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AI has been around for a long time, so why is it exploding now? 

Despite the hype we’re seeing around it in the past few months, artificial intelligence (AI) technology has been around a long time, and it’s long been used to make behavioral predictions and support forecasting. But we’ve now pivoted to a discussion where we’re talking specifically about Large Language Models (LLMs) — a type of AI that’s trained around a massive data set and that’s able to generate text that’s conversational and to catalyze interactions. In some cases, the output from LLMs can be indistinguishable from a human conversation, which is in large part where both the excitement, and the concern, around generative AI stem. 

Together with our partner, Product Pair (a product innovation studio for data-intensive technology companies), we recently hosted a webinar on all things AI: how to harness its potential with Lytics, how to get the most out of both predictive and generative AI, what to look out for, and what the future may hold. Here’s some of what we learned.

Balancing widespread excitement and cultural resistance

Today, the practice of and field of AI is changing rapidly. There are a lot of players in the game, and with more businesses running on a cloud environment, it’s important to know how AI and your existing stack work together. We’re in a cultural moment of AI, and it can have a relationship with the business you’re doing now, can address relevant use cases, and can inform the future of innovation for your organization  — if done right. But with a playing field that’s constantly shifting, how can you stay informed and remain agile?

Drew Lanenga, Chief Data Scientist at Lytics, who has been responsible for building AI into the lifeblood of the company and platform for a decade, shared his perspective:

“There’s been a lot of work for a long time put into using AI to augment business systems, but right now, it’s exploding. Why? Because AI got really good, very quickly, and took people by storm. You went from Gmail completing your sentences and making suggestions for calendar items on your agenda, to ‘Here’s an interactive template and detailed agenda for your meeting happening Thursday.’ The pace of transformation and the way people are leveraging systems and tools are faster than they ever have been. Plus, everyday users can now truly feel its impact: you can directly interact with it and see the response. AI used to be integrated into the background of a lot of processes we didn’t necessarily see.” 

Generative AI is at the forefront of this craze. But, what can organizations do to take advantage of AI now? Where are we headed in the next 12 months? In 5 years? Tools like GPT-5 will unleash a whole new suite of solutions aimed at widespread end-user adoption, but that’s not all. 

How to approach enterprise-level AI adoption — and what not to do

If you’re part of a modern-day enterprise with dreams of unrivaled AI-powered innovation and competitive advantage, you may be thinking: so what do we do now? That’s the multi-trillion dollar question. 

Do: Understand AI’s capabilities and limitations

The key is, in part, to recognize the things these AI systems are good at, and how you can leverage those (i.e. synthesis, translation, identifying language patterns). When this kind of intelligence is taking care of your baseline tasks or processes, it almost takes them out of the equation. This means the only question you have to worry about is: what can I focus my time on more intentionally to have a greater impact? AI will allow you to offload and shift.

Don’t: Just keep up appearances

Many businesses want to check the ‘do AI’ box for appearance’s sake. But what does that really mean? It means that when you combine the maturity of those AI platforms and features, a lot of the work that once needed somebody technical or someone versed in the platform to be done, can now be picked up by AI. For example, with Lytics AI-powered SQL Translator, you can segment audiences without needing to know how to write SQL. 

Do: Think about tomorrow, not just today

With an understanding of the advantages of AI for your organization, you may be wondering: how can I tool my business to be both ready to leverage AI and resilient enough to adapt to continued change? Lanenga shares: 

“The realistic answer is that, in order to use these tools, we need to get better at prompt engineering. The better the prompt, the better the answer you get back. This intentionality in putting what we want into terms AI can understand is critical.  You need to know what you need to understand how to fill that need.”

Don’t: Just cherry-pick AI features

We could — in theory — all go out, grab a new AI feature, and implement it into our business’ way of working. But more mature companies are not just thinking about features; they’re embedding AI into their platforms consistently and intentionally. 

At Lytics, we believe that there are, fundamentally, two core sets of data we have the opportunity to look at out of the gate when we’re thinking about how AI can add value into an organization. The first is in your data pipeline, focused on customer data. The second is your  unstructured data or GTM data. When you’re integrating a modern data stack around those two areas, you can add a lot of value. And the reality is that you need a modern data stack and best-of-breed platform, so you can rest assured that AI isn’t an afterthought — it’s something that’s already and constantly being considered and thoughtfully embedded.

The future of AI for businesses, for CDPs, and beyond

The focus for AI hopefuls everywhere, particularly for enterprises, should be on meeting use cases — identifying a need, understanding how AI can fill that gap, and executing. Blackman explained: 

 “Of those organizations coming to Product Pair looking for guidance on AI adoption, 80% of the asks are for recommendation engines powered by AI (Read: “I need better scheduling for my customers.” “Or I want better suggestions to populate in my feed.”) That always leads down the path of customer journey and experiences. So, I see AI and customer experience as pretty interlocked, and as a result, everything from Martech to in-app experiences will be hyper personalized.” 

AI has evolved from enabling predictions to enabling human-like interactions, making it distinctly possible that in 12-months’ time, we’ll see the explosion of Artificial General Intelligence (AGI): technology that could potentially be sentient and able to ideate. No matter what’s on the horizon, the technological moment we’re in is a learning lesson: one on the importance of, as a business, both developing a modern data stack and using AI to embrace near-term and long-term opportunities.

But that doesn’t mean embracing AI is the whole story, adds Blackman. 

“Tooling the business to make sure you have good data is just as important as setting up the AI itself. If you have bad data, when you unleash AI somewhere, it becomes a black box. You can’t make tweaks on it because you can’t anticipate how it’s learning. Setting up the infrastructure properly can mean better outputs for customer experience.

To dive even deeper into the potential of AI and explore more tips and considerations, watch our on-demand webinar in full.