Foundations in AI

This content was originally featured in the March 12th, 2025 newsletter found here:INBOX INSIGHTS, March 12, 2025: Foundations in AI, AI Model Benchmarking

The Foundation Still Matters

I was sitting in a lecture hall at Worcester Polytechnic Institute (WPI) last week, speaking to a room of bright students through the Women in Data Science (WIDS) program. The energy in the room shifted noticeably when we got to the Q&A section, and a hand tentatively went up.

“What should we even be studying if AI is going to do all the work anyway?”

I could see the worry in her eyes, and looking around the room, it was clear many others shared the same concern. Having spent years building my career on technical skills that AI can now replicate in seconds, I understand that fear. Seriously, I do.

But here’s the thing – AI isn’t replacing you. It’s replacing the way you work.

The Human-Machine Partnership

Let’s talk about software development as an example. Yes, generative AI can spit out code faster than most humans can type. I’ve been amazed watching it generate in seconds what might have taken me hours.

But who’s telling the AI what to build? Who’s checking if that code actually solves the right problem? Who’s testing edge cases the AI might have missed?

That’s still you.

What’s happening isn’t job elimination – it’s job transformation. Instead of pairing with another developer, you’re now paired with a machine. The foundational knowledge you need hasn’t disappeared; it’s become even more critical.

Your Foundation Is More Important Than Ever

At Trust Insights, we talk constantly about two frameworks that remain essential regardless of how advanced AI becomes. Let’s dive deeper into each one:

The 5P Framework for Process

1. Purpose: Why are we doing this work? What business problem does it solve? This is where you identify the core objective and how it ties to business outcomes. For example, are you trying to improve customer retention, increase conversion rates, or streamline operations?
2. People: Who needs to be involved and what skills do they bring? This isn’t just about technical skills – it’s about domain expertise, stakeholder buy-in, and cross-functional collaboration. A successful AI implementation requires subject matter experts who understand the nuances of the business problem.
3. Process: What steps will we follow to complete the work? This includes defining workflows, governance, feedback loops, and how decisions get made. Even with AI doing much of the execution, you need a clear process for how humans review, validate, and incorporate AI outputs.
4. Platform: What tools will we use? This covers both the AI technologies and the supporting infrastructure. Are you using generative AI, predictive models, or computer vision? Do you have the right data storage, processing power, and integration capabilities?
5. Performance: How will we measure success? These are your KPIs and metrics that determine whether the work is delivering value. What quantifiable outcomes will tell you if the AI implementation is successful?

AI might help with the Platform component, but it can’t determine Purpose. It can’t understand the nuanced needs of People. It can follow a Process but can’t design one from scratch that accounts for organizational quirks. And it certainly can’t set meaningful Performance metrics tied to business value.

The 6C Framework for Data Quality

Just last month, I was working with a client who wanted to use AI to predict customer churn. They had invested in sophisticated AI tools but weren’t seeing the expected results. When we dug into it, the issue wasn’t the AI – it was the data. Their data failed several of our 6C quality checks:

1. Clean: Free from errors and inconsistencies
   • Are there duplicate records?
   • Have outliers been addressed?
   • Is the data formatted consistently?

   We found their customer behavior data had significant gaps due to system migrations.

2. Complete: No missing information
   • Are all required fields populated?
   • Do you have sufficient historical data?
   • Are there any sampling biases?

   Several key metrics had incomplete histories, making trend analysis unreliable.

3. Comprehensive: Covers all aspects needed to answer the question
   • Does the data include all variables that influence the outcome?
   • Are you capturing both digital and offline interactions?
   • Do you have visibility across the entire customer journey?

   They were missing important touchpoints from their mobile app, which skewed the analysis.

4. Chosen: Only relevant data, no noise
   • Have you filtered out irrelevant variables?
   • Is the data specifically selected to address your question?
   • Have you removed redundant or highly correlated variables?

   Their dataset included hundreds of variables, many of which weren’t relevant to churn prediction.

5. Credible: Collected in a valid, trustworthy way
   • Is the data collection methodology sound?
   • Do you have confidence in the data sources?
   • Has the data been validated?

   Some of their customer feedback data came from a biased sample that wasn’t representative.

6. Calculable: Usable by business users who need it
   • Can the data be easily accessed and analyzed?
   • Is it in a format that works with your tools?
   • Can non-technical stakeholders understand the outputs?

   The data was spread across multiple systems with inconsistent formatting, making it difficult to use.

After addressing these issues, their AI models started producing significantly more accurate predictions. The AI wasn’t the problem – the data was.

Applying These Frameworks in the AI Era

So how do these frameworks help you stay relevant in the age of AI?

One of the most successful approaches is applying these frameworks as a diagnostic tool. When an AI implementation isn’t delivering expected results, it’s rarely because the AI technology itself is flawed. Almost always, the issues lie in the foundational elements – unclear business objectives, misaligned teams, broken processes, or poor-quality data.

By systematically evaluating each of the 5Ps and 6Cs, you can pinpoint exactly where the gaps are and address them directly. This is where human expertise becomes invaluable – understanding the interconnections between business goals, human factors, processes, technology, and data quality.

Bridging the Gap Between Humans and AI

This is where the opportunity lies for all of us – not in competing with AI, but in managing the interface between AI and business value. The most successful professionals I see today are those who can:

1. Translate business needs into AI requirements: Understanding what problems need solving and how AI can help
2. Ensure data quality: Applying the 6C framework to give AI the inputs it needs
3. Design effective processes: Using the 5P framework to integrate AI into workflows
4. Interpret and contextualize AI outputs: Adding the human judgment and domain expertise
5. Bridge technical and business language: Helping non-technical stakeholders understand and trust AI results

Moving Forward: Questions to Ask Yourself

Whether you’re a student wondering about your future career or a professional adapting to AI, ask yourself these questions:

1. Do I understand the foundational principles behind the work I do? (Not just how to do it, but why it works)
2. Can I articulate how my work connects to business value?
3. Do I know how to evaluate the quality of data and processes?
4. Can I identify when AI is the right solution and when it isn’t?
5. Am I building skills in areas that require human judgment, creativity, and contextual understanding?

If you can answer yes to these questions, you’re building a foundation that will remain valuable regardless of how AI evolves.

The fear those students expressed at WPI is understandable, but their concerns are based on a misunderstanding of AI’s capabilities. AI isn’t making humans irrelevant – it’s making the human role even more important. The key is to shift from doing the work to orchestrating how the work gets done.

Quick plug – if you want help navigating this change, give me a shout!

What questions do you have about adapting your skills for an AI-enhanced future? How are you seeing the relationship between humans and AI evolve in your organization? I’d love to hear about it in the comments!

Reply to this email to tell me, or come join the conversation in our free Slack Group, Analytics for Marketers.

– Katie Robbert, CEO

Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.