AI Explored Podcast — Episode Summary

Podcast: AI Explored
Host: Michael Stelzner (Social Media Examiner)
Guest: Taylor Rady, Director of Research at Smarter X, Instructor at AI Academy, Author of Growth Curve newsletter
Episode: Personalizing AI for a Business: Turning Generic Tools into Customized Solutions
Date: January 13, 2026
Show Notes: Socialmediaexaminer.com/aipod

Overview: Main Theme & Purpose

This episode dives into the nuts and bolts of personalizing AI for business—transforming generic AI tools into custom solutions tuned to your team, processes, and goals. Taylor Rady draws from her deep experience in digital transformation, change management, and AI consulting to explain why out-of-the-box AI usage leads to generic results and how you can truly differentiate and gain value by integrating your organization’s unique expertise and insights into your AI systems.

Key Discussion Points & Insights

1. The Misconception of Out-of-the-Box AI (05:18)

• Efficiency Over Originality: Many businesses treat AI as just an efficiency shortcut—focusing on doing more with less, but this often leads to generic, undifferentiated outputs.
• AI’s Nature: Large Language Models (LLMs) like ChatGPT, Gemini, and Claude are trained on vast, generic data and gravitate toward “safe” and average outputs.
  • Notable Quote:
    “This race to do less is going to lead a lot of companies straight into the trap of looking and sounding like everybody else.” — Taylor Rady [07:02]
• Competitive Risk: If everyone uses AI in the same way, brands end up indistinguishable. Standing out requires intentional personalization and leveraging unique internal knowledge.

2. Why Personalize AI? (08:21)

• Better Outputs, Not Just More Outputs: Personalization delivers higher-quality, more authentic, and original work efficiently.
• Trust & Accuracy: Incorporating your own business data increases the reliability and trustworthiness of AI outputs.
• Career Value: Mastering these skills makes you indispensable—AI won’t replace people who know how to elevate its use.
  • Notable Quote:
    “Those that really know how to output exceptionally high quality are going to be the ones that are going to be remaining behind.” — Michael Stelzner [10:14]

3. What is Personalizing AI? (11:19)

• Definition: It’s not about custom-targeted messaging to customers—it's about customizing how AI works for your internal operations, strategy, and content generation.

The Personalization Process: How-To Breakdown

Step 1: Capture & Collect Knowledge (11:45)

The D.A.T.A. Framework [11:45–15:10]

• D — Domain Expertise:
  Capture unique, hard-won insights from years in business.
  Prompting questions: What do experts know that novices don’t? What do outsiders get wrong?
• A — Approaches:
  Document frameworks, methodologies, SOPs, checklists, and instinctive processes.
• T — Talent (and Testimonials):
  Identify unique strengths, talents, and pull from testimonials to understand your “secret sauce.”
• A — Accomplishments:
  Chronicle stories, case studies, big wins, and failures. AI doesn’t have lived experience—this is your IP.

Quantity & Type of Data [15:10–16:47]

• Huge “context windows” of modern AI models mean you can load in massive amounts of unstructured data (whole libraries, not just snippets).

Whose Domain Expertise? [17:13]

• Could be personalized per person (e.g., CMO, sales director) or company-wide—building a “corporate brain” accessible to all.

Extracting Tacit (In-the-Head) Knowledge [18:02]

• Voice capture is fastest and most authentic. Use meeting transcriptions, interviews, and prompt-based discussions to draw out insights.
  • Tools Mentioned:
    • Otter (custom vocabulary for industry terms)
    • Fireflies (auto-joins calls, chatbot for insights, keyword tracking, sentiment analysis)
    • Descript (record ideas, transcribe, edit via text)

Step 2: Understand & Organize Knowledge (22:35–24:54)

• Knowledge management platforms (Guru, Glean, Coveo): Aggregate data from emails, internal wikis, support tickets, and Google Drive.
• Enterprise Search: Query across all platforms, AI provides answers with citations and direct links to source material.
  • Tip: Google Gemini and related tools allow you to dynamically search tagged content across Drive, Asana, email, etc.
  • Notable Quote:
    “This brings it all together and makes it more accessible.” — Taylor Rady [23:41]

Step 3: Build & Maintain Custom AI Assistants/Agents (24:54–29:44)

• Custom GPTs & “Gems”:
  Tailor AI “agents” for specific business functions—HR, support, marketing, etc.
• Guru’s Knowledge Agents:
  Assign specific knowledge/roles; all Q&A and verification managed via an agent center. Built-in “verifiers” (internal experts) review, correct, and update responses (“self-improving system”).
  • Notable Quote:
    “Your system is basically getting smarter the more your team uses it.” — Taylor Rady [26:10]
• Dynamic Data Management:
  Use tools that connect to live docs/sheets for up-to-date info—avoid static PDFs and outdated uploads.
• Process/Access:
  Set verification schedules, assign data stewards, and consider privacy access control.

