AI is complying with relevant standards and regulations. For instance, companies using AI systems should ensure that the:
AI systems are not breaking any regulations or laws
Data being used for training is collected ethically and legally
AI systems do not discriminate against any particular group and aren’t used to deceive people
If you’re using a third-party-created AI tool, you’ll want to ensure it is compliant and that any future updates remain so.
Not ensuring AI compliance can open you up to potential legal and financial risks, including fines, penalties, and impacting customer confidence in your business.
SaaS AI Trends to Pay Attention to
1. Security AI and automation help improve cybersecurity for cloud environments
Keeping cloud SaaS environments secure from threats like ransomware, data leaks, and more is challenging. But the IBM Cost of a Data Breach Report 2021 findings support that businesses using AI and automation programs saved money in costs and were able to identify and contain a breach faster than those that didn’t.
As a result, finding and improving the effective implementation of cybersecurity automation in cloud SaaS environments remains a continuing SaaS security trend.
2. Incorporating generative AI into existing products
Many existing SaaS companies are finding ways to incorporate generative AI into their existing channels and products to increase the value and usefulness of their products to customers.
For instance, in February 2023, Microsoft combined OpenAI with Bing to provide an AI-powered Bing search engine and Edge browser. According to Microsoft, this combination helps enhance and “reinvent” search since the OpenAI model is specifically designed for search.
3. Set clear user expectations when incorporating AI into products
Providing specific information and guidance on the limitations and what users can expect from the AI-incorporated features can help SaaS companies ensure customers get the most out of the features and maintain trust.
For instance, when Snapchat released its AI-powered chatbot, My AI, the company also indicated some of its limits. For instance, Snapchat acknowledged that My AI can be tricked into saying things and informed users they shouldn’t share secrets with My AI.
Snapchat also invites users to submit feedback and makes the process easy.
Taking these kinds of steps can help build trust between you and your customers.
4. Incorporating AI capabilities with low-code and no-code platforms and tools
Low-code and no-code platforms and apps have been important to many industries, including SaaS. These platforms are typically more accessible to non-tech-minded people and can remove some barriers for less tech-focused startups that are entering into SaaS.
Additionally, less code can help lower the cost of product development, allowing developers to focus more time on innovation. And startups may be able to create minimal viable products faster, allowing them to test more solutions.
Incorporating AI with low-code and no-code options can further assist with the development, especially for companies with limited software developers.
