Podcast Summary: Experts of Experience

Episode Title: Why Data-Ready Companies Are Winning at AI
Date: January 28, 2026
Host: Lacey Peace (Mission.org)
Featured Guest: Mike (Leader at Teradata, 35+ years in customer experience)
Theme: How companies prepared their data are surging ahead with impactful AI, the key challenges and solutions in leveraging data for AI-driven customer experience, and the skills needed as we enter a more agentic, AI-powered business era.

Episode Overview

This episode examines the intersection of data readiness and AI success in customer experience. Host Lacey Peace and guest Mike (from Teradata) dissect why organizations with a well-established, clean, and accessible data foundation are reaping the biggest rewards from AI. They discuss real-world examples, illuminate pitfalls in AI adoption, and share guidance for businesses at various data maturity levels, culminating in predictions for the year ahead.

Key Discussion Points & Insights

1. The Critical Role of Data in AI Success

• Foundational Gap: Many companies overestimate AI's promise, underestimating the data groundwork needed.
• Quote: “While you may be able to use AI for some really cool things, if you don’t have that data, you’re going to end up with AI that’s not going to be really valuable to you.” – Mike (00:13)
• Two Main Challenges:
  • Data Problem: Having the right data, clean and structured, is essential.
  • Approach Problem: Best use cases begin with a specific business problem, then work backward to fit AI—not the other way around.

2. Evolution of Customer Data & the New Frontier

• From Structured to Unstructured: Companies are adept at handling structured data (CRMs, transaction logs), but struggle with vast, unstructured sources (text, voice, video, chats).
• Quote: “What’s not being captured today is all of that other information... That’s the new frontier... How do you mine all of that unstructured data to get true benefit?” – Mike (02:50)

3. AI’s Impact: Making Sense of Unstructured Data

• Techniques:
  • Translation (voice/video into text)
  • Vector stores to search and extract context from unstructured data
• Crucial Step: Combining (marrying) unstructured data with structured data for real insights.
• Use Case: In banking, matching chat data with account values highlights issues affecting top clients—narratively improving NPS.

4. Data Readiness – The Leading Advantage

• Companies with longstanding investments in data curation are well ahead in extracting AI value.
• Early investments in data hygiene and structure underpin rapid AI deployment and success.
• Quote: "Our customers…have spent the time to analyze that data, have it structured, have it clean, have it accessible. And…[are] a huge leap ahead when it comes to AI." – Mike (08:21)

5. Defining Meaningful AI Use Cases

• Avoid 'AI for the sake of AI' or hackathon-inspired ideas that lack business impact.
• Focused, outcome-driven use cases such as improving NPS, understanding sentiment, or streamlining workflow yield the strongest results.
• Quote: "The best use cases are the ones that are starting to solve a business problem...then back into a use case" – Mike (09:33)

6. Real-World Success Stories

Timestamps:

• Bank Example: [12:35] – Large APJ bank improved NPS significantly in 3 months by analyzing and acting on previously unused chat data.
• Airline Example: [13:55] – European airline isolated baggage handling as the main driver of negative sentiment using AI sentiment analysis.
• Internal at Teradata: [15:12] – Weekly, AI-powered account plans for all clients, combining internal CRM, telemetry, and public data.

7. Small and Medium Business Applications

• Teradata scales for organizations of all sizes; SMEs can leverage robust, flexible tools without massive upfront investments.
• On-prem and cloud flexibility to meet regulatory/security needs.
• Quote: “We can scale up to the world's largest companies, but we also have a number of smaller organizations or departments...scaling is something we've done for years.” – Mike (18:03)

8. Tackling AI Hallucinations & Data Governance

• Guardrails are essential—direct AI explicitly where to fetch data and what sources to trust (e.g., ARR definitions).
• Less is more: Overloading AI with irrelevant/unfiltered documents leads to errors.
• Quote: “The more you tell AI where to go get its answers…that's where the key comes from.” – Mike (21:37)
• Ongoing monitoring and feedback cycles are key for improvement.
  (21:37 – 23:24)

9. Guidance for Companies with Data Gaps

• Even new/young companies have data—often external (social, reviews).
• AI can help sort, clean, and organize existing data faster than brute-force legacy methods.
• Quote: "There's a new tool set that we didn't have 10 years ago...Now you can use AI to help you." – Mike (23:59)

Human Impact & User Experience

10. Measurable Customer Outcomes & Real Experiences

• NPS and customer sentiment have improved quickly where data-driven AI is applied.
• Chatbots are getting better—sometimes indistinguishable from humans for several minutes.
• The risk: Even a single bad chatbot experience can sour consumer perception for all AI interactions.
• Quote: “It’s certainly a dilemma for companies...you can really hurt your brand pretty quick.” – Mike (30:37)

11. Rethinking Workforce Impact: Augmentation vs. Replacement

• Current focus is on using AI to make employees (agents, sales, support) more effective—not replacing them.
• AI as a force-multiplier rather than a job killer.
• Example: Seamless transitions between support reps due to good data integrations.

