Gen AI in services beyond chatbots - 3 case examples
Hi all,
I hope everyone had a great summer! It’s back to the newsletter after the holidays and some busy weeks of client work.
First, two announcements
My Gen AI for Designers live training workshop on October 9 and 10 is now open for registration. Seats are limited.
I’ll be speaking at the GrandOne AI Day about the future of customer experiences in the age of AI. Other speakers include AKQA Sweden's CEO and Adventure Club's founder.
Gen AI in services beyond chatbots - 3 case examples
How should we use Gen AI to help our customers beyond chatbots?
This a recurring question I’ve heard from clients after the summer holidays.
It’s clear that after the initial excitement over Gen AI, companies are now looking to pilot products that will most benefit their customers.
This week, I’ll share three ways companies are helping their customers with Gen AI. For each theme, we’ll look at recent case examples and questions to get started with.
1. Enable faster creation for customers
For recruiters, one of the most time-consuming parts of their role is writing job postings.
Upwork, the freelance marketplace, recently integrated a tool for recruiters to generate engaging job postings with a few keywords.
The results? The time to create job postings fell by 80%.
How could you apply this to your company? Here are some questions to start with:
What are your customer’s most tedious workflows? How could Gen AI dramatically reduce the heavy lifting involved?
What unfair data advantage do you have over your competitors in training the gen AI?
2. Hyper personalized recommendations
Generative AI can take personalization to a whole new level.
Indeed, a job matching platform used GPT to craft highly personalized recommendations for job matches for applicants.
Instead of the vanilla recommendations we see on LinkedIn, such as “You could be a great match at Accenture,” Indeed was able to pinpoint specific reasons why a certain job was a promising match for an applicant (see example below).
Indeed uses Gen AI to hyper personalize job posting recommendations.
How could this new level of personalization apply to your field? What if Gen AI provided highly targeted specific messages to your customers based on their context?
Here are a few thought starters:
Smarter subscription recommendations - ”We recommend switching to a market rate electricity contract because your consumption often peaks during the cheapest hours”.
Context-aware product advice - ”Based on the picture you uploaded, we recommend this grey L shape sofa for your living room”
Hyper personalized property recommendations - ”This property listing could work well for your family as public transportation and schools are your most important criteria.”
3. Customized sales outreach
In B2B sales, researching and enriching information about leads is a vital part of the workflow.
Prospects are much more likely to respond to highly customized and relevant outreach. This has traditionally been the role of junior salespeople.
Now, platforms like Clay are using agentive AI systems to solve the problem. Building on GPT-4, Clay’s agent enriches a list of leads with relevant information like founder names, headcount, social media post, and so on.
Based on this information, users can use AI to generate a personalized outreach message to each prospect.
The AI agent uses 75 different tools, such as Stripe and Gmail, to enrich the lead information.
The bottom line?
Agentive AI tools like Clay enable the automation of personalized outreach at scale. It can help companies go from cumbersome manual research or standard messages to automated, highly tailored sales messages.
Clay uses agentive AI to enrich client lead lists for personalized communication.
Expanding our thinking
What should be our takeaway from all of these examples?
The critical point is that even the current tools like GPT-4o, Gemini, and Midjourney present vast and largely untapped opportunities. They also have ever-increasing capabilities from multimodality to agency.
It’s down to our imagination to look at our internal and customer paint-points and think broadly about how gen AI could make life easier.
Anytime anyone solves a tricky issue with information, the odds are that Gen AI could help somehow.
Until next time.
