Within the last few years, it’s felt like AI is everywhere. From giving voice to Anthony Bourdain’s emails in a documentary to more business-applicable forms like improving and streamlining pizza delivery for Domino’s. With AI set to add $15 trillion dollars to the global economy by 2030, there’s little question that companies who want to stay competitive will need to incorporate it.
WHERE ARE THE AI PROJECT RESULTS?
Unfortunately, the vast majority of organizations remain stuck in a cycle. They continue to treat AI as an experiment, making it difficult to systematically deliver value across the enterprise. A mere 5% of organizations have streamlined and systemized the production of AI-backed solutions.
To reliably build and integrate AI solutions, it’s critical that you identify problem areas, determine whether those translate into use cases, and assess whether your company’s technological capabilities are genuinely up to the task.
METHODS OF BUILDING AI PROJECTS
So how do companies make the jump from simply wanting to create and integrate AI solutions to doing so successfully?
It begins with building a smaller version of the desired solution, which is later scaled and expanded enterprise-wide. Companies have used a variety of options for this part of the process. However, it’s important that the project begins small since scaling a solution across the enterprise is extremely complex. It requires orchestrating numerous departments and activities, such as development, business integration, and more. Before you invest too much time, money, and effort, you want to ensure as much as possible that the solution is valuable.
One method for creating a smaller solution is to use wireframes or pretotypes. But leading with user interface requirements or simply a hollow demo version of an AI solution won’t answer the business value question of whether the AI project is worth it or can deliver on your business goals.
Another method is to use Proof-of-Concepts or POCs. While they can validate technologies, they are frequently only useful for just that—proving the technology works. Organizations that utilize partners such as SoftServe will already know this information before starting most of their AI projects. Therefore, it can be difficult to find the value in using this method. POCs won’t tell you how to scale, model risk, account for data biases, etc.
A third method is a Proof-of-Value or POV. While they are a better alternative to POCs since they are focused on the ROI of a technological solution, they lack usability mapping and integration plans.
However, the best method to start your AI project is with a vertical pilot that’s narrow in scope yet comprehensive enough to cover the technology, ROI, usability, and integration.
DRIVING A SUCCESSFUL AI PILOT
Successful AI pilots require a focus on data, intelligence, and performance. It’s critical to understand the required data dependencies—from the data’s availability to potential future needs. Identify what data assets are necessary at the start, whether that’s ample for a good enough model, whether any of the data is proprietary, and how much effort it will take to gather and process it.
In addition, you need to determine the Day Zero intelligence value, embrace the potential of seemingly simple or hybrid AI/heuristic solutions, and identify the integration potential. Lastly, you must know what performance baseline you must beat for the pilot to be successful. You’ll need to evaluate how well the solution must work and in what capacity.
If you build your AI pilot with this strong foundation, you’ll reap the business benefits. One of the most significant business benefits of an AI pilot is that it provides you with early feedback from end-users, thereby allowing you to update your solutions accordingly as you go. If the pilot is well-focused in the “goldilocks zone”—where it’s relevant in the marketplace and can collectively be solved—it will accelerate the trajectory of your organization’s maturity. A goldilocks zone pilot will also enable your business to grow value cumulatively from its entire portfolio of projects.
Equally as important is the proof offered by empirical evidence where companies that approach AI projects with a pilot-first approach are twice as likely to scale these projects than those that don’t. In addition, these companies see triple the ROI on their baseline AI investment.
Yet what might be the most important benefit is that pilots drive digital transformation. They move the thinking from a one-off mindset to a systematic use of all of AI’s capabilities. Ultimately, an AI pilot lets you safely accelerate your projects, drives innovation, puts you at a competitive advantage, and increases your chances of success.
If you’re interested in learning more about driving pilot-first AI projects, be sure to watch Sebastian Santibanez’s keynote speech from the Global Big Data and AI Expo.
Then, let’s talk about how SoftServe can collaborate with your organization to build and integrate successful AI project pilots.