The biggest challenge with building AI SaaS products is knowing where and how to introduce the artificial intelligence recipe.

Do it the wrong way, and you’d have just a regular SaaS product with an AI chatbot feature. 

Over-leverage machine learning and you’d be preparing for multiple copyright and data privacy lawsuits. 

In this article, I will discuss the step-by-step procedures on how to build a SaaS product with AI capabilities, overcome safety and legal risks, and attract loyal users. Let’s ride!

What Is a SaaS Product?

Software-as-a-service (SaaS) is a cloud-based software delivery model in which applications and products are hosted by service providers and made available to users over the Internet. 

Instead of users compulsorily downloading the software on their devices, SaaS allows them to simply interact with it via web browsers. 

From social media platforms to productivity tools like Canva, Slack, Figma, etc., SaaS is redefining how people use software.

However, being web-based doesn’t mean you can’t access these tools through other platforms.

Most of them are also available as downloadable mobile and desktop apps, increasing their reach.

It’s fair to say that there are SaaS products for every purpose, cutting across the business-to-business (B2B) and business-to-consumer (B2C) markets. 

With AI’s revolution and predicted adoption of cloud computing among 50% of the world’s enterprises, SaaS is set to consolidate its position as the blue-chip of software business models.

1. Identify a Niche and Validate the Problem

What drove some of the world’s greatest inventions is the need to fix an existing problem. 

While it’s possible to code a product as a fun DIY SaaS experiment, it’s not advisable business-wise unless you have more than enough resources to play with.

Hence, the first and most critical step on how to create SaaS AI tools is identifying a specific market niche or a pressing problem that AI technology can address. 

To be sure you’re on the right path toward fixing a real problem, research your target audience and validate the problem by conducting interviews or surveys with potential customers. 

At times, you may think you have the perfect idea of the right solution to proffer, but after surveys, you’ll realize that your solution is far from the reality of the problem. 

Without validation, you risk building a product that no one wants or needs, wasting time and resources in the process.

In fact, 42% of startups fail because they lack market fit.

Stripe Co-Founder and CEO Patrick Collison’s View on Solving Problems

Collison is a staunch advocate for starting small and solving one specific problem. 

Growing up in rural Ireland, he experienced continuous poor internet connectivity, which drove him to make his first pitch to his parents when he was only 13. 

Collison’s pitch was to create a long-lasting satellite internet solution to the imminent problem his family and neighbors were facing.

While it didn’t work, it set the foundation for his learning programming languages, which led to the creation of Stripe—another real-life solution to payment hurdles.

Stripe started by simplifying payment processing, focusing on a niche pain point for developers. 

He suggests deeply understanding the problem you’re solving before scaling up.

SaaStr CEO Jason Lemkin’s View on Product Market Fit (PMF)

Lemkin throws his weight behind finding a PMF before an actual product development. 

He strongly opines that before you take the step to create a SaaS solution, you must have found at least ten to twenty potential customers who genuinely need your product.

For best results, you may avoid family and friends because they always have your back and can’t always be considered strong critics of your idea.

Here are a few questions you should consider when building an AI software:

• What problem does your AI SaaS product aim to solve?
• Who are your target audiences, and what are their needs and pain points?
• What unique value does your product offer among your competition?
• What should the users’ journey be from visiting your platform to achieving their purposes?
• Does your product fit your company’s business goals?

2. Define AI Capabilities and Scope

Since you’re not building just any tool but an artificial intelligence SaaS, you need to determine whether AI has any place in the product design process.

If yes, the next step on how to create AI software is to decide what AI technologies will be at the core of your solution. 

Depending on your problem, consider whether you need machine learning, natural language processing, or computer vision.

It’s essential to define the scope early on to avoid feature creep. 

One prevalent problem several AI SaaS builders make is trying to build an all-in-one tool that can literally do anything and everything (if possible). 

From personal experience, this will get you frustrated because you’ll hit a brick wall, forcing you to go back to square one.

It’s better to start small with a well-defined set of AI features and plan to expand later as you gather more data and user feedback. 

Since customers almost never use about 80% of the features in a typical SaaS product, this approach ensures you deliver a product that works and grows sustainably as the need arises. 

More so, it helps you go to market faster.

HubSpot CTO Dharmesh Shah’s View on AI

In a detailed LinkedIn post published in May 2024, Shah gave his small business creation predictions for the near future.

According to him, AI will help SaaS companies and other outfits to easily create businesses that can scale and grow.

Knowing you can always leverage the vast potential of AI when you need it, there’s no point rushing things. 

Start somewhere and grow at your own pace.

3. Assemble a Capable Team

If there’s one thing I’ve learned about Elon Musk, it’s the fact that having a formidable team and visionary leadership can withstand any storm.

