Insights
3 AI Principles to Drive Your Digital Transformation
- Article
- 5 MIN READ
- June 17, 2024
-
Michael GeorgasSenior Director, AI
Summary
Generative AI and machine learning have emerged as transformative forces, reshaping industries and redefining what’s possible. As major players like Google, Microsoft, and OpenAI continue to invest in developing new tools and technologies, 80% of enterprises are projected to leverage generative AI by 2026. This trajectory highlights why so many organizations are assessing the role of AI and integrating it into their workflows to remain competitive. Businesses must also consider the immense potential for AI’s impact both internally and externally: 75% believe AI revolutionizes customer engagement, while 80% are already experiencing productivity gains within their business. At Apply Digital, we understand AI's true power lies in its technical capabilities and strategic application — and we’re supporting our clients through the transformation.
However, as with many other powerful technologies, potential mistakes (such as “hallucinations”), misuse, and security risks demand a thoughtful approach; especially as the technology continues to evolve. For this reason, organizations must develop guiding principles for their AI ambitions. Our Apply Digital team has undertaken this process, establishing three core principles that guide active AI efforts with clients and within our business.
Principle 1: Drive value with speed and scale
Driving value for our clients with speed and scale starts with developing a deep understanding of their unique business challenges. Once these challenges are understood, it’s crucial to ensure we’re working on solutions with the potential for measurable impact. This can include helping to shape our clients’ overarching strategy or business goals and then identifying specific focus areas to apply AI — whether it’s improving customer experiences, reducing costs, or driving sales. A structured, disciplined approach enables us to apply our expertise to unlock value reliably.
In addition, we use rapid prototyping with The Lab (our innovation and research hub) to show the potential of these solutions and explore the most efficient build-vs-buy options. For example, we recently illustrated how vector search technology could be paired with chat to help a consumer find the right product within a large eCommerce catalog, ultimately improving the user experience. We translated a strategic priority into a clear, actionable plan leveraging AI that drives greater value with speed and scale.
Principle 2: Navigate AI’s evolving roadmap responsibly
Rapid advancements in AI technology have rightfully raised concerns regarding model accuracy and security, and companies need to consider the balance between risk and reward. We rely on a combination of technical expertise and business experience with AI to support our clients in mitigating risk across industries and platforms. Behind this approach, we first make sure to understand each company’s unique situation and move forward with “quick win” initiatives that showcase impact and deliver value early; ultimately gaining buy-in. These initiatives are often intentionally low-risk and removed from sensitive data and riskier considerations where possible.
But regardless of where AI is introduced, companies must implement a robust data validation process, continuous model monitoring, and transparency about where AI is implemented when appropriate. For example, the personalized recipe builder Apply Digital developed for Kraft Heinz acknowledges the potential for inaccurate recipes and communicates them clearly to the end users.
Principle 3: Enhance human efforts
AI integrations require a responsible, thoughtful implementation approach that compliments existing human skill sets. The most successful AI solutions enhance human capabilities rather than replace them, and our approach prioritizes weaving AI into existing workflows instead of replacing them entirely. In many cases, “rip and replace” implementations suffer from several common pitfalls, including corner-case management or long implementation timelines, particularly where AI is introduced as a new process tool. Instead, we focus on creating collaborative solutions that leverage AI where it’s most effective. For example, to provide personalized experiences, automate repetitive tasks, and deliver valuable insights while keeping humans in the loop for judgment and guidance.
In one case, we recently explored leveraging language models to study call center transcripts, developing a set of caller personas that could be used to support the training of human call agents. The benefit here is that AI is used to draw insight from a volume of calls that would have been prohibitively challenging for a human. However, the skill of experienced human agents is still trusted to handle the most sensitive and challenging calls.
Another example of accelerating human efforts is an internal application built for our Project Managers to create Jira tickets more efficiently. Humans are still responsible for reviewing and revising the content but benefit from AI task acceleration. On the creative side, teams also use AI tools like ChatGPT to spark animation concepts, speed up prototyping, and elevate the customer experience. Each of these applications enhances human efforts to drive innovation, save time, and improve the user experience.
Applying the AI Principles
AI continues to evolve quickly, and many organizations are racing to unlock its value. Companies waiting to see where the technology settles risk being left behind while those forging ahead navigate many inherent concerns. The three principles shared here were established to balance speed, value, and care, and we are actively applying them with our clients and internally with Apply Digital teams. We invite you to apply these principles in your organization as you consider how AI is changing your competitive landscape.
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