Predictive & Agentic AI Applications

12. Predictive Modeling at Scale

• AI enables highly sophisticated scenario planning across far more variables than traditional models (e.g., for airlines: load, weather, regional conflict, economics).
• “They can have the eventuality of weather patterns, of a global conflict…[and have] an exponentially bigger set of models…” – Mike (31:59)

13. The Next Trend: The Rise of Autonomous AI Agents

• 2026 = "Year of Agent Building"
• Companies will deploy numerous agents to autonomously handle processes—account planning, support, negotiations.
• Defining AI Agents: Not just automations, but background systems acting independently on new data triggers.
  • Example: Autonomous account planning agent updates reps when public/internal events signal change (50:59)
• Future: Agents “hiring” one another and even handling contracts/negotiations.
• Quote: "...it's a year of agent building and getting outcomes and...moving more and more to autonomous agents that you have the confidence in and the trust in..." – Mike (49:29)

Skills for the Future

14. What Skills Matter in the AI-Driven Workplace?

• Data literacy and data science foundation remain crucial.
• Curiosity, willingness to experiment, and continuous learning are the most valuable long-term skills.
• Understanding both structured and unstructured data is essential.
• Quote: "The most important thing...is curiosity and being able to win in this world of AI..." – Mike (38:13)
• For leaders: Continued emphasis on authentic human relationships—AI won’t replace trust and rapport.

15. Does Domain Expertise Still Matter?

• Yes—expertise is needed to prompt, interpret, and verify AI outputs.
• Analogy: You still want a trained pilot in a plane, even if it’s automated.
• Quote: "...someone’s got to look that output over and say, hey, is this getting to our intended outcomes?" – Mike (46:46)

Notable Quotes & Memorable Moments

• On Data Readiness:
  "Our customers...have a huge leap ahead when it comes to AI because they have spent the time to analyze that data, have it structured, have it clean, have it accessible." – Mike (08:21)

• On AI Overhype vs. Reality:
  "Hackathons have a purpose...But to use a hackathon to say, okay, we're going to come up with our use cases for AI? Not a great approach." – Mike (11:47)

• On Skills for the Next Decade:
  “What I’m encouraging [interns] is go try it … Learn. That thirst for learning is probably the most important skill…” – Mike (38:13)

• On Expertise in the Age of AI:
  “If an engineer can’t understand the logic of that code and how it’s going to work, how are we ever going to really understand if we’re getting to the right outcomes and the right results?” – Mike (46:46)

• On Customer Relationships:
  “You can’t underestimate...those human relationships...Use AI to its best of its ability, but don’t replace those relationships...” – Mike (43:44)

Timestamps for Important Segments

• 00:13 – Data readiness as the core of AI success
• 02:50 – The challenge of unstructured data
• 08:21 – Data maturity as a competitive advantage
• 12:35 – AI in banking: NPS turnaround
• 13:55 – Airline case: Baggage sentiment analysis
• 15:12 – Teradata internal: Agentic account planning
• 21:37 – Battling AI hallucinations with data governance
• 23:59 – Advice to companies lagging on data
• 31:59 – Predictive modeling for customer needs
• 38:13 – Skills for the AI future
• 46:46 – The enduring value of domain expertise
• 49:29 – 2026 trend: Autonomous AI agents
• 50:59 – Real-world agent example: Account planning agent

Conclusion

Companies winning with AI are those that invested early in organizing, cleaning, and structuring their data and those who continually tie AI use cases to real business problems. As agentic AI rises, success will increasingly rely on hybrid teams: deeply data-literate, curious humans supported—not replaced—by increasingly sophisticated autonomous agents. Data readiness is the new competitive advantage, and the future belongs to those who continuously learn and adapt, all while nurturing the irreplaceable human relationships at the heart of customer experience.