AI Adoption: Why It’s More Than Just Flipping a Switch

Mar 22

AI is everywhere. You can’t scroll through LinkedIn without seeing another post (hehe, this one included) about how AI is revolutionizing industries, replacing jobs, or making someone’s workday magically easier. But let’s be real: getting AI up and running in an organization is about more than just flipping the metaphorical AI switch.

Behind the sleek marketing decks and bold promises of instant automation lies a harsh reality — AI adoption is messy, expensive, and full of challenges that most leaders aren’t prepared for.

If you’re leading an AI program or just curious about what it actually takes to make AI work in an organization, here’s what you need to know.

1. The Big, Bad Data Problem

AI Needs Fuel (aka Data), and Yours Might Be a Mess

AI runs on data. But here’s the kicker: most organizations don’t have AI-ready data. If you’ve ever worked on a tech project, you know this reality all too well. Technical debt and data cleanup have always been that annoying splinter in your foot — persistent, painful, and hard to ignore.

Companies have spent decades collecting data across different tools, systems, and formats — none of which were designed for AI. The result? Your AI project might stall before it even starts.

The Real Challenges:

Data Silos: Sales has one dataset, marketing another, finance a third. None of them talk to each other. AI needs unified, structured data, and most companies don’t have it.
Data Quality Issues: AI is only as good as the data it learns from. This is fairly common knowledge by now, right? Naturally, if your data is incomplete, biased, or outdated, your AI will be too.
Integration Nightmares: Your legacy systems weren’t built for AI. Retrofitting AI into existing workflows often requires expensive middleware, APIs, and cloud migrations.
AI Maintenance: Yes, even AI needs maintenance. I’m sure one day, AI will brush our teeth and put our socks on for us — but that day is not today. AI models are not “set and forget.” They degrade over time, requiring ongoing data updates, retraining, and monitoring.

The Solution: Get Started on Data Cleanup

Before investing in AI, invest in data cleanup — it’s the foundation of everything. Don’t worry, I’ve got you covered. I wrote a whole article dedicated to starting your cleanup journey — check out my step-by-step guide here: How to Clean Up Your Data for AI Adoption.

But let’s talk about what leaders need to get started on today:

Audit Your Data: Use tools like Collibra or Alation to map your data landscape and identify gaps, duplicates, and inconsistencies. (This is arguably the most important step — no matter where the AI space evolves, we’ll always need accurate, clean data.)
Centralize Your Data: Invest in a cloud-based data warehouse like Snowflake, BigQuery, or Redshift to bring all your data into one place.
Automate Cleaning: Tools like Trifacta or OpenRefine can help standardize formats, remove duplicates, and fill in missing data.
Govern Your Data: Establish clear ownership and policies for data collection, storage, and usage. Tools like Informatica or DataRobot can help enforce these rules.

By simply assessing your technology and data sources, you’re already ahead of the curve.

2. Spot the AI Value in Your Workflow

How to Choose the Right AI Tools for Your Business

Most businesses rush into AI without a clear strategy. I’ve said it before, and I’ll say it again: one of the biggest issues in AI initiatives is unclear usage. Just because an AI tool exists doesn’t mean it’s the right fit for your workflow.

You see, it’s tricky for leaders to choose the right solution. Which expensive tech is going to deliver the best ROI?

Here are some things to consider:

Automation vs. Augmentation: Are you replacing human tasks, or enhancing them? AI works best when it supports, not replaces, human expertise.
Transactional vs. Strategic AI: Some AI tools handle basic automation (chatbots, RPA). Others provide deep insights (predictive analytics, fraud detection). Are you investing in the right type?
Cloud AI vs. In-House Models: Disruptive companies like DeepSeek R1 are proving that smaller, more efficient AI models can be just as powerful as massive cloud-based solutions. Do you really need an expensive AI SaaS tool, or could you run your own AI in-house for a fraction of the cost? (Now things are even more confusing — thanks, DeepSeek.)

