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- The Problem With Giving AI to IT
Every time there’s new buzz in tech (recently AI) and it hits office floors, people’s common first thought is "Let's call IT!"
At first glance, it makes sense. Leave the tech to the tech folks, right? But if we take a closer look, tossing AI over to IT might actually be one of the biggest mistakes you can make.
Why? Just look at the past - we've seen this movie before. Let's go over what we should have learned and find out how successful companies are handling their AI projects differently, so you can avoid repeating the same old mistakes – and get your AI initiative successfully off the ground.
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The Reality of IT's Expertise in AI
“You work in IT, can you fix my printer?” is a common meme and it somehow reminds me of what’s happening with AI right now.
“You work in IT, can you take care of AI?”
Spoiler alert: Not every tech expert is an AI expert (and not every AI expert is a tech expert, which doesn't make things any easier).
From my experience, the proportion of IT professionals with hands-on AI experience isn't that different from non-IT people, especially in larger organizations. To understand why, let's zoom out and look at the traditional role of IT departments.
Traditionally, IT departments have been the backbone of technical operations, responsible for ensuring that systems run smoothly, data is secure, and the technology infrastructure is rock solid. IT is the enabler and driver of established systems and technologies.
Typically, IT departments are not structured as innovation units.
Innovation is mostly part of the "business" DNA. It's the business's job to set strategic directions, define new requirements, and launch projects (which often have significant IT components).
We could have a long debate about whether the strict separation between IT and business still makes sense in the digital age, but at least at the time of this writing the divide is still a reality in pretty much every organization I've worked with.
So, the impetus for innovation should ideally come from the business side, not IT. To make this clear, consider the following scenario:
A CTO pitches an AI solution to a CEO.
The CEO might ask, “Great, what can it do for us?”
If the CTO replies, "It will save us infrastructure operating costs," he or she may have just given birth to a new IT project to add to the existing pile (which, by the way, is how many cloud migration projects started - and ended).
Conversely, if the CTO suggested that "AI could enhance our marketing strategies or improve our customer service operations," the convo shifts dramatically. It's no longer the CTO's topic.
Moreover, IT departments are often so busy with day-to-day responsibilities that they lack the bandwidth to strategically explore or implement new technologies like AI. Many are focused on keeping the lights on, not revolutionizing the business.
We’ve learned this lesson the hard way before when it comes to data.
Lessons (hopefully) learned from the Big Data Boom
When big data and advanced analytics became a thing in the early and late 2010s, many companies hired data scientists to reap the benefits. Guess where those data scientists often ended up? In IT, reporting to the CTO. The result was data initiatives disconnected from real business needs, and an entire generation of data professionals chasing an abstract thing called "business value. We are still working to bridge this divide.
The lesson is that business-critical capabilities (such as data analytics and AI) often come in a technological disguise, but at their core are not pure IT problems.
Similarly, when AI talent is siloed within IT, it's cut off from the pulse and needs of the business. Instead of asking, "Why are we doing this? Where should we apply it?" – we're asking "How can we do it efficiently?", which should only be step 2, not step 1, in your AI journey. Otherwise, you'll have AI projects that are technically impressive but practically irrelevant.
Plus, when AI is seen as IT's domain, other departments are less likely to engage. They assume it's not their responsibility or that they don't have the skills. This mindset stifles the kind of cross-functional collaboration that's essential for successful AI adoption.
The parallels to the data boom are striking and instructive.
So, if not IT, where should you look for AI expertise?
Practical Guidance: Where to Find AI Expertise
The answer might be closer than you think. With the rise of user-friendly AI tools and platforms like ChatGPT, every business user has the power to start exploring AI's potential. You don't need a PhD in computer science to start experimenting and learning.
Here’s a step-by-step guide for non-technical business users eager to harness AI:
1) Understand the Basics: Gain a high-level understanding of what AI is and how it functions. You don’t need to master the details, but understanding the core capabilities and limitations of AI is crucial. It’s similar to knowing the basics of electricity — knowing when to use it and when to avoid it (keep away from water!). Some resources to check out:
2) Identify Real Problems: Start with real challenges you face. It's easy to fall for the "when you have a hammer, everything looks like a nail" fallacy. Avoid this by thinking about your problems first and AI second. Pinpoint specific issues in your work where AI could be beneficial. See this article:
3) Experiment with Tools: Try accessible platforms like ChatGPT, Power BI Desktop, or any given AutoML tool that put world-class AI at your fingertips - no coding skills required. If you’re keen to explore, check out resource libraries like the great Nocode.ai newsletter. Some articles to start:
4) Collaborate Cross-Functionally: Connect with colleagues from various departments to brainstorm potential AI applications and share insights. Find the AI experts (and those who want to be) in your organization and team up!
5) Seek External Expertise: If you want to explore AI opportunities that require specialized knowledge, reach out to outside experts. Find partners who are willing to dive deep into your business and provide tailored, vendor-neutral solutions.
Consider Establishing an AI Center of Excellence: Similar to an analytics center of excellence, this unit can bring together business leaders, subject matter experts, data professionals, and yes, IT – especially in the early stages of AI adoption. The AICoE is critical for identifying opportunities, prioritizing projects, and aligning them with strategic goals. As AI capabilities mature within your organization, the need for centralized efforts may diminish and AI responsibilities may be more broadly distributed. Initially, however, centralizing resources is critical - when everyone is responsible, it often means no one is truly accountable.
Conclusion: The Key To AI Is You
Ultimately, the real secret to successful AI adoption isn't which department manages the technology; it's how proactively and strategically you use it. AI success starts with individual curiosity and initiative - supported by forward-thinking leadership role models and embedded in a coherent business strategy.
So don't wait for IT to hand AI solutions to you. AI is not just another piece of software to install. AI is a horizontal technology that, when leveraged effectively, can become a strategic capability to transform your entire business and drive innovation.
If you need help implementing AI in your organization, simply reply to this email, and let's get started.
Stay augmented, stay ahead.
See you next Friday!
Tobias
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