5 Practical Implementations of AI and ML in SaaS Product Management

Artificial intelligence (AI) was once predominantly the domain of well-funded tech companies. But now, businesses needing innovation are looking to benefit from it. And globally, organizations are considering AI and ML, discovering benefits in their operations.

A McKinsey report suggests 61% of high-performing enterprises have resorted to AI deployment in response to COVID-19.

An optimistic projection for the next several years anticipates a CAGR above 25% for the SaaS industry. Simply staying afloat in such a competitive market requires a SaaS to adopt AI, deep learning, and machine language (ML) as data science drives modern business processes.

Implementing ML and AI in product management offers a more efficient, faster, and more effective way to drive growth. Such prowess equips workers with better means of doing their jobs with the right product management tool.

AI and ML are altering the world and software product management as SaaS product management adopts these technologies.

Here’s why —

• AI and ML are used in almost every software.
• Product development demands everyone to know the possibilities that AI and ML often unlock.
• Learning the AI and ML language help SaaS team members ask appropriate questions that help drive product innovations.

Here are the five common implementations to begin AI & ML for your SaaS product management.

1. Intelligent UX

AI and ML help SaaS product managers categorize data based on demographics, behavior, and pattern. This helps tailor each user’s experience using data collected from past queries.

Modern-day computers mine a huge pile of human behavior data for every conceivable trait, which is then fed into algorithms that produce actionable patterns. This is called intelligent UX, where each part of the product’s usability goes towards improving user experience.

For example, Oracle is moving beyond the general interface for everyone. Their SaaS applications offer a more individualized and customized user experience, complete with content uniquely suited to customers’ needs. Oracle’s intelligent UX is bringing this to corporate applications.

Intelligent UX collects information about the user’s position and work habits, then uses AI to provide suggestions that improve the user experience and productivity.

The AI-enabled features make using business software a more natural and satisfying experience for the team. It encourages timely and efficient actions that provide more successful outcomes by enhancing accuracy.

2. Release management

Artificial intelligence can improve SaaS developers’ code by checking it for errors. It can improve their coding abilities by running necessary tests to guarantee the code is correct.

A single, widespread bug in SaaS’s code can slow down the service for everyone and may have devastating financial repercussions.

Releasing a SaaS product requires a secured, clean code but within the timeframe. Therefore, using AI as a part of SaaS product releases helps optimize time-to-market from months to weeks. Here, AI validates the software code quality of the code and its overall scalability for all its users.

A good example of this is Docker. It runs tests and does code analysis for rapid deployment.

Also, Microsoft and the University of Cambridge work to teach AI to write computer programs.

3. AI in business process

SaaS product management involves evaluating critical business processes to identify where to implement AI that complements the workforce capabilities. This requires product managers to consider overall company performance and align that with customer expectations to integrate AI features in their products.

For instance, it requires identifying the part of a business process that can hugely benefit from AI — payables, receivables, costs, vendor management, etc.

Oracle leverages AI in finances to automate several important operations, bringing businesses closer to its goal of a touchless back office enabled by continuous closure capability.

SaaS developers can use AI capabilities with immediate effect inside a business process. It includes automatically defaulting values during invoice processing or spotting and sorting redundant transactions in receivables.

4. NLP and collaborative filtering

Natural language processing (NLP) is the area of AI that enables computers to understand written and spoken words just as humans do.

There’s a wide variety of human speech (depending on ethnicity and culture) for which NLP works to decipher and leverage AI to offer a satisfactory response. A SaaS product management will need NLP if it deals with translations, text-to-speech, or vice-speech. Knowing what customers expect from a SaaS product requires the power of ML.

For instance, Amazon uses ML to predict queries that people would use. Its smart speaker (Echo with Alexa) anticipates specific queries (music, weather, news, etc.) using ML and lets it respond using AI.

Moreover, Amazon also uses the same ML algorithms to optimize its packaging.

Another example is Netflix. It uses ML to recommend content to its user who is likely to watch those titles. It leverages collaborative filtering algorithms to build a model that considers users’ ratings of movies and pairs them with others with similar viewing preferences.

5. Enhanced security

The top 5 breaches of 2021 show how vulnerable the software industry is. Traditional security mechanisms are static and often fall flat by relying on human input to update for new threats. This shows why emphasizing cloud security is essential for SaaS.

AI can enable security services for SaaS product management. It can automatically learn from new security risks to evade breaches of a similar type. For instance, Oracle incorporated ML and AI into its cloud security services, allowing for automatic threat identification.

It is ideal to follow cybersecurity testing for a SaaS solution (over a conventional tool) that helps ensure the continued security of operations and products. Usually, such a solution offers two data centers in a different location than the other, each housing critical IT infrastructure used to distribute applications.

Consider Sophos, for example; it provides data security services in the cloud. The security software and hardware company incorporates AI in cybersecurity to execute its business’ information security objectives.

Wrapping up

SaaS and cloud-based companies are redefining how AI and ML impacts business outcomes and clients. Businesses looking to stay relevant and create a difference would resort to modern technologies, guarantee compliance and ensure consumer data security — all of which is possible with AI and ML.

AI holds the key to the future of product management that simplifies operations, maximizes efficiency, and drives data-backed decisions. ML helps product managers identify what customers like/dislike, enabling key decision-makers to respond to user challenges and enhance the whole process.

Originally posted on OpenDataScience.com

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