Table of Contents
Not long ago, we talked about the MIT and Gartner reports, both on failed AI pilots due to AI agent washing, wrong use cases, and the learning gap—where organizations fail to deploy AI tools on an enterprise level that can learn and integrate into workflows.
This time, we will focus on the themes identified within the MIT report, and outline how enterprises—those in the 5% that succeed with AI pilots—are bridging this GenAI divide. They’re achieving success through AI with memory that can handle mission-critical work with memory and strong partnerships while leveraging internal expertise. Human-AI collaboration is central, enabling AI to learn, adapt, and deliver consistent, high-quality outcomes.
You’ll see insights drawn from the MIT study, paired with our own experience successfully deploying enterprise AI agents for customer experience. The report also alludes to the agentic web of interconnected AI agents, a topic we’ll explore in a future edition.
You might ask, “Wouldn't it be easier to just wait until the dust settles?” According to this recent report by McKinsey, the gap between AI leaders and the rest has been growing from 2.7x in 2022 to 3.8x today, signaling that waiting isn’t a feasible strategy…unless you want to be left behind.
Here’s a closer look at what’s working—and how.
Theresa Liao
Editor and Director of Content & Design, ASAPP
Why memory will define the future of AI agents in CX
“GenAI lacks memory and adaptability.” Without memory, AI can’t provide consistent or personalized experiences. The next leap forward in customer experience will come from generative AI agents that can remember, learn, and adapt across interactions, delivering service that feels hyper-personal and continuous. Read how memory will define next gen AI agents.
The GenAI divide—and how to bridge it
“90% of enterprises have explored buying an AI solution,” but most pilots fail to deliver real business impact. Success comes to the few who approach AI strategically—choosing the right partners, defining clear use cases, and continuously monitoring and optimizing outcomes. See how enterprises bridge the divide to achieve results.
The enterprise AI playbook: Why partnerships beat internal builds
“Internal builds fail twice as often.” Delivering measurable impact requires deep expertise, and enterprises that partner with experienced AI vendors are far more likely to select the right use cases, optimize performance, and sustain ROI over time. See why partnerships are the smarter path to success.
Equip your contact center for human/AI collaboration
AI alone can’t deliver consistent, high-quality customer experiences—human expertise is still the key. ASAPP’s Human-in-the-Loop Agent (HILA™) workflow embeds humans behind the scenes to unblock AI in real time, enabling faster resolution, personalized service, and AI that learns and improves with each interaction. Learn how human-AI collaboration delivers in contact centers.
What would GenerativeAgent® say if it could talk?
We had a fun time writing this one. From stopping fraud in minutes to applying human guidance while resolving complex issues, experience a day in GenerativeAgent’s perspective.
"We need to realize what the whole concept of customer care and support is all about. It’s not a cost center. It should be a profit center. It should be a retention center."
CX Expert | Keynote Speaker | NYT Bestselling Author
From Voice isn’t dead—it just needed better AI
From Voice isn’t dead—it just needed better AI
What we are reading
- [McKinsey] Next best experience: How AI can power every customer interaction
- [CIO] The CIO’s guide to AI-driven customer experience in financial services
- [Harvard Business Review] Why Agentic AI Projects Fail—and How to Set Yours Up for Success
- [No Jitter] Goodbye IVR Hell: Smart AI Will Take Over Phone Calls
