SaaS-ify faster: How generative AI is transforming ISV operations

1. Software development

One of the major hurdles software companies face when transitioning to a SaaS business model is with code development. Decoupling services for scalability and security, building new features, and accounting for tenancy requirements all take significant time and effort. Technical debt can further bog down these tasks, leading to headaches for your developers.

Using generative AI to assist in code modernization has helped businesses like Novacomp, a leading IT company in Latin America, save weeks of time. Using Amazon Q Developer, you can ask questions, debug your code, or generate entirely new functions directly in your Integrated Development Environment (IDE). AI can also accelerate feature development by automating unit testing.

If you’re expanding your team, generative AI can streamline new team member onboarding and enhance cross-team collaboration. By using Amazon Q Business, it’s seamless to provide your employees with instant answers to organizational questions. If you’re interested in empowering your employees with generative AI, I highly recommend watching this session from re:Invent 2023.

2. Operational investigations

With limited bandwidth to cover development, support, and operations, you may be faced with a challenge: expand your team, or risk a negative customer experience. The former only adds to your bottom line, increasing the size of the fish, but so does any lost revenue from customer attrition.

This is where AI can come in handy by augmenting your existing team, limiting additional spend while helping to mitigate lost revenue. Amazon Q Developer can assist with software troubleshooting to enhance your operations and uptime.

By integrating seamlessly with operations and host management tools, Amazon Q Developer can understand your application topology and make suggestions for remediating issues. Issues that would regularly take your team hours to diagnose - looking across various logs, operational metrics, and identifying remediation strategies - can be reduced to minutes.

3. Sales and marketing content creation

Marketing takes time and money. Depending on where you are in your ISV journey, it may just not be in the budget.

AI can help you create effective advertising early in your journey at lower price points, and you’ll be able to continue enhancing your models over time as you establish your brand and catalog. By leveraging AI to build marketing content and a marketing strategy, you can generate valuable leads without blowing your budget on additional headcount. 

4. Feedback analysis

It’s critical to garner feedback from your end users early in your product’s journey to influence the product roadmap, but gathering and analyzing feedback can be a formidable task. Pulling in various formats of unstructured data across survey responses, public sentiment, and in-app ratings can take away valuable time from your team, and making sense of the data can be just as overwhelming.

Accessible AI models can provide a method to analyze open-ended responses to surveys and reviews at scale, allowing you to maximize the information gathered in each customer response without feeling overwhelmed by the volume of data coming in. With conversational analytics, generative AI can improve quality management and accelerate the discovery of insights from speech-based conversations or via direct integration to your contact center.

5.  Feature heat mapping

Using AI with your business intelligence platform can help you integrate unstructured data into your analysis of product interest and feedback.

If you’re struggling to identify actionable insights without a data analytics team, AI allows you to use natural language to ask questions about your business data. Having a strong data foundation and strategy in place can allow you to understand customer interest through actual feature usage and clickstream data patterns.

These insights will help you enhance your product roadmap and understand how your offerings are scaling individually, leading to more savings as you optimize where you put your time and resources.

6. Customer support

The customer experience is paramount to building a successful SaaS product. Very few things are more frustrating for a customer than a slow or negative support experience, and even just a few of these incidents can have a major impact to your reputation early in your product’s journey.

Amazon Q in Connect provides generative AI for customer service, enhancing customer conversations with personalized data. Amazon Q in Connect provides your support agents with real-time responses and recommended actions, reducing the time to resolution for customer issues and increasing the productivity of your existing support staff.

Generative AI for ISVs is no longer “nice-to-have”; it’s a indispensable. With the rapid pace of change in this space it can be hard to keep up, but starting somewhere is paramount to an ISVs success.

If you’re keen on making your SaaS business more profitable, I highly recommend experimenting on your own via our guided workshops. There’s workshops on how to generate effective marketing content, build autonomous agents with the MCP protocol, and many more.

If you need expert support “swallowing-the-fish”, AWS SaaS Factory can provide business guidance and solutions architects to meet you wherever you are in your SaaS journey. Through the support of AWS SaaS Factory experts, identity security platform provider CyberArk was able to reduce its time to market by 30%.

While I’ve covered a broad base of generative AI use cases for SaaS companies in this article, you may find further inspiration by looking at use cases specific to your industry. As always, if you aren’t sure where to start you can reach out to your AWS account team to get support which works backwards from your needs.

Thanks for reading, and happy building!