Why Waiting for the "Killer AI App" Could Leave You Behind

Generative AI (GenAI) has taken the tech world by storm, becoming the fastest-adopted technology in history. Two years after its release, 39.4 percent of Americans aged 18-64 reported using GenAI— a rate of adoption nearly double that of PCs and the Internet, which saw only around 20 percent usage in the same time frame. Despite this rapid uptake, AI's integration into organizations remains surprisingly limited. While 72 percent of companies claim to have integrated AI into at least one business function, only 6.1 percent use AI to produce products or services.

This gap between hype and reality raises an important question: Why are so many decision-makers hesitating to fully embrace AI?

Why the hesitation?

Several valid reasons explain why organizations are cautious about diving headfirst into AI initiatives:

Talent shortages: The expertise required to implement and manage AI systems is scarce. According to a recent survey, 44 percent of executives identified a lack of in-house expertise as their primary obstacle to advancing with GenAI, and 69 percent of HR leaders reported greater difficulty in hiring candidates with sufficient AI skills compared to those in traditionally challenging roles such as data analytics, data science, software engineering and UX design.
Security concerns: AI introduces new risks around data privacy and cybersecurity. According to our CISO's Guide to AI research report, only 24 percent of generative AI projects are secured, yet 46 percent of leaders cite data security as a top concern. This mismatch between actual protections and perceived risk is fueling hesitation to scale.
Integration complexity: Approximately 65 percent of IT and business decision-makers have indicated that their IT infrastructure needs modernization to support GenAI applications. As organizations expand their usage, integrating AI into legacy workflows and systems not designed for modern AI tools can be challenging or infeasible.
ROI uncertainty: Many leaders find it challenging to quantify AI's value. More than 70 percent of chief financial officers (CFOs) reported difficulty in monetizing AI, despite nearly 90 percent stating AI is a mission-critical priority over the next five years.

What's the struggle with ROI? There's a deeper issue at play: GenAI doesn't behave like traditional software. We're not used to computers confidently lying to us! Unlike deterministic systems that produce predictable outputs, GenAI can be unpredictable and therefore difficult to trust. Its demos are often impressive, but implementation frequently reveals gaps in reliability and clarity. Decision-makers are left wondering what AI is truly good for — and whether it's worth the risk.

Waiting for the "killer app"

Many organizations await a "killer app" — the transformative use case that will make generative AI indispensable. Historically, this approach has worked:

PCs became ubiquitous thanks to spreadsheets and word processors.
The Internet gained traction through search engines that quickly answered questions.
Mobile devices gained widespread adoption with social media and became feasible in corporate environments with mobile device management (MDM).
Blockchain, lacking a clear killer app, proved that waiting can sometimes be prudent.
GPUs found their killer app in generative AI itself.

Potential killer apps for GenAI include coding assistance, content generation, image creation, customer service automation and enterprise search.However, none of these has yet emerged as the definitive game-changer.

What makes GenAI different?

GenAI stands apart from previous technologies because of its accessibility and flexibility. It handles unpredictable scenarios and ties software closer to real-world usage without needing to predict every possible outcome. Unlike traditional tools, GenAI is limited only by the information we provide, and it opens doors to use cases that were previously unimaginable.

But what if there is no killer app? GenAI might be more akin to cloud computing— a foundational technology that has become a popular operating model rather than a single-use application. Cloud revolutionized industries by enabling possibilities that weren't previously feasible; perhaps generative AI will do the same.

The GenAI "flywheel effect"

Traditional software development has long aimed to reuse code, hoping that breaking it into smaller, modular pieces would make it easier to apply across different projects. However, this often didn't work because the code depended too much on responding to specific situations. For the software to be helpful, developers needed to fully grasp the exact context in which it would be used. When circumstances changed, the code often required significant changes or even had to be rewritten from scratch.

GenAI, on the other hand, thrives on adapting to different situations. Unlike traditional software, it doesn't need every detail planned out. Instead, it takes in various inputs and provides solutions tailored to the user's needs. This flexibility makes it easy to reuse, not by recycling fixed components but by using its ability to adapt. Whether writing code, creating images or answering questions, GenAI adjusts quickly to different tasks, offering unmatched versatility and making it valuable in many areas.

This easy reusability creates a "flywheel effect," where each AI initiative is easier to implement than the last. The first effort might take much longer than anticipated, but the ability to redeploy large sections of the application for an entirely different purpose dramatically reduces the time-to-value of each subsequent use case. This allows rapid experimentation and agility that can have tremendous ROI benefits.

Why organizations shouldn't wait

While waiting for a killer app may seem prudent, this flywheel effect should change your cost of delay calculation. The organizations that start early and see projects through to completion will not only move ahead of the competition, but the flywheel effect will allow them to move at a much faster, perhaps exponential, rate. By the time the late adopters get started, it may be too late to catch up.

At WWT, we've already seen the value of starting our journey early:

Atom AI: We developed an internal tool that gathers knowledge from past projects to answer user questions and provide insights. It has become an invaluable resource for our employees and is now available for our customers to use as well.
RFP Assistant: We leveraged what we learned from Atom to build an application that parses incoming requests for proposal (RFPs) and prepopulates responses by pulling relevant information from various internal databases. Not only has it resulted in time savings, but it has generated revenue by allowing our teams to respond to and win RFPs we wouldn't have responded to in the past.
Copilot for Microsoft 365 adoption:We deployed Copilot to enhance employee productivity by assessing user personas, integrating it with tools like Salesforce and ServiceNow, and supporting adoption through robust training. On average, Copilot has saved employees 446 hours weekly by automating routine tasks.

These applications demonstrate how GenAI can help organizations share knowledge effectively and turn information into action.

What should you do?

GenAI isn't just another tool; it's a revolutionary way of thinking about technology and collaboration. Those who act now will help shape its future and lead their industries forward.

To capitalize on generative AI's potential:

Dream and experiment: Explore creative ways this technology could transform your business.
Collaborate and implement: Work with teams across your organization to identify use cases.
Start small: Use existing tools or pilot small-scale projects before scaling up.
Focus on knowledge sharing: Find ways to extract valuable information from employees' minds and make it accessible.

Perhaps the true killer app for GenAI won't be a single product or service — it will be how GenAI enables your organization to achieve outcomes you never even dreamed of.