Artificial Intelligence research has been around for many decades and till November of 2022 (ChatGPT burst into the scene and broke all tech adoption records by miles), when one referred to AI, there was no confusion. There was only “one type of AI” in the minds of users. But yes, the terms AI and ML (Machine Learning) were often interchangeably used (wrongly) by many folks.
AI – in a simplistic view – performs tasks that typically require human intelligence – reasoning, learning, problem-solving, perception, and language understanding. It encompasses an expansive range of technologies and approaches including rule-based systems, expert systems, and neural networks. There are many applications where we see AI being used such as robotics, natural language programming, computer vision, and autonomous vehicles, among many others.
Machine Learning is a subset of AI that focuses on algorithms that allow computers to learn from and make predictions or decisions based on data. It specifically deals with training models on data to improve their performance over time and the “magic” is that the programming does NOT have to be explicit for each task – so the model self-learns, just as a human does.
So, what we have are predictability and prescriptiveness. The intelligent system can analyze and then predict what’s coming next and what you should do to mitigate risks and enhance growth and productivity which is the prescriptive part.
Generative AI on the other hand can create new content such as text, images, videos, and music based on patterns and data. Gen AI models are trained on large datasets to understand the patterns and structures within the data.
Common techniques include deep learning and neural networks, particularly models such as Generative Adversarial Networks (GANs) and Large Language Models (LLMs) like GPT-4. Much of this development and research has been going on for well over a decade but it’s the last 18 – 20 months which has seen the incredible pace at which these techniques and frameworks are being adopted by organizations.
That’s why today when we refer to Artificial Intelligence, we must distinguish whether we are referring to “Traditional AI” or Generative AI.
Importance of Generative AI in modern SaaS solutions
Generative AI continues to revolutionize modern SaaS in several ways that are tremendously impactful. Let’s look at a few aspects:
- Hyper-personalization: Gen AI by analyzing users’ data generates tailored content suited to individual preferences. This creates a highly personalized user experience which leads to higher C-SAT and engagement.
- Automated content creation: It can generate a massively expansive range of content – marketing materials, customer support responses, and images for branding. This enhances and speeds up productivity, freeing up human hours for better utilization. Most importantly the speed at which new content is created is truly remarkable.
- Improved decision-making: Since it’s trained on expansive datasets, Generative AI helps organizations make more informed decision-making.
- Cost efficiency: Automating content generation massively reduces the cost of content creation, and it can be done with great speed.
- Scalability of content creation: Human effort constraints can be removed – of course, human expertise will still be required to verify and substantiate accuracy.
- Enhanced security: In theory, it can generate security protocols and monitor systems for potential attacks. But it’s not infallible – because the same technology is also being used by threat actors to effect much more sophisticated attacks that are becoming harder to detect.
Current trends in Generative AI for SaaS
Generative AI is significantly transforming how businesses operate and engage with the technology. According to Statista, the market size of Generative AI in 2024 is 36 billion dollars. They envisage that the CAGR over the next 5 – 6 years will be upwards of 45 – 46%.
Bloomberg, last year predicted that by 2032, Generative AI will be a 1.3 trillion-dollar market and will grow at 42% over the next decade. Whether it turns out to be a trillion-dollar market or not, at this point we don’t know, but what we do know is when a technology is expected to grow at this pace, one has to embrace it and transform the delivery model accordingly (if need be).
Here’s another projection of the market:
This, of course, is the total impact of Gen AI in business, and at this point, it’s difficult to quantify the impact it will have on SaaS in particular but here are some ways it could impact SaaS businesses:
1. Enhanced customer experience
Employee productivity and customer satisfaction are interlinked. Customers want personalized, multi-modal, and quick responses with continuity. A major pain point is when they have to repeat context every time the human touchpoint changes.
GenAI-powered virtual assistants can minimize human touchpoints and deflect the case to a human agent only when it’s necessary. Analysis of customer conversations ensures a better understanding of customer pain points and contexts. This can lead to more proactive recommendations. Routine queries and customer service tasks can easily be automated, freeing agents’ time.
Essentially there are three ways, Gen AI can massively impact customer experience:
- It can enable personalization at scale and provide a touchless experience to the extent possible.
- GenAI can also verify customer contracts to ensure compliance.
- Customer self-assist boosts Customer Lifetime Value.
- Employee self-assist. When an issue occurs, GenAI-powered solutions can proactively alert customer support to take the necessary steps to resolve it. It saves time and enables much faster resolution. A job done well enhances employee experience and customer experience simultaneously.
Similar functionalities are beneficial to vendors/partners of the SaaS business. Given the complex nature of the environment, the great significance of partnerships cannot be over-emphasized. GenAI can help partners during onboarding by answering queries. While also leveraging predictive analytics, Gen AI can proactively identify opportunities for partnership expansion and make recommendations to unlock value through expanded partnership opportunities.
2. Automation of business operations
Generative AI is streamlining operations in customer service and marketing. For instance, Gen AI-based chatbots can handle customer inquiries with efficiency and speed. Based on customer interaction and sentiment analysis, these chatbots can also help agents write emails that create appropriate messaging. In turn, all such interactions keep adding to the knowledge repository data which can be further used to improve model performance.
3. Multimodal AI solutions
The emergence of multimodal Generative AI solutions helps businesses interpret and connect information across data types – text, images, and audio. Insights can be more accurate because of the holistic interpretation. In addition, the experience is across devices – phones, computers, tabs, etc.
