The best way to upskill and upgrade my performance using AI tools for employees across an adtech company
Welcome! I am looking forward to helping you build a clear, effective strategy for upskilling your workforce.
To make sure we focus on what matters most to you, what is your role in the company, and what specific challenges are you currently facing with your team's AI adoption?
I'm an executive looking for a company-wide AI adoption plan.
It's great to connect with another executive. Designing a company-wide initiative requires a strategic approach that balances immediate efficiency gains with long-term capability.
Defining AI Literacy in Adtech
To build a targeted plan, we first need to define what AI literacy means for an adtech firm. It isn't about teaching every employee how to code neural networks from scratch; rather, it is about developing functional fluency across your organization.
In an adtech environment, AI literacy breaks down into three core pillars:
- Data Fluency & Tool Usage: Understanding how to use algorithmic tools, automated bidding systems, and generative assistants in daily operations.
- Critical Evaluation: Recognizing bias in , spot-checking automated campaign outputs, and validating predictive data.
- Ethical & Brand Safety Assurance: Ensuring customer data privacy, compliance with regulations, and protecting brand safety when utilizing generative tools.
Can you give an example of AI literacy for a media buyer vs a creative manager?
That is a great question. In adtech, AI literacy looks completely different depending on the job function because each role interacts with different parts of the platform stack.
For a , AI literacy centers on algorithmic efficiency and data evaluation: knowing how to set constraints for automated bidding engines, interpreting why programmatic algorithms shift impression allocations, and auditing performance outputs for automated anomaly detection.
For a creative manager, AI literacy focuses on generative workflows and scale: utilizing prompt engineering for rapid asset iteration, using computer vision models to tag visual assets, and ensuring generated ad variations comply with brand safety rules.
How do we identify which AI tools teams are already using?
Before we move to selecting platforms, let's take a quick snapshot of where your company stands right now.
To start our assessment, could you share what AI tools your teams are currently using (even if informally), and which departments seem most eager or hesitant about AI adoption?
Teams use ChatGPT and Copilot informally; media buyers are eager, but creatives are hesitant.
Thank you for sharing those insights. Knowing that your team already relies on tools like ChatGPT and Copilot informally gives us a great starting point for structured adoption.
The contrast between your eager media buyers and hesitant creatives is a classic dynamic in adtech. It highlights two distinct priorities for your skill-gap roadmap:
- Media Buyers (High Eagerness, High Execution): Their immediate gap isn't basic tool adoption, but moving from simple output generation to strategic guardrails, algorithmic auditing, and advanced data evaluation.
- Creative Managers (High Hesitancy, High Risk Perception): Their primary gap is psychological safety and creative control. They need clear frameworks around brand safety, asset ownership, and seeing AI as an efficiency assistant rather than a replacement for human artistry.
By translating informal use into concrete priorities, you can build tailored pathways for each team rather than a generic, company-wide mandate.