It’s either the best or worst time to be a technology leader. Interest in emerging technologies within enterprises is skyrocketing, from the C-suite down. Vice President and Principal Analyst Paul Miller highlighted this at a recent Forrester Technology & Innovation Summit, aptly titled “When You Should Invest In The Next Shiny Thing.” He noted, “We’re all being bombarded by amazing, new, cool innovations: generative AI, the metaverse, blockchain, NFTs, and whatever the next cool thing will be.” This bombardment isn’t just from the media and vendors but also from internal executive teams.

Identifying the right use cases for technology can be more challenging than it appears in today’s tech-enthusiastic environment. If you’re a CIO, you’ve likely heard comments like “We should do something with generative AI” or “Our competitor is using virtual reality” in meetings and had to hold back your response.

To build and present a strong business case for the right emerging technology, you must avoid the allure of the wrong technology or use case. The “shiny object syndrome” is prevalent and can be extremely costly. Take chatbots, for instance: your customer experience team may see productivity and cost-saving opportunities and push for widespread deployment. While chatbots can efficiently handle simple tasks like order status or account balance inquiries, expecting them to resolve complex or unique issues without providing another option can lead to customer frustration, a loss of trust, and brand damage. The same technology used in slightly different contexts can yield vastly different results.

So, how do you avoid the wrong technology path? Evaluating an emerging technology’s ability to deliver desired benefits involves three key factors: catalysts, dependencies, and inhibitors. This forms the basis of Forrester’s CaDI (catalysts, dependencies, and inhibitors) analysis framework.

The CaDI framework helps technology teams differentiate between distractions and genuine opportunities:

• Catalysts: These are factors that accelerate technology development, bringing it to market sooner or making previously overlooked technologies relevant. For example, machine learning and AI growth was catalyzed by cloud computing and GPU programming languages like CUDA.
• Dependencies: These are required factors for a technology to achieve its expected outcomes. This could include hardware adoption for extended reality or the availability of labeled datasets for AI. Without these, the technology may function but won’t create value.
• Inhibitors: These are factors that undermine the opportunities created by technology, such as high prices at early adoption stages or the need for new hardware or data sharing for technology to achieve its goals.

For example, analyzing TuringBots for software acceleration through CaDI reveals powerful catalysts, dependencies, and inhibitors. Generative AI models learning from billions of lines of open-source code dramatically accelerate TuringBots. However, many augmented coding tools need more training and language-specific models to reach their full potential. This opens opportunities like designer TuringBots that can turn simple sketches and natural language into front-end code with fewer dependencies and inhibitors.

Dive deeper into our comprehensive guide to effectively leverage emerging technologies. Get practical insights and strategies to build a compelling business case that aligns with your organization’s priorities and drives ROI. Don’t miss out—continue reading and download the full guide now!