INBOX INSIGHTS: AI Integration Strategy Part 5, Sustainability in AI
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Approaching AI Integration Strategically - Part 5: Now What? Putting It All Together
A marketing director once told me she’d spent over $50,000 on AI tools but was “still figuring out” how to use them.
That’s a problem.
But it’s a solvable problem, and that’s what we’re here to do. Solve problems. I’ll keep it short and sweet, and actionable this week.
Throughout this series, we’ve built a framework for AI integration covering strategy, planning, implementation, and measurement. Now let’s address the critical question: So what? Let’s put it all together with real-world applications and an actionable roadmap.
How to Identify Your Best AI Opportunities
I’ll skip the personal anecdotes and get right to the good stuff. I would recommend the Trust Insights TRIPS Framework to map this out. It’s a simple sheet that will help outline all your candidate tasks and help you prioritize where to start with AI.
TRIPS stands for Time, Repetition, Importance, Pleasantness, and Sufficient Data. As you list out your tasks and score the columns, you’ll see your top winners for where to start with AI integration.
Start with high-value, easy-to-implement applications to build momentum.
High-Impact AI Use Cases to Consider
Here are some of the most successful AI applications we’ve implemented with clients:
Marketing Applications:
Customer Service Applications:
Operations Applications:
Finance Applications:
Remember to apply what we covered in the previous parts: align these with your business KPIs, build processes around them, implement with a phased approach, and ensure they support your overall strategy. Speaking of a phased approach, use this 30-60-90 day plan to focus your AI integration.
Your 30-60-90 Day Plan
First 30 Days: Assessment
Days 31-60: Planning
Days 61-90: Implementation
If you’ve done the work from the previous four parts, you can likely do this faster than the 90-day timeline. And that’s the not-so-secret secret. Gathering your requirements and data up front will save you oodles of time and headaches with execution.
The Bottom Line
AI in business is only valuable when it solves specific business problems. Successful organizations identify high-value opportunities, implement focused solutions, measure results, and expand based on what works.
Start small, be strategic, measure everything, and focus on business impact. That’s the difference between wasting money on tools you’re “still figuring out” and generating measurable value.
What business challenge will you tackle first with AI?
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 the growing risks of over-relying on generative AI tools like ChatGPT. You’ll discover the hidden dangers when asking AI for advice, especially concerning health, finance, or legal matters. You’ll learn why AI’s helpful answers aren’t always truthful and how outdated information can mislead you. You’ll grasp powerful prompting techniques to guide AI towards more accurate and relevant results. You’ll find strategies to use AI more critically and avoid potentially costly mistakes. Watch the full episode for essential strategies to navigate AI safely and effectively!
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Data Diaries: Interesting Data We Found
In this week’s Data Diaries, let’s talk about sustainability. One of the questions that keeps coming up over and over again at events like the two-day workshop I just did for SMPS AEC.AI is how much of a sustainability impact AI has.
The reality is, we don’t know. Companies that have massive data centers don’t publicize just how much energy they use. But we do know how many GPUs have been sold, as a proxy for how much energy AI could be consuming. NVIDIA holds something like a 98% market share of GPUs in data centers, so if you go by the public estimates of how many data center GPUs NVIDIA has sold per year for the last 4 years, it looks like this:
All of these are modern datacenter GPUs, A100s through the current GB200 GPUs.
That’s 15.5 million GPUs. Export restrictions, especially to China (China and Taiwan make up about half of NVIDIA’s sales), started in 2022 and ramped up over the years. So call it half of those GPUs are likely in US data centers. Let’s make it 7 million for an even number, a little less than half.
Every NVIDIA “GPU” is actually a 8 core blade. If you look at the product specs, they’ve had 8 cores since the A100. That means with 7 million GPUs, you’re talking 56 million cores. Each core uses 700 watts. That’s JUST the core of the GPU. An 8 core GPU consumes 5,600 watts.
So just on cores alone, you’re at 39.2 billion watts. (7 million GPUs 8 cores each 700 watts per core)
But we don’t use GPU cores, we use GPUs. They all need cooling and they all have heat waste. For example, the DGX H100 pod that has 8 H100 cores in it has a peak usage of 10,200 watts, an overhead power consumption of 4,600 watts above and beyond the cores themselves.
So 7 million GPUs * 4,600 watts (because we accounted for the core power already) is another 32.2 billion watts.
So the total draw is 71.4 billion watts, SOLELY for the GPUs. This doesn’t count running the actual data centers, the HVAC, etc.
To put that in context, that’s 71,400 megawatts. The average USA home at any given time is consuming 20-30 kilowatts, which means that if AI chips in USA data centers are running full tilt, then AI is using the same amount of power as 2.86 million homes.
That begs the question, how do we reduce our AI power consumption? There are a few different ways to do this, to improve its sustainability.
For tasks like summarization, extraction, rewriting - basically any task where you’re providing the data - a small model will get the job done just as well, but more efficiently in terms of energy use.
The bottom line for AI power usage is simple: use the smallest effective tool for the job. Just like you don’t need to fly a 747 to the grocery store, you don’t need to use the latest, greatest, biggest AI model for simpler tasks.
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