Building a team is not a license to onboard every Tom, Dick, and Harry that crosses your mind.

Among the 90% of startups that fail, 23% of them were a result of having a weak founding team.

As such, when you want to create AI software, hiring competent and self-motivated people ready to grind with you until the brand succeeds is important. 

While some business leaders and coaches may preach about redundancy and the need to have a large team, you should know that startups with 11 to 50 employees have a higher chance of failure than those with ten or fewer.

Once you have identified the critical features and functionalities required for your AI SaaS product, you must assemble the right team to bring it to life. 

You could consider augmenting with external AI expertise if you already have a working team. 

Supercell CEO Ilkka Paananen’s View on Team Building

One of the best games I’ve enjoyed is Clash of Clans, which Supercell developed. 

After playing for a few years, I did a little digging to know the team behind the project. 

That was when I realized the Clash of Clans team initially comprised only 12—resourceful, committed, and result-driven—developers.

According to Paananen, the game’s secret sauce is the team’s high excellence quotient. 

He believes that having a small team with autonomy is the foundation of innovation, without which a SaaS company is just another regular brand.

4. Selecting the Technology Stack: 

This is one of the most important steps on how to create an AI software.

A tech stack is the backbone of your AI-based SaaS product and is necessary for scalability, flexibility, and security.

You need to decide on it even before you build your MVP. 

I dedicated an entire section in the later part of this article to discuss the 9 factors to consider when selecting a technology stack to create a SaaS AI tool.

Shopify CEO Tobias Lütke’s View on Tech Stack

Tobias Lütke has always discussed the importance of making the right technological decisions early to support scalability and adaptability.

He highlighted that Shopify’s early success was partly due to choosing the right technology stack, which allowed them to build a highly scalable platform. 

This choice helped Shopify scale from supporting small merchants to powering major enterprise stores.

5. Develop a Minimum Viable Product (MVP)

An MVP is a basic product version that contains your idea’s most important features and solves the identified core problem. 

If there’s anything I need to emphasize the most here, it’s the fact that an MVP doesn’t have to be fancy. 

So you shouldn’t spend so much time building it. 

The goal of an MVP is to help you launch early and get real users to interact with your SaaS AI tool.

The feedback from users will help you return to the drawing board, reiterate, and relaunch.

Apart from helping you reduce your development costs by over 70%, an MVP will hasten your time to market and increase your funding success rate significantly.

LinkedIn CEO Reid Hoffman’s View on MVP

No one advocates about launching early like Reid Hoffman, even if your product is a mess.

After quitting PayPal, Hoffman recruited some of his erstwhile colleagues to build LinkedIn, and the platform launched within six months.

During one of its early funding pitches, LinkedIn was considered a “Friendster for business” mainly because that was all people could see about the product—a business-centered clone of the once popular social media platform.

Then, the app could pass for a haunted house with poor designs and features. In fact, it was impossible to search for people outside your network.

The LinkedIn Hoffman and his team launched in 2003 is a far cry from the one everyone knows today. 

But all the growth and success the product enjoys now wouldn’t have been possible if they hadn’t put out the seemingly terrible MVP then.

See Also: 6 Benefits of AI in SaaS Solutions With Use Cases

6. Leverage AI to Automate, Optimize, and Develop Further

The user feedback from your MVP is fuel for the next step of your AI SaaS development journey.

Make all necessary changes in line with what you’ve learned, and focus on automating the processes using artificial intelligence. 

AI helps SaaS companies streamline repetitive processes, analyze large data sets, and make personalized recommendations. 

Most importantly, ensure that the automation adds value to the user experience by solving pain points and saving time. 

As you scale, keep refining the AI algorithms to adapt to user behaviors and changing business needs.

Salesforce CEO Marc Benioff’s Views on Automation

As the leader of one of the world’s largest enterprise AI suppliers, Mark Benioff has always pushed for automation to create scalable solutions. 

He believes automation should make complex processes simple for the end user.

After officially launching Agentforce V1, codenamed “Einstein Copilot,” in February 2024, Salesforce needed to optimize the solution further using customer feedback and obvious results from the solution behaviors.

The eventual result led to the launch of Agentforce V2 on October 25, 2024—which Benioff claims is far better than Microsoft Copilot—and is now the rave of the moment among Salesforce clients.

Also known as Atlas, Agentforce V2 is expected to handle trillions of AI transactions per week, just like its predecessor.

7. Integrate Seamlessly with Existing Tools

Your customers already have traditional tools they use for various purposes and will not change most of them simply because you asked them to do so.

So, one of the best ways to penetrate an existing market with your SaaS AI product is to make it easily integrate into the tools and platforms your users currently rely on.

