An impressive 92% of organizations plan to invest or have already invested in artificial intelligence (AI). With so many organizations on their AI adoption journeys at varying levels of AI maturity, we can learn from those who led the charge on these initiatives. From identifying use cases and integrating AI into your architecture to getting your employees on board and measuring your success, the IT leaders at these organizations have been through it all — and they have a lot of wisdom to share.
Table of Contents
What is AI adoption?
First, some background. Researchers from the National Bureau of Economic Research define AI adoption1 as using AI for production — that is, using AI to get work done within the organization. This would include support engineers using an AI-powered tool to find the information they need to help customers with their requests, or a car manufacturer using AI-powered predictive analytics to analyze sensor data from machines to predict failures and maintenance requirements.
The progression of AI adoption: Key statistics and trends
In the past year, we saw a surge in AI adoption across the globe. A 2024 survey found that 72% of organizations2 integrate AI into at least one business function — this is a huge leap from the 55% in 2023. Still, large companies are taking the lead on AI adoption. Half of organizations with more than 5,000 employees1 use AI. And 60% of companies with more than 10,000 employees use AI. As for industries, the manufacturing, information, and — perhaps surprisingly — healthcare industries are the leaders in AI adoption, while finance, insurance, and real estate have lower adoption rates.
With this widespread AI adoption, the reality is that not all projects are successful. In fact, 70% of CIOs3 reported a 90% failure rate for their custom-built AI applications. But, it’s not all bad news! The Boston Consulting Group found that the companies that have adopted AI early claim 1.5x higher revenue growth than other companies. In addition, 74% of enterprises4 using generative AI (GenAI) are seeing a return on investment. Not all projects will be successful — and the ones that aren’t, you can learn from. The successful ones will help you stay competitive, bolster your revenue, and advance your AI maturity.
To help you on your AI adoption journey, we talked to three IT CxOs who are early adopters of AI to gain insight into their own journeys. We talked about where they’ve faced challenges, how they’ve harnessed opportunities, any best practices they’ve uncovered, and what AI endeavors have been successful.
What IT leaders have learned on their journey to AI adoption
1. Start with the problem
The best way to incorporate AI capabilities into your organization is to start with a high-value problem you’re trying to solve. Rick Rioboli, EVP and CTO at Comcast Connectivity and Platform says, “Forget about AI, what is your biggest problem?” Focus on problems that, when solved, will have a dramatic impact on business. There are a variety of generative AI use cases that organizations are already exploring that you could take inspiration from. Once you’ve identified your problem, start thinking about what data you’ll need to feed your AI model to address this problem.
2. Embrace experimentation
Cynthia Stoddard, SVP and CIO at Adobe, encourages her employees to get creative. Stoddard says, “We’ve created an innovation hub that allows employees to understand what tools and Adobe products they can use to experiment with and solve real business problems.” This not only empowers employees to try new technology and create new solutions but also aids in the cultural transformation that comes with such a dramatic organizational change.
3. Use the right data
And make sure it’s quality data. Generative AI models are trained on massive amounts of data from the public internet, but they don’t have current data and wouldn’t have been trained on your data. To get the most value out of AI, you need to be able to pass your proprietary data to the generative AI model, which is done through retrieval augmented generation (RAG). On top of having the right data, you need to make sure that it’s quality data and will give you relevant, accurate answers. Poor quality, inaccurate data will provide misleading results. Matt Minetola, CIO at Elastic, says, “Having a solid data strategy is essential. Without unified and accessible data, even the most advanced generative AI initiatives will struggle to deliver real value.”
Dive deeper into the CxOs’ AI experiences
4. Quantify impact
Once you identify your ideal outcome and confirm you have the right data, you need to continuously quantify what success looks like — from your MVP to your ideal solution. Whether that’s an increase in your net promoter score (NPS) to signify an improvement in customer experience or a decrease in the mean time to respond to show efficiency gains, make sure you can quantifiably show that the initiative was successful. Stoddard says by keeping an eye on performance, you’re able to determine if projects need to be tuned or, in some cases, dropped because you’re not getting the results you were expecting. And while monitoring business impact, you should also be monitoring the health and performance of your AI systems. This includes user satisfaction with the experience and accuracy of the outputs.
5. Avoid AI sprawl and technical debt
Organizations may be tempted to use different point solutions for different problems to try to get applications stood up quickly. Minetola warns that “the businesses who solved in pockets are starting to see the long-term cost. If they’ve done five to six different solutions with five to six different vendors and have to glue that together, the cost of that will be huge.” The technical debt — the implied cost of the future work required to revise a project because speed was valued over long-term usability — and the data silos and compliance mess will make future AI endeavors a challenge. Stoddard says that all AI initiatives go through an architecture review to ensure they will fit into existing infrastructure.
6. Use AI to predict and decide
AI is an incredible tool when it’s used in employee- and customer-facing products. It’s also a powerful tool when used to make predictions and business decisions. “We look at using AI in our profitability and precision in how products are going to be used. We try to predict if we will get the usability out of our products that we thought we would,” Stoddard says. On using data and AI to make business decisions, Minetola adds, “You can think of this as the multiplier effect that can truly take your organization to the next level by making every decision count.” When each decision is backed by (accurate and contextual) data, you can ensure it’s the most optimal one.
7. Implement guardrails
Governance and risk management are essential parts of your AI journey and must be prioritized. Stoddard says for AI at Adobe, the team relies on governance and examination of potential risks to “make sure it’s safe, we’re using the right data, and we’re doing the right things for our customers.” Compliance is only going to become a bigger issue across markets as more laws around AI technologies are put in place. “You’re going to have multiple compliance issues if you don’t understand how the data for your AI was generated,” adds Minetola.
Future-proof your AI adoption strategy
When it comes to scaling your strategy and making it work in the long-term, ensure that you’re not operating in silos. AI shouldn’t be thought of as individual solutions but an interconnected ecosystem that you’ll be able to grow as your use cases expand. Your data is your most valuable commodity. Avoiding silos and having the ability to access data no matter the environment will help as you scale and need to comply with new laws and regulations.
The AI adoption journey is not a race; it’s a marathon. Start with a strong data foundation and a solid use case to expand from there. If you haven’t started with AI yet, you haven’t missed the boat! There’s still time to future-proof your organization and stay competitive. You have an excellent opportunity to create an AI program that is scalable and transparent and that works for your needs. Check out this webinar in partnership with Fast Company for additional insights from these CxOs to help you along your AI adoption journey.
1 MIT Sloan, The who, what, and where of AI adoption in America, 2024.
2 Statistica, Adoption of artificial intelligence among organizations worldwide from 2017 to 2024, 2024.
3 IDC, IDC Executive CIO QuickPoll Series: Operationalizing AI, 2024.
4 Google, Global survey: How leaders are generating value from generative AI, 2024.
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