How AI can help your software company move faster

The upshot for software companies is that they have more powerful tools than ever to meet customer demands.

“Software companies are certainly feeling the pressure,” says Garry Ma, founder and CEO of Toronto-based AI Ample Insight, a Canadian AI and data consultancy that helps companies build AI systems and modernize data infrastructure. “But there’s also a huge opportunity for them to use these technologies to add speed and efficiency.”

According to Ma, AI is helping software companies speed up product development, reduce routine work, and add features to products. But many of these businesses are still struggling to determine where AI can genuinely add value—and where to begin.

Advantages of AI for software companies

One major shift is the rise of AI-assisted software development.

AI Tools can generate software code, create prototypes and automate certain testing and documentation tasks. This has lowered the barriers to building software products and increased competition across the industry.

“One engineer can now do work that previously required several specialized developers,” says Ma. “AI allows teams to prototype and iterate much more quickly.”

For example, AI can help developers:

• generate code more quickly
• automate aspects of quality assurance and software testing
• review code for errors or inconsistencies
• create documentation
• develop applications for multiple platforms simultaneously

AI is also making it easier and cheaper for companies to test ideas before investing heavily in development.

Overall, this acceleration is changing the economics of software development. Smaller companies can compete in highly specialized niches because the cost of creating software has fallen significantly.

AI is changing customer expectations

Not only are software companies using AI themselves, but their customers increasingly expect AI capabilities in the products they buy. For example, some business users want software that can answer questions in natural language, generate reports automatically, and derive insights from large datasets. Others may want to reduce manual workflows or personalize user experiences.

“Customers are asking for AI-enabled features,” says Ma. “Software companies know they need to add them, but many don’t know how to get started or what the return on investment will look like.”

Some firms are responding by embedding AI assistants or chat-based interfaces into products, but the opportunity goes beyond simple chatbots. These tools enable their users to ask questions conversationally, much like they would ask an analyst or technical team member, and get immediate answers and insights without waiting for custom reports, relying on technical staff, or navigating complex workflows and pages to tailor reporting themselves.

Where companies are seeing the biggest gains

Many of the biggest productivity improvements for software companies are happening in:

• prototyping
• coding assistance
• testing and quality assurance
• internal knowledge management
• customer support
• reporting and analytics

For example, AI can help your customer-support team summarize tickets, analyze issue patterns, draft responses or retrieve relevant documentation more quickly. It can also help employees search large volumes of company information and retrieve insights.

These types of use cases often provide returns more quickly than more ambitious AI projects, says Ma. “Companies sometimes think they need to build a massive AI platform right away. But often, the biggest value comes from solving very specific operational bottlenecks first.”

AI is not a magic bullet

Despite the many upsides, Ma cautions businesses against unrealistic expectations. He says it’s easy to underestimate the operational changes that are needed to integrate AI effectively.

“AI is not fairy dust you can sprinkle on a problem to make it disappear,” he says.

In fact, introducing AI tools may reduce productivity at first as employees adapt to new workflows and processes. Ma compares this to a “J-curve” effect, where productivity dips before bouncing back and climbing once teams gain experience and systems mature.

“The goal is to minimize the depth and duration of the dip,” he says.

It also pays to have a clear objective for adding AI. Otherwise, companies risk investing in tools that don’t solve a meaningful business problem. Some common mistakes include:

• implementing AI without identifying a real business problem
• trying to build custom AI systems too early or when they’re not needed
• neglecting data quality and governance
• not giving employees enough training

“There’s still a lot of noise and hype,” says Ma. “It pays to be very clear about what problem you’re trying to solve.”

How to get started with AI

In most cases, your first step is to identify operational bottlenecks or customer pain points that you think existing AI tools might address. Then evaluate whether AI is actually the right solution to these particular problems.

“The companies that succeed usually start with focused use cases,” says Ma. “They experiment, learn what works and build from there.”

In many cases, off-the-shelf tools may be enough. Or there might be a platform that allows you to add AI capabilities to your existing software rather than building from scratch.

At a more operational level, you could also experiment with AI coding assistants, AI-powered testing tools, customer-support copilots, internal knowledge-search tools, analytics and reporting assistants.

Whether you choose to build a custom solution, adopt an off-the-shelf tool, or work with an outside partner depends on your situation. Custom development can make sense when the use case is specialized, existing tools fall short or your team has the technical expertise to implement and maintain the solution—or when AI capabilities are central to the product itself.

For most smaller companies, off-the-shelf tools or an external partner will be a faster and more cost-effective starting point. When a project is complex or business-critical—for example, if it involves sensitive data, integration with existing software, or scaling beyond initial experiments—outside expertise can help you avoid costly mistakes.

Ultimately, AI is becoming less of a specialized innovation tool and more of an operational capability—one that could reshape how software companies build products, serve customers and compete in the years ahead. For businesses, the key is not adopting AI everywhere at once, but identifying where it can solve real business problems and deliver measurable value.

Next step

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