The Solution: Align AI with Business Needs

Start Small: Pilot AI in one department or workflow to demonstrate ROI before scaling. I’m a big fan of iteration — test quickly and often.
Upskill Your Team: Invest in AI literacy programs to help employees understand and work with AI tools. Platforms like Coursera, Udacity, or LinkedIn Learning offer great courses. Or, you know, hire experts like Blusail — hint hint, wink wink.
Choose the Right Tools: Evaluate AI solutions based on your specific needs. For example, use UiPath for automation, H2O.ai for predictive analytics, or Hugging Face for natural language processing.

3. The Fear of Job Displacement

The Industries Feeling the Shift

It kind of reminds me of Shrek — Lord Farquaad says, “Some of you may die, but it’s a sacrifice I’m willing to take.” There’s no denying it: AI is and will continue to reshape the job market. It’s fair to say — all of us — are at risk of change. But change isn’t always bad, and what I’m learning is that the best AI solutions are all about human collaboration.

Yes, some industries are being hit harder than others. Let’s take a quick look at what’s already happening in Law:

Case Study: The Legal Industry

Junior Legal Roles Are Disappearing: AI tools like Harvey AI can draft contracts, review case law, and analyze risk faster and cheaper than entry-level lawyers.
Training Pipelines Are Breaking: Law firms used to train new lawyers by having them do research and document review — work that AI now automates.
New Roles Are Emerging: AI-savvy lawyers are in demand, but firms are struggling to retrain existing employees for AI-enhanced workflows.

The Solution: Navigate Displacement Responsibly

Invest in Upskilling: Provide training programs to help employees transition to AI-enhanced roles. For example, teach lawyers how to use AI tools like Casetext or ROSS Intelligence. I would, again, shamelessly plug Blusail — but you get the picture. We skill up your workforce for AI.
Create Ethical Frameworks: Develop guidelines for AI use that prioritize fairness, transparency, and accountability. I plan on talking more about this soon.
Focus on Augmentation, Not Replacement: Like peanut butter and jelly, AI and humans are better together. (Insert romantic montage of me and my laptop here.) Leaders need to use AI to enhance human capabilities, not replace them. For example, AI can handle repetitive tasks, freeing up employees for higher-value work.

4. The Biggest Challenge of Them All: Energy

AI’s Dirty Little Secret

AI isn’t just expensive in dollars — it’s expensive in energy. The explosion of AI adoption is putting serious strain on power grids, and companies are scrambling to keep up.

The Harsh Energy Reality:

Massive Energy Consumption: Training a single large AI model like GPT-3 can consume over 1,287 MWh of electricity — equivalent to the annual energy use of 120 homes. A single ChatGPT query uses 10x the energy of a Google search.
Tech Giants Are Turning to Nuclear Power: Companies like Microsoft, Google, and Amazon are investing in nuclear energy to sustain their AI workloads.
Sustainability Concerns Are Growing: AI is being used to fight climate change, but ironically, its carbon footprint is massive.

Though this is a challenge, I think it’s a good one. Now more than ever, we need to create better, innovative energy solutions.

The Solution: Mitigate AI’s Energy Footprint

This has to be a multi-pronged solution — we need to create more efficient AI solutions, improve manufacturing (let’s save the semiconductors chat for another day), and develop better sources of energy.

Use Efficient Models: Smaller, more efficient AI models like DeepSeek R1 or TinyML can deliver similar results with far less energy.
Partner with Green Energy Providers: Choose cloud providers like Google Cloud or AWS, which are investing in renewable energy to power their data centers.
Optimize Workloads: Schedule AI training during off-peak hours to reduce strain on the grid.

The Bottom Line: AI Adoption is Hard

As with all things — let’s work to make it less hard.

The AI hype cycle is peaking, but leaders who blindly rush in without addressing these core challenges will struggle.

The companies that succeed with AI will be the ones that:

Focus on data infrastructure first.
Align AI with real business needs.
Navigate job displacement with ethics in mind.
Factor in AI’s massive energy consumption.

AI is powerful, but it’s not magic. If you want real ROI, you need a real strategy.

What’s Next?

How is AI adoption going in your organization? What challenges are you facing? Let’s talk about it — drop a comment or DM me! And, of course, you can always find me at Blusail.co.

Tori O'Sullivan