A recent McKenzie blog stated "Omnichannel is table stakes" and "our research suggests that a fifth of current sales-team functions could be automated."

We started asking clients and partners how they're R&D'ing Generative AI with their eCommerce efforts and here's the top 3 we've heard so far:


1. Customer-facing sales and support enablement (e.g., chatbots).

After my team testing 5 different decision-tree-based bots over the past couple of years, I can tell you I'm really excited about the potential here, but in our own efforts, training a Generative AI platform on a Large Language Model (LLM) so that it understands your custom company data is aptly titled - it's a large undertaking as the tech exists today.

In fact, a recent report on data science shows that data professionals spend 38% of their time preparing and cleaning data - housekeeping.

Accenture posted their own white paper on Generative AI as well, stating "Nearly 6 in 10 organizations plan to use ChatGPT for learning purposes and over half are planning pilot cases in 2023. Over 4 in 10 want to make a large investment." Right now, "a large investment" is likely needed to get custom data cleanly fed into a usable format.


2. Discovery / Findability (e.g., the new search engine)

This one again is near and dear to me, as we have the first deal in our pipeline that said they found out about Broadleaf from ChatGPT - who doesn't like a new lead channel?!

Well, first of all, neither ChatGPT or Bard (by far the two most popular Q&A AI's right now) give the same answer to everyone who asks the same question, BUT they're still, at their core, conversational technologies, so while metadata may be super-important to SEO listings, conversational language on page is likely much more important to be found on a Generative AI platform - this is a big area of R&D and I'm sure will spawn cottage industries for marketing agencies and tech optimization platforms.

In the meantime, our friends over at LucidWorks have a great blog published this month on Why Conversational Search is the Next Leap in eCommerce. It's worth a read if you're interested in this topic.


3. Hyper-Personalization (e.g., dynamic merchandising)

Tried and true Omnichannel technologies integrate product recommendations, searchandising, marketing automation, multi-variate testing, and customer profile data to determine merchandized product placement, personal pricing, and offers/promos. And advanced Unified Commerce companies already have rule-sets which dynamically build personalized promos, pricing and placement. If you want to see this in action, just checkout Amazon across different channels and accounts looking at the same products.

Our friends at Grid Dynamics have written a blog on Generative AI assisting Content Production, which is being done today by an ever growing army of Omnichannel merchandisers and marketers. And personalized visualization (read the article) is especially interesting where half of consumers say they'll become repeat buyers after a personalized shopping experience according to Twilio's latest State of Personalization Report. But what about hyper-personalization?

Traditionally, hyper-personalization uses AI and Machine Learning (ML) to go beyond segmentation and deliver truly personal experiences. Throwing Generative AI into the mix of AI + ML further allows for truly dynamic experiences altogether - from the page layout to the product description - so that an experience is not only truly personalized, but never replicated.


Like any technology, there's plenty you can do through automation, but the secret sauce lies in how you leverage the tool different from anyone else.

Sometimes it's not what's out of box that counts, but what's outside the box!