In the year since Gen AI made its public-facing debut, putting it to use has quickly risen to the top of the priority list for many boards, CEOs, and other business leaders and stakeholders. The 2023 McKinsey Global Survey examining the current state of AI found that 30 percent of the companies surveyed were using Gen AI regularly in at least one business function, and 40 percent said their companies plan to invest more in AI overall thanks to advances made in Gen AI over the past year.
As companies are eager to invest in the tech and get their own Gen AI solutions up and running, the critical question they must be able to answer is how these investments are driving real business value. While this can be a challenging question to answer for a technology that is evolving on a daily basis, the four considerations below can be helpful to improve targeting while accelerating the journey from idea to implementation.
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Focus on Customer Experience.
Don’t get distracted by buzzwords or FOMO.
Instead, clearly define the goal and scope of the AI system, based on customer use cases and desired outcomes, and work backward from there. I can almost guarantee that taking this approach will not result in slapping a generic chatbot on the bottom of your website, so save that time and investment by starting with a well-defined purpose for your Gen AI initiative. This will not only help guide development but also evaluate performance, which is key to demonstrating ROI.
While the pressure to be able to say your organization is offering Gen AI is real, it’s never a good idea to let the technology lead. The business need and desired user experience should always dictate strategy.
Be Data-Driven.
Taking a data-driven approach to inform business decisions and guide strategy is what will propel an organization to reach full data maturity, which is needed for Gen AI models to generate maximum business value.
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Data is an asset, and corporate culture values data-based decision-making.
When considering Gen AI initiatives, take into account your org’s entire data journey, from data production through data presentation, and then identify where AI can augment processes. Gen AI becomes much more valuable when used to address a clearly identified gap or opportunity and powered by high-quality, timely data.
Make Safe Technology Bets.
Start with pre-built solutions. Many services provide pre-trained Gen AI models that accelerate time-to-value and can also ensure that your solutions are built on a flexible and scalable architecture. This is especially important for being able to generate a positive ROI, faster. For example, cloud services like the Amazon SageMaker suite can accelerate development and reduce undifferentiated heavy lifting.
Gen AI is a rapidly evolving field, and the leading model today will be a laggard tomorrow. To mitigate this risk, organizations should look to build solutions on top of modular architectures that can support rapid changes.
Engage in Rapid Prototyping.
Don’t waste time trying to create the perfect model (spoiler alert: it doesn’t exist).
To get to the point of driving real business value with Gen AI, it’s more important to quickly build a working model in order to test ideas, gather feedback, and iterate quickly on the design. This iterative process allows stakeholders to visually see ideas and give early feedback before investing time and effort into full development. A culture of experimentation fosters a solution that’s right for your business.
Unfortunately, Gen AI isn’t a magic wand, but it can be an extremely powerful tool when leveraged strategically. While it can be tempting to fast-track a project for your organization to capture a piece of the conversation and investment swirling around Gen AI, an implementation means nothing if it’s not driving meaningful results.
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It’s equally important that your organization is able to move quickly and that you’re building a solution that addresses a real business need. You can be sure to accomplish both by focusing on the end user experience, letting data drive your decision-making, leveraging pre-existing models from leaders in the field, and not being afraid to fail fast, learn, and improve rapidly with a culture of experimentation.