For example, if your product is aimed at marketers, it should connect easily with popular CRM systems or analytics platforms. 

Providing a robust API and offering pre-built integrations will lower the barrier to entry for users and make it easier for them to incorporate your AI solution into their existing workflows.

This, in turn, will make your product more valuable to a wider audience.

Blade Labs CEO Sami Mian’s View on Easy Integration

Blade Labs is one of the best API-first blockchain development firms in the Web3 world. 

One notable thing I’ll always remember from my days as a copywriter at Blade Labs was Sami Mian’s emphasis on achieving frictionless integrations.

Although the brand has several amazing solutions like Blade Wallet, the one I loved the most was the Whitelabel solution. 

The Whitelabel solution offers one-click integration on clients’ platforms, allowing them to experience the power of Web3. 

The tool’s performance was amazing, but what was more outstanding was the ease of integration.

8. Scale and Iterate Based on Data

Scaling your AI-based SaaS product involves not just adding new features but also improving the efficiency and effectiveness of your existing ones. 

Analyze the user data you collected from your MVP and other versions of your product to know what to modify in your process and output. 

AI thrives on data, so as you scale, you’ll have more data to refine your models, improve decision-making, and optimize performance. 

Continuous iteration is key to staying competitive, as new advancements in AI will require you to adapt your solution over time.

Various tests to conduct during this period include:

• Unit tests ensure that every individual component functions according to specifications.
• Integration tests see to it that different software parts work seamlessly and reliably with the app.
• System tests assess the overall application’s performance, responsiveness, and stability.
• Regression tests are carried out to ensure changes and updates to existing software do not affect the functioning features.
• User Acceptance Testing (UAT) is the final stage of software development, during which end users or business representatives test the product in the real world and give feedback.

CEO of Dropbox Drew Houston’s View on Scaling

Drew Houston strongly believes that the fundamental goal of scaling should be to achieve a better customer experience.

He often points out that as a product grows, the focus needs to shift towards refining performance and user relevance rather than just feature expansion. 

For example, as Dropbox grew, Houston and his team continuously improved their product based on customer feedback, focusing on ease of use and infrastructure reliability.

This approach enabled Dropbox to handle its growing user base effectively, ensuring that its core promise, “it just works,” remained intact even at a larger scale.

9. Monetize and Optimize Customer Retention

After building a user base, it’s time to monetize and focus on customer retention. 

AI SaaS businesses typically offer subscription models, but you can also consider pay-as-you-go or tiered pricing depending on user needs. 

Whichever model you opt for, artificial intelligence can find expression in all areas of the monetization journey, including marketing and personalization.

AI can help predict which users might churn, allowing you to engage them with tailored offers or content and improving your long-term profitability by keeping customers happy.

Okta COO Frederic Kerrest’s Views on Net-Dollar Retention

As the co-founder of an enterprise software firm, Frederic Kerrest insists that SaaS companies must be deliberate about pricing and retention.

That’s why Okta focuses on understanding customer usage to optimize renewals, which is crucial for AI SaaS to avoid churn.

However, he believes having happy customers isn’t enough. 

The cofounder opines that customer retention that doesn’t translate to net-dollar retention will send small businesses into bankruptcy.

Net-dollar retention means a favorable balance sheet when a firm’s financial expenditure on the business is compared to what customers pay for subscriptions.

In essence, AI SaaS companies must be profitable if they must survive harsh economic conditions.

10. Ensure Ethical AI and Compliance

My last step on how to build an AI system is to take adequate precautions to shield yourself from legal risks.

The Authors Guild lawsuit against OpenAI is one of many similar lawsuits we’ve seen since this latest revolution of AI started gaining mainstream adoption.

With growing concerns around data privacy and the ethical use of AI, it is essential to ensure your AI-based SaaS product follows the latest regulations and ethical guidelines. 

This includes implementing transparent data handling practices, preventing bias in AI models, and ensuring users understand how their data is being used. 

Failure to comply with regulations like GDPR could result in heavy fines or loss of customer trust. 

Generally, building ethical AI principles into your product from the start creates a foundation for long-term success.

OpenAI CEO Sam Altman’s View on Ethical AI

Since ChatGPT launched, Sam Altman has conducted several interviews focused on safety concerns and not just the power of the tool.

He stresses that fairness is not just about accuracy but ensuring that AI systems do not propagate biases inherent in their training data.

Nonetheless, despite his opinions on fairness, users have reported algorithmic biases on ChatGPT, especially concerning liberal ideologies and the US November 2025 election.

What to Consider When Choosing Technology Stack for AI SaaS Products

One of the most important things to consider when searching for how to build an AI platform is the underlying infrastructure to use. 

