INBOX INSIGHTS, January 29, 2025: Prioritizing People, AI Models vs Apps - Trust Insights Marketing Analytics Consulting

INBOX INSIGHTS: Prioritizing People, AI Models vs Apps (2025-01-29) :: View in browser

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Why Your AI Implementation Will Fail Unless You Prioritize People

“We need to implement AI now—our competitors are leaving us behind.”

That’s what a client told me last week. But when I dug deeper, I heard a different story from his team. They were overwhelmed, worried about their jobs, and frustrated by constant technology changes that seemed to create more problems than they solved.

The real challenge wasn’t the technology; it was the human element that everyone was afraid to talk about.

This scenario is playing out across industries – from healthcare organizations where patient care teams are wary of automation, to B2B SaaS companies where marketing teams are questioning their future role. The pressure to adopt AI is immense, but the human cost of doing it wrong is even greater.

The Hidden Costs of Ignoring Your People

For enterprise organizations, the impact of a poorly managed implementation goes far beyond wasted technology investment:

Increased stress and burnout as teams try to adapt
Lost institutional knowledge as key people leave
Damaged trust between leadership and staff
Resistance to future innovation
Cultural rifts between “tech-savvy” and “traditional” team members

More concerning? These failures often create lasting cultural damage that can take years to repair.

Building a Foundation That Puts People First

Through years of helping organizations navigate technology transformation, I’ve learned one crucial truth: successful change starts with understanding and supporting your people. That’s why we developed the 5P Framework, with people at its core:

Purpose

Before discussing any new technology, start with:

Why are we really doing this?
How will it help our teams do better work?
What problems are we trying to solve for our people?

This clarity helps teams see opportunity instead of threat.

People

This isn’t just about skills – it’s about understanding and addressing very real human concerns:

What are people actually afraid of?
How can we protect and enhance people’s roles?
Where do we need to provide extra support?
How can we turn skeptics into champions?

Remember: Fear of change is natural. Your job is to make it feel manageable.

Process

Document processes with the people who actually do the work:

What unofficial workflows exist?
Where do people struggle most?
What tribal knowledge needs capturing?
How can we make people’s jobs easier?

This collaboration builds trust and surfaces critical insights.

Platform

Technology decisions must consider human factors:

How intuitive are the tools?
What training support is available?
How will this affect daily work?
Where might people get stuck?

Performance

Measure what matters to people:

How has work-life improved?
Where are teams struggling?
What unexpected challenges arise?
What support is needed?

Real World Success: Leading with Empathy

Recently, we worked with a marketing team to implement AI for content creation. Instead of focusing on just the technology, we:

Started with listening sessions (Purpose) – understanding the team’s fears and hopes.
Created safety plans (People) – documenting how roles would evolve, not disappear.
Let teams shape new processes (Process) — giving them control over their future work.
Chose tools collaboratively (Platform) – ensuring solutions felt helpful, not threatening.
Measured human impact (Performance) – tracking both efficiency and satisfaction.

Result? Not only did they achieve their implementation goals, but team satisfaction increased and they should meet their staff retention goals.

Leading Change in Your Organization

If you’re planning to implement AI, start here:

Listen before acting:

What are your teams really worried about?
Where do they need the most support?
What would make their jobs better?

Create psychological safety:

Be transparent about changes
Acknowledge fears openly
Show commitment to people’s growth

Build confidence through quick wins:

Start with pain points teams want solved
Celebrate early successes
Let teams drive some decisions

Support ongoing adaptation:

Provide more training than you think needed
Create multiple feedback channels
Address concerns quickly

Remember: Technology changes are really about people changes. Organizations that recognize this don’t just implement successfully—they build stronger, more resilient teams in the process.

The most powerful question you can ask isn’t “What technology do we need?” but rather “How can we help our people thrive through this change?”

Your team is watching. Will you lead with empathy and intention, or just hope for the best?

How are you putting people first? How are you building trust? Reply to this email to tell me, or come join the conversation in our free Slack Group, Analytics for Marketers.

– Katie Robbert, CEO

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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to stay sane amidst the whirlwind of constant AI advancements. You’ll discover practical strategies to navigate the overwhelming influx of new AI models and technologies. You’ll learn how to prioritize your business needs and focus your AI efforts for maximum impact. You’ll understand how to use frameworks like the 5Ps to make informed decisions and avoid getting lost in the AI noise. Tune in to learn how to manage AI chaos and keep your sanity!

Watch/listen to this episode of In-Ear Insights here »

Last time on So What? The Marketing Analytics and Insights Livestream, we looked at the barebones of setting up local AI models. Catch the episode replay here!

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In this week’s Data Diaries, let’s demystify something about AI models.

A lot of people have spilled a lot of digital ink about the newest AI model, Deepseek, and their (justified) concerns about privacy. To understand your level of risk, we have to understand how AI models work.

An AI model – like Deepseek R1 or OpenAI o1 – is essentially a very large statistical database. By itself, it doesn’t do much, kind of like a spreadsheet doesn’t do much.

An AI model has to be run in some kind of environment. Just as a database needs server software, AI models need AI server software.

And AI models also need some way for you to interact with them; typically, this is a web interface, like ChatGPT.

When people say they have privacy concerns about Deepseek, those concerns are about the free chat web interface at chat.deepseek.com (or their free mobile app). That destination is inside the People’s Republic of China and per Deepseek’s privacy policy, all your data is 100% being logged, reviewed, and trained on.

For that simple reason, you should never, ever use confidential or private information with their web interface.

But Deepseek the website/app isn’t the same as Deepseek the model.

You can, if you have enough hardware, download and run Deepseek the model within your own environment – and then it’s as private, secure, and safe as the rest of your IT infrastructure.

Think of the model like an engine of a car, and the server and interface as the rest of the car.

You can get in Deepseek’s car, and they’ll watch everything you do.

Or, you could put Deepseek’s engine in your own car, and then it’s a private, safe space because you own and control the car.

No AI model itself, regardless of the maker, can ever record data or call home, any more than a CSV file of numbers can call home or spy on you. They’re just databases of statistics.

By the way, the same is true for every AI service out there. Models are always safe if you’re running them inside your company, on your hardware. Interfaces? That depends on the Terms of Service, which you should always read carefully before putting private or sensitive data in.

So, Deepseek the model? Amazing. State of the art, and as private and as safe as the provider running it.

Deepseek the website/mobile app? Free, fast, and absolutely not private in any way, shape, or form.

If you’d like to learn more about how models like this work, go check out our talk from MAICON 2024 on open models.

Here’s a roundup of who’s hiring, based on positions shared in the Analytics for Marketers Slack group and other communities.

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