The Stash | August Edition
Welcome to August’s edition of The Stash , our monthly newsletter where we share tools and tactics for teams putting Generative AI to work.
We hosted a live webinar on the power of running prompts on at-scale customer data - reviews, support calls, and more.
In this segment, we show how a fine-tuned prompt can extract key issues from customer reviews, and how data engineering and continuous prompting can turn those insights into actionable improvement strategies. You can watch the full webinar on-demand here .
We packaged 6 of the most valuable, repeatable, field-tested prompts we've developed for leading CX teams. Download your guide to learn how to analyze customer sentiment across different touch points, generate knowledge articles from successful issue resolutions, craft customer outreach in your specific brand voice, and much more!
Dialogflow CX’s generators are a big step forward in building responsive conversations. With a workspace to design and test your prompts, you can use generators to solve problems that were previously impossible. Learn how it works in HumanFirst .
We’re excited to share that we’ve partnered with Infobip , a global cloud communications platform, to bring HumanFirst directly to their AI-powered communications suite.
With a one-click integration to Conversations, Infobip’s contact center solution, Infobip customers will be able to apply LLMs to critical customer communications data to open up new, non-technical use case; conversation analytics, topic modeling, root cause analyses, agent QA, and more. Find HumanFirst on the Infobip Exchange !
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Did you know you can purchase HumanFirst on 谷歌 Cloud Marketplace? (And every dollar spent on Marketplace is a drawback on your GCP commit?)
Google Cloud Marketplace has become a one-stop shop for AI-ready teams. We’re proud to be a #GoogleCloudPartner, working with great customers. Check out this video to learn how easy it is to get started.
We look forward to seeing partners, customers, and peers at ALL IN in Montréal. Let us know if we'll see you there!
McKinsey’s August report shows 91% of surveyed employees are enthusiastic gen AI adopters, but only 13% of organizations surveyed have implemented multiple gen AI use cases.?
Obvious barriers cause organizations to move slower than their in-house enthusiasts. Security and compliance, budget considerations, and a dizzying rate of change confront enterprise technical leaders, many of whom are navigating an influx of tooling and training requests from multiple departments.
But the discrepancy between employee enthusiasm and organizational adoption risks delaying gen AI’s full business value.
In the McKinsey report, 沃尔玛 is an exemplary early adopter. A Q2 earnings call records Doug McMillon , Walmart’s CEO, sharing that they’ve used “multiple large language models to accurately create or improve over 850 million pieces of data” in their catalog, which would have previously required 100x their current headcount to complete in the same amount of time.?
To achieve that level of impact across multiple departments, employees need more than what McKinsey calls ‘embedded’ AI tools - chat interfaces, copilots, and point solutions. Expanded value capture requires a data-connected AI environment, where domain experts can experiment with prompt and data engineering for real business projects.
McMillon also signals the importance of leveraging multiple models. As employees turn toward gen AI for multiple use cases, multi-model access will become increasingly important (and cost effective). In an ideal AI environment, models will be interchangeable; employees will be able to use the workflows they build with the model that’s right for the task.
In-platform collaboration will also be critical to actualizing this new way to work. Walmart’s feat is the achievement of many contributors, likely from different domains, working together with data and gen AI. Collaborative interfaces will profoundly accelerate the learning curve, and the ability for teams to develop prompts and datasets in shared workspaces will be imperative.
Gen AI as a better search, a faster first draft, or a conversational sidekick isn’t a strong enough promise to change the way teams work. Continued gen AI implementation comes from an expanded definition of its potential; a belief that gen AI can help to automate routine tasks, augment complex knowledge work, and extend domain experts' impact beyond the work they can manage themselves.
McKinsey’s recommended course of action involves reinventing operating models across domains, reimagining talent and skilling strategies, and reinforcing new ways of working. With better tools and bigger expectations, those strategies will help achieve the stated goal: turning employee experimentation into organizational transformation.