Finding the right tech stack can be daunting and overwhelming due to the extensive volume of tools available.

You can rely on these nine parameters to make your decision easier:

1. Type of AI Application

As mentioned earlier, you need to determine early whether your SaaS will involve machine learning, natural language processing (NLP), or computer vision.

In the same vein, some AI-based SaaS products require large-scale batch processing, while others simply work with low-volume real-time data processing.

Decide on these early, as they will influence the type of AI frameworks you will adopt.

2. Scalability

Two of the major considerations for scalability are cloud infrastructure and microservices. 

Generally, AI models are resource-intensive; hence, you need a scalable cloud infrastructure such as Google Cloud, AWS, or Microsoft Azure.

Tools like Docker and Kubernetes allow your AI SaaS product to scale efficiently and independently by breaking it down into microservices.

3. Speed and Performance

If you’ve ever used ChatGPT for any form of research, you’d realize that results don’t just pop up immediately after you type in your search query.

It takes a “few seconds” to process the data at incredible speeds.

ChatGPT’s “few seconds” processing timeframe is thanks to OpenAI’s multibillion-dollar infrastructure. 

Since you may be unable to afford such supercomputers, choose tech stacks that will allow you to leverage their GPUs or TPUs for faster computation and lower latency, especially in tasks like deep learning.

4. Security and Data Privacy Compliance

As I mentioned earlier, privacy breach lawsuits are springing up here and there against AI projects due to the sensitive data they crawl. 

To protect your startup from being slammed with a subpoena, ensure you work with a stack that meets the regulatory requirements of GDPR, CCPA, or HIPPA, as the case may be. 

Similarly, choose frameworks that simplify adding robust security mechanisms, such as OAuth for user authentication and multi-factor authentication (MFA)

5. Ease of Development and Maintenance

The stack should align with the skillsets of your development team. 

Choosing technologies your team is familiar with reduces development time and the learning curve.

Also, frameworks and tools with strong community support can provide quicker access to troubleshooting resources, libraries, and updates.

6. API Integration Capabilities

A good stack for building a machine learning SaaS tool should support integration with third-party APIs and services, such as payment gateways, analytics tools, or customer relationship management (CRM) software.

If you plan to use third-party AI as a service (AIaaS) like Google AI and IBM Watson, ensure the technology stack supports easy integration with those platforms.

7. Cost-Effectiveness

Cost should be one of your major considerations when you want to start an AI company. 

If you don’t have a fat budget, you may consider working with open-source frameworks like Python libraries (e.g., scikit-learn, TensorFlow).

With good funding, enterprise-grade services are definitely better because they offer better security, scalability, and support. 

Another important cost-intensive factor is your cloud infrastructure. Calculate the long-term price implication before you agree to use anyone. 

8. Future-Proofing and Flexibility

AI developments are moving at great speeds; what seems to be in vogue today may be out-phased in a few months. 

Hence, the right stack to build AI products should be able to accommodate future AI advancements and support features you might add later. 

Likewise, it should be flexible enough to allow you to deploy your SaaS solution on mobile, web, and desktop.

9. Data Management

AI often requires fast access to large datasets for training, testing, and inference.

Hence, choose a database that can handle structured and unstructured data efficiently.

Use ETL (Extract, Transform, Load) tools or data pipeline solutions like Apache Kafka or Airflow to manage and preprocess data before feeding it to your AI models.

Conclusion

If you read to this point, you deserve an accolade, and I’m certain you’ve learned the essentials of starting an AI company.

Without a doubt, it requires careful and strategic planning, execution, and consideration of technical and business factors. 

Leveraging AI capabilities in your SaaS solutions helps you create software that drives business growth and enhances user experiences.

Also, you need consistent monitoring, optimization, and adaptability to achieve immense success and user relevance.

FAQs 

1. What Is the Best Programming Language SaaS?

There is no best programming language for building SaaS tools. It all depends on the type of product you are building and the goals you want to achieve. However, Python is one of the best languages to consider. 

2. Which Framework Is Best for AI Development?

AI frameworks are the building blocks for creating intelligent AI systems and a foundation for implementing machine learning and deep learning algorithms. Some notable top AI frameworks include TensorFlow, Keras, Pytorch, Google Cloud A,  and IBM Watson.

3. Are SaaS Products Profitable? 

SaaS companies hold a promising return on profit margin and revenue. However, a SaaS company’s success mostly depends on the team and leadership efforts.

4. How Long Does it Take to Build a SaaS Product?

It takes about 3 to 10 months to build a SaaS product from conception to completion.

5. How to Create Your Own AI Software

You can build a DIY SaaS tool by first finding product market fit, setting up the right team, selecting a scalable tech stack, and creating an MVP.