Generative AI Pushed Us to the AI Tipping Point

Before artificial intelligence (AI) was launched into mainstream popularity due to the accessibility of Generative AI (GenAI), data integration and staging related to Machine Learning was one of the trendier business priorities. In the past, businesses and consultants would create one-off AI/ML projects for specific use cases, but confidence in the results was limited, and these projects were kept almost exclusively among IT teams. These early AI use cases required dedicated data scientist teams, too much time and effort to produce results, lacked transparency and the majority of projects were unsuccessful.

From there, as developers grew more comfortable and confident with the technology, AI and Machine Learning (ML) were more frequently used, again, mostly by IT teams because of the complex nature of building the models, cleaning and inputting the data and testing results. Today, with GenAI being inescapable in professional and personal settings all around the world, AI technology has become accessible to the masses. We are now at the AI tipping point, but how did we get here and why did GenAI push us to widespread adoption?

The Truth About AI

With “OpenAI” and “ChatGPT” becoming household names, conversations about GenAI are everywhere and often unavoidable. From business uses like chatbots, data analysis and report summaries to personal uses like trip planning and content creation, GenAI is quickly becoming the most discussed technology worldwide and its rapid development is outpacing that which we have seen with other technological innovations.

While most people know about AI, and some know how it works and can be implemented, public and private sector organizations are still playing catch-up when it comes to unlocking the full benefits of the technology. According to data from Alphasense, 40% of earning calls touted the benefits and excitement of AI, yet only 1 in 6 (16%) S&P 500 companies mentioned AI in quarterly regulatory filings. This begs the question: what are the financial impacts of AI and how many companies are truly invested in its adoption?

Rather than jumping on the AI bandwagon just because it is trendy, enterprises need to think about the value AI will bring internally and to their customers and what problems it can solve for users. AI projects are generally expensive, and if a company jumps into using AI without properly evaluating its use cases and ROI, it could be a waste of time and funds. Customer private previews provide a controlled way to confirm product market fit and validate the associated ROI of specific use cases to validate the value proposition of an AI solution before releasing it into the market.

What Vendors Need to Know Before Investing in AI

To invest in AI, or not to invest in AI? This is an important question for SaaS vendors to consider before going all in on developing AI solutions. When weighing your options, be mindful of value, speed, trust and scale.

Balance value with speed. It is unlikely your customers will be impressed just by the mere mention of an AI solution; instead, they will want measurable value. SaaS product teams should start by asking if there is a real business need or problem they wish to address for their customers, and whether AI is the proper solution. Do not try to fit a square peg (AI) into a round hole (your technology offerings). Without knowing how AI will add value to end-users, there is no guarantee that someone will pay for those capabilities.

Build trust, then scale. It takes a lot of trust to change systems. Vendors should prioritize building trust in their AI solutions before scaling them. Transparency and visibility into the data models and results can resolve friction. Let users click into the model source so they see how the solution’s insights are derived. Most reputable vendors can also share best practices for AI adoption to help ease potential pain points.

Common Obstacles for Tech Vendors: AI Edition

For organizations ready to embark on the AI journey, there are a few pitfalls to avoid to ensure optimal impact. Avoid groupthink, and do not follow the crowd without knowing where you are headed. Have a clear strategy for AI adoption so you can reflect on your end goals and confirm the strategy aligns with your organization’s mission and customer values.

Bringing an AI product to market is not an easy task and the failures outnumber the successes. The security, economic and talent risks are numerous.

Looking solely at security concerns, AI models often hold sensitive materials and data, which SaaS organizations need to be equipped to manage. Things to consider, include:

• Handling Sensitive Materials: Sharing sensitive materials with general purpose large language models (LLMs) creates the risk of the model inadvertently leaking sensitive materials to other users. Companies should outline best practices for users – both internal and external – to protect sensitive materials.
• Storing Data and Privacy Implications: In addition to sharing concerns, storing sensitive materials within AI systems can expose the data to potential breaches or unauthorized access. Users should store data in secure locations with safeguards to protect against data breaches.
• Mitigating Inaccurate Information: AI models collect and synthesize large amounts of data and inaccurate information can easily be spread. Monitoring, oversight and human validation are necessary to ensure correct and accurate information is shared. Critical thinking and analysis are paramount to avoiding misinformation.

In addition to security implications, AI programs require significant resources and budget. Consider the amount of energy and infrastructure needed for efficient and effective AI development. This is why it’s critical to have a clear value proposition for customers, otherwise, the time and resources put into product development is wasted. Understand if your organization has the foundation to get started with AI, and if not, identify the budget needed to catch up.

Lastly, the talent and skill level risks should not be ignored. General AI development involves a dedicated group of data scientists, developers and data engineers, as well as functional business analysts and product management. However, when working with GenAI, organizations need additional security and compliance oversight due to the security risks noted earlier. If AI is not a long-term business objective, the costs for recruiting and reskilling talent are likely unnecessarily high and will not result in a good ROI.

Conclusion

AI is here to stay. But, if you are not thinking strategically before joining the momentum and funding AI projects, it can potentially do more harm than good to your organization. This new AI era is just beginning, and many of the risks are still unknown. As you are evaluating AI development for your organization, get a clear sense of AI’s value to your internal and external customers, build trust in AI models and understand the risks.