4. Industry-specific applications
The focus of enterprises is training LLMs on industry-specific data. Travel, BFSI, Automotive, it can be anything. ChatGPT can use the internet to learn a broad range of topics but that’s of little practical use to a user who needs to make certain decisions in his industry. Businesses need to train models specific to their sector, otherwise the insights will be generic. Here are some examples of industry-specific LLMs:
- BloombergGPT – A causal language model designed for the finance industry, trained on decades-worth of domain-specific data.
- Med-PALM 2 – A custom language model built by Google for the medical industry, capable of accurately answering medical questions at par with medical professionals.
- ClimateBERT – a transformer-based LLM trained on climate-related data.
- ChatLAW – An open-source language model specifically trained with datasets in the Chinese legal domain.
5. Focus on responsible AI
Gen AI has drawn massive excitement across industries. And it has the potential to disrupt business in a way we could never have imagined. Tech disruption is nothing new, but this is the first time we can interact with technology just as we do with humans. The possibilities are immense but it also raises significant concerns, particularly regarding fairness, bias, and the potential for misuse, such as the creation of deep fakes or the generation of harmful content. Increasingly, businesses are turning their focus to acknowledge this aspect.
Key applications of Generative AI in SaaS platforms
1. Content creation
Generative AI can automate content creation – blog posts, product descriptions, and marketing materials. It is particularly beneficial for content management systems and e-commerce platforms – high-quality content can be created quickly and efficiently. Tools like WordPress and HubSpot have integrated generative AI to enhance their content creation capabilities.
2. Personalized recommendations
The vast majority of businesses recognize the critical importance of personalized experiences for users. In fact, nearly 89% of business leaders view personalization as a key driver for their company’s success in the coming years.
By analyzing user behavior, and preferences and using sentiment analysis, user experience can be greatly enhanced. So whether it’s your shopping experience on e-comm platforms or content consumption on platforms like Amazon and Netflix, AI-powered personalization is centered around a deep understanding of behaviors and preferences that lead to a more engaging experience.
3. Design automation
Gen AI can simplify graphic design tasks by enabling users to create great-quality graphics, illustrations, and layouts. Applications like Canva and Adobe Creative Cloud leverage AI to automate design processes, making advanced design capabilities accessible to all users.
4. Language translation
SaaS platforms such as Slack and Zoom employ these capabilities to break down language barriers, enhancing user interactions across diverse linguistic backgrounds.
5. Synthetic data generation
Gen AI can create synthetic data samples based on existing datasets, which helps improve the performance of machine learning models. It’s of great value to business intelligence tools, enabling companies like Tableau to derive deeper insights from their data analyses.
6. Customer support automation
Gen AI-based virtual assistants can handle inquiries 24/7. This improves response times and human agents can focus on more complex issues. By automating routine customer interactions, SaaS companies can significantly enhance C-SAT and operational efficiency.
7. Software development assistance
Developers can generate code snippets, and documentation, and even obtain debugging support through natural language interactions.
Challenges of adopting Generative AI in SaaS
- Data – Over time, companies have adopted multiple systems that don’t talk to each other. Data lies in silos so imperfection or incompleteness of data results in LLMs not being trained adequately. If companies are to reap the benefits of Gen AI, they must invest in a robust data foundation.
- Explainability – Given the dense nature of the models, sometimes it works like a Black Box. For instance, if a doctor is recommended an action by the model, there may not be clear visibility how it was arrived at.
- Ethical concerns, bias – this is a result of biases that exist in training data leading to ethical dilemmas. Organizations must identify and mitigate these biases to ensure fair outcomes. This involves conducting fairness checks, using diverse datasets for training, and establishing ethical guidelines for AI usage. Transparency in AI decision-making is also crucial for building user trust.
- Data security concerns – These systems often require large datasets, which may include sensitive personal information. Compliance with data protection regulations such as GDPR and CCPA is essential, necessitating robust encryption techniques, access controls, and regular security audits to protect user data from breaches and unauthorized access.
- Scalability & performance – These systems must maintain performance levels without degradation as they handle increasing amounts of data and user interactions. Implementing scalable cloud-based solutions and optimizing AI models for efficiency are necessary steps to ensure smooth operation under heavy loads.
- Complexity of integration – Integrating generative AI into existing SaaS platforms can be complex. Organizations need to ensure that AI technologies seamlessly fit into their current workflows and systems. This requires careful planning and consideration of the specific use cases for AI, as poorly executed integration can lead to inefficiencies.
The future of Generative AI in SaaS: What’s next?
In a SaaS model, users access the software through a web browser where the service provider centrally hosts, updates, and manages the software. With this centralized model – users needn’t worry about installing or maintaining the software on their local devices – SaaS developers can’t rely on sending out occasional updates. SaaS developers must continually innovate and update their products. And they’re now looking to AI to drive that process.
Interoperability is a big one. The seamless exchange of information and functionality among diverse AI systems is called AI Interoperability.
Connected AI represents AI systems working together, sharing information and learning from each other to enhance overall capabilities. To achieve this, SaaS developers must create seamless integration among the various systems that constitute their SaaS offering.
And of course, we will see a massive expansion of all the use cases that were mentioned earlier
In conclusion
Generative AI distinguishes itself by its ability to create new content. The foundation of which is based on clean data – for LLMs to be adequately trained, that’s a pre-requisite. It can generate texts, code, videos, audio, and images which can have a wide range of uses in industries.
The most important aspect is about talent – is the AI talent being adequately upskilled to function seamlessly with Gen AI? Secondly, the focus on domain skills is critical as we look towards enterprise-grade generative AI being widely adopted – very industry-specific. And finally, are we using AI in a responsible manner? That’s the only way forward if we are to trust these complex systems and deploy them in mission-critical processes.