Step 4: Action & Creation (Do the Work!) (38:02–43:09)

• Apply assistants to daily workflows:
  Use custom GPTs or gems for content, strategy, support, social media, training, etc.
• Tool Spotlight:
  • Grammarly: Inline tone/style validation; knowledge share for hovering over acronyms, products, etc.
  • NotebookLM: Google-powered, file-grounded AI hub for teams. Source-cited answers, easy for onboarding/training; more resource hub vs. role-based assistant [30:25–35:20].
  • Automation:
    • Writer (multi-step AI workflows)
    • Google Workspace Studio (deep integration: triggers, automation across Docs, Sheets, Email, Chat, Asana, Salesforce, etc.)
      • Notable Quote:
        “We’re creating a workflow that is basically going to replace our [previous] automations… All this kind of stuff is now possible with Google Workspace Studio.” — Michael Stelzner [41:19]
• External Bots:
  How internal data can power external customer-facing chatbots and virtual experts (e.g., on community sites).

Tool Highlights & Notable Recommendations

• Otter: Meeting transcripts with custom vocab
• Fireflies: Meeting insights, Q&A, analytics
• Descript: Fast voice-to-text/ideas for content creation
• Guru: Knowledge management, verification
• Glean & Coveo: Enterprise search and compliance (HIPAA, etc)
• NotebookLM: For research, onboarding, internal help desk, always-cited answers
• Grammarly: Real-time style/tone, in-context knowledge sharing
• Google Workspace Studio: AI-powered workflow automation
• Google Gemini: Embedded AI search across Google ecosystem

Notable Quotes & Memorable Moments

• On AI Homogenization:
  “This race to do less is going to lead a lot of companies straight into the trap of looking and sounding like everybody else.” — Taylor Rady [07:02]
• On Standing Out:
  “Good enough has become so accessible and so attainable that marketing professionals…have to be better in order to stand out.” — Taylor Rady [06:53]
• On the Value of Personalization for Careers:
  “Those that really know how to output exceptionally high quality are going to be the ones that are going to be remaining behind.” — Michael Stelzner [10:14]
• On Internal ‘Work Slop’:
  “If I wanted to ask ChatGPT, I would have.” — Taylor Rady [10:49]
• On Using Voice to Capture Knowledge:
  “We speak three to four times faster than we write…you are also going to capture language that is going to be a lot more natural and authentic to you.” — Taylor Rady [18:02]
• On Trust and Source Verification:
  “With NotebookLM…not only will it provide the source, but you can actually hover over it and see the exact excerpt that it pulled it from and again confirm that the information's correct.” — Taylor Rady [32:28]

Important Timestamps

• Misconceptions of AI Use — [05:18]
• Upside & Definition of AI Personalization — [08:21]; [11:19]
• D.A.T.A. Framework — [11:45]
• How Much Data to Collect? — [15:10]
• Extracting Tacit Knowledge — [18:02]
• Knowledge Management Tools & Tips — [22:35]
• Building Custom AI Agents (Guru, etc.) — [24:54]
• The Need for Dynamic Updates — [27:52]
• Managing Data & Future of AI Memory — [29:41]
• NotebookLM vs. Custom GPTs/Gems — [30:25]; [33:16]
• Internal vs. External Bots — [36:24]
• Actioning With Custom AI Assistants & Automation — [38:02]
• Google Workspace Studio Deep Dive — [39:37]
• Security & Privacy in Google Workspace — [43:09]

Flow & Tone

Taylor and Michael blend strategic insights with actionable examples, offering detailed tools, frameworks, and pragmatic takeaways. The conversation moves from high-level concepts—why personalization matters and the perils of AI homogeneity—to granular advice for every step of the personalization journey.

Connect with Taylor Rady

• LinkedIn: Taylor Rady
• Newsletter & Resources: taylorrady.com/SME
• AI Academy & Mastery: SmartRx AI at Marketing AI Institute

Bottom Line:
Personalizing AI isn’t about “hacking prompts”; it’s about embedding your unique expertise, process frameworks, and proprietary knowledge into AI systems, then maintaining and operationalizing that intelligence for standout results. The tools are here—now it’s about harnessing them intentionally.