Artificial intelligence (AI) has taken the world by storm. ChatGPT was the ultimate proof of concept, demonstrating the power of large language models and AI in easy-to-understand terms. So naturally, business leaders are eager to unlock the productivity benefits that come from integrating AI into business operations.
But despite their eagerness, organizations still need to do some work to prepare for AI integration. AI isn’t magic—you still need a strong plan for deploying integrated AI in your end-to-end processes. Today, we’ll cover six roadblocks to AI integration—and how you can avoid them.
Between pressure from executive leadership and the fear of missing out (aka, FOMO), organizations run the risk of hastily chasing AI for its own sake. But chasing AI for its own sake can lead to wasted resources and prevent you from actually achieving real business outcomes. While experimenting with AI just to test its capabilities has its own merit, organizations should focus most of their AI adoption efforts on clear use cases that can generate real value for the business.
Make your organizational or departmental goals your strategic north star, and start small by using AI to solve a specific problem. For example, if you want to reduce costs in your customer support or call center, you could use decision-based AI to classify and route emails to the appropriate people, which leads to faster case resolution times and higher customer satisfaction. Or you might decide to use generative AI to automate routine tasks like customer communications by drafting customer service emails and allowing human employees to review before sending. When you start with a goal, then you can decide on the use cases for AI. This can help show AI’s direct impact to gain further buy-in for additional projects.
I mentioned earlier in this post that businesses are increasingly receiving executive buy-in for artificial intelligence initiatives. While this is true as an overall trend, there’s still room for improvement on this front. For any AI rollout to succeed, the leadership teams need to wholeheartedly stand behind the effort. They have the power to put the needed resources behind any initiative and help guide the organization through potential transition periods.
Consider potential resistance in an organization. First off, some teams will feel threatened by the advance of AI, feeling it may encroach on their job duties, the value they provide. Others simply are too bogged down in the minutiae of their daily responsibilities to adopt a new technology. Making AI a top-down leadership initiative enables you to manage change, manage worries about job loss, and ensure teams remain on board and working toward their AI goals.
While top-down buy-in is critical, it’s not enough. It takes experts from across the organization to implement artificial intelligence, advise executive sponsors, and understand the pitfalls unique to the organization. That’s why it’s critical to develop an AI center of excellence (CoE) to guide your artificial intelligence projects.
Choose CoE team members based on the use cases you’ve selected. If you’re looking to roll out a new predictive AI model to help with pricing forecasting, the team members might include data scientists, engineers, UX designers, and business stakeholders who will use the AI model. If you’re optimizing existing processes like document processing with a process automation platform to design and release content processing models, you wouldn't need a team of data experts, simply a few low-code developers to upload the batch of documents and retrain the model as necessary. But regardless of the use case, a strong CoE team can track progress, advise leadership about snags in the process, and course correct the project as needed.
Pro tip: Make sure to bring in technology groups—including both IT and cybersecurity teams—for any AI CoE effort. This ensures you can plan out infrastructure needs as well as check for security issues, which is critical for reducing AI project risks and increasing success.
It’s no secret—AI can come with privacy issues. Even Google, a strong proponent of artificial intelligence and machine learning, has warned its employees about entering confidential data into AI chatbots. But it isn’t just chatbots bearing these troubles; if you use a large public cloud provider to offer some of the infrastructure and prebuilt models for your organization, you may be sharing your data with a public provider that can then use your data for their own purposes, like training their own models. In this way, you’ll be helping everyone using that algorithm—including your competition!
That’s why it’s critical to seek out organizations with a firm commitment to data privacy for AI in business. Being able to isolate your data and ensure it never leaves your control (along with strong internal security and security from your provider) helps keep your information private. For organizations in heavily regulated industries, this is even more critical.
[Did you know that you don’t have to choose between using AI and keeping your data private? Learn more with Implementing Private AI: A Practical Guide.]
AI is built on a foundation of data. If these foundations aren’t strong, AI initiatives are nigh impossible to pull off effectively. Too many organizations have their data scattered throughout various data warehouses and data lakes across the enterprise. This lack of centralization makes it hard to operationalize AI or build AI models that truly work in the enterprise.
One solution to this problem is to use a data fabric. A data fabric enables you to work with data in a virtual layer so you don’t have to migrate data or refactor code when databases change. This provides easy access to data, enabling better predictions and accuracy from AI.
AI is getting all the press right now. But it’s not the only player on the field. It’s tempting to think AI equates to automation, but automation is a team sport. Sometimes, AI makes the most sense; other times, robotic process automation (RPA) should take the corner kick. For example, when there’s a repetitive task that involves routine steps, RPA can accomplish the task with precision. And some AI platforms make it extremely easy to build these bots with RPA task recorders that capture the steps of someone performing a task on screen, then build an automated bot to do the work. RPA is only one tool among many—just like AI. Groundbreaking though it is, AI still is just one technology. Make sure to use the right tool for the right job.
When it comes to automation, businesses need to think big. Organizations need to use all the tools at their disposal to automate as much of their operations as possible. Gartner® refers to this as “hyperautomation.” So how do you accomplish this? Read the Gartner report, Gartner Hyperautomation Report: Emerging Tech Impact Radar.