From Hype to Reality: What the Rally Innovation Conference Taught Me About AI's Future

How to navigate AI's Trough of Disillusionment like you've been there before.

Last week, I attended the Rally Innovation Conference in my hometown of Indianapolis, largely due to the deep bench of speakers focusing on AI and the future of work. Given my obsession with both topics, it felt like a natural fit. 

I went in with three primary goals…

1. I wanted to have as much fun as possible

2. I wanted to learn as much as I could from people at the forefront of AI and the future of work

3. I wanted to answer a simple question for myself… What’s going on right now and how should I be thinking about it?

Maybe you’ve felt this, too. The current state of technology feels choppy and unpredictable. Funding has been volatile, with investors rushing into AI while pulling back from other areas. AI has disrupted entire business models, leaving companies scrambling to adapt. Traditional go-to-market strategies like SEO and PPC are undergoing radical transformations, making it hard to rely on past successes. The pace of change is unsettling, and many of us are struggling to find solid ground. But amidst this chaos, there’s an opportunity to redefine how we think about technology and its role in our work and lives.

Between the conversations I had on the floor, the sessions I attended, and a private dinner I hosted for CEOs, engineers, and investors interested in AI, I learned a ton about the AI landscape and where we’re going from here. 

I decided to break this down into 3 primary component parts… Where have we been, where are we, and where are we going? 

Where we've been: lessons from past tech booms

“History is an input for the future.” - Tia White, day 1 keynote at Rally 2024

Let’s start by going back. 

Over the past 20+ years, we’ve witnessed some of the most dramatic transformations in tech history. The dot-com bubble in the late '90s taught us the dangers of unchecked speculation, but it also laid the foundation for the internet we know today. The introduction of the iPhone in 2007 revolutionized mobile technology while giving rise to the app economy and changing how we interact with the world. 

We’ve seen tech giants rise and fall, empires built on innovation that became obsolete in just a few years. Now, we’re at another watershed moment. The rapid advancement of AI feels both exhilarating and terrifying. If history has taught us anything, it’s that these moments of upheaval often lead to greater innovation and progress, but they also require us to navigate uncertainty with courage and foresight.

I thought the easiest way to “place ourselves” in the current moment was by remembering some of these OTHER watershed moments in tech history, and the friends (and companies) we made along the way. So I asked a lot of people some weird questions…

We are in a scary moment right now. If you’re in tech and you’re not scared, you’re probably not paying attention. Truthfully, there’s an entire generation of tech companies founded between 2010 and 2020 whose entire business models are coming into question. That said, we’ve been here before and every time we’ve gone through one of these cycles, we come out bigger and better than ever.

So, if that’s the case, where are we now? 

Where we are: what organizations are doing today to get value from AI 

“AI is here to enhance humanity, not destroy it.” -  AJ Richichi, Session Speaker at Rally 2024

Tech is in a very weird place… Investment in AI initiatives is going way up, but understanding of AI-centric initiatives is still incredibly low. 

We’re currently entering the Trough of Disillusionment. The early hype around AI led many to believe it could solve all of our problems overnight, but reality is setting in. Companies are now saying, ‘Enough of the hype, we need results.’ Today, 65% of businesses use GenAI regularly, but only 13% have regular use cases. Even more striking, only 5% of businesses are able to show any material gains from using AI. 

This stark disparity highlights a critical challenge: the technology’s potential is immense, but realizing that potential requires more than just implementation; it requires strategic, data-driven approaches that focus on measurable outcomes. These numbers need to shift if we’re going to move from disillusionment to the next lifecycle of this technology. Companies that can navigate this phase effectively will be the ones to reap the benefits when AI reaches the slope of enlightenment and, eventually, the plateau of productivity.

Far too many are squandering this opportunity on despair and panic instead of leaning into where we’re going… Before long, we’ll be on the slope of enlightenment, and the people who did the hard work in this period to understand the most effective ways to tap and deploy this technology will get an outsized advantage during the plateau of productivity. 

Understanding where we are today allows us to better anticipate where we’re headed. The organizations that can look beyond the current challenges and focus on building a sustainable AI strategy will lead the way into the future. Let’s explore where we’re going next.

Where we're going: Crafting the future of work together

“The best solutions aren’t the most sophisticated, but the most elegant” - Tyler Foxworthy, Session Speaker at Rally 2024

There are two aspects of our future that need to be discussed… 

1. How are we going to unlock the potential productivity?

2. What new guardrails do we need to consider as this new technology gains steam? 

Luckily, the Rally agenda helped to answer both.

First, regarding productivity unlocks, there is an abundance of evidence that there’s market demand for solutions here. We’re investing more and more in an AI-driven future. Last year, AI captured a quarter of all venture dollars; in Q2, it moved to 38%. This surge in investment reflects a growing belief in AI’s transformative potential, but it also underscores the uncertainty that surrounds it. 

While there’s no shortage of funding, there’s still a lack of consensus on what specific technologies are needed and how they should be deployed. This dearth of best practices can be daunting, especially for organizations without a clear AI strategy. However, for forward-thinking business leaders with a high risk tolerance, this ambiguity presents a massive opportunity. Those who are willing to experiment, iterate, and learn from their AI initiatives will be the ones to set the standards and lead the charge as the technology matures.

I also had the chance to hear from Indiana Senator, Todd Young, who co-sponsored the CHIPS and Science Act with Chuck Schumer, about the work he’s doing to identify the right legislative guardrails for things like AI and data privacy. The two also authored a roadmap for Artificial Intelligence policy in the Senate.

The 30-page bipartisan document outlines several proximate next steps and objectives for AI policy. The document highlights the need for a national data privacy standard, which Senator Young emphasized as critical. Among the key takeaways are the recommendations for increased federal investment in AI research and development, the establishment of clear transparency requirements for AI systems, and the need for robust testing and evaluation standards to mitigate potential risks. 

The document also calls for enhanced collaboration between the public and private sectors to ensure that AI advancements are aligned with democratic values and that the technology is deployed responsibly. These policy initiatives aim to position the United States as a global leader in AI while safeguarding the rights and privacy of its citizens.

Key Lessons from Rally 2024

So, driving back to actionablility… What can we take back to our organizations if we’re going to embrace this shift and lean into that slope of enlightenment. I walked away with 3 key insights that every organization can leverage. 

• The best companies created rabid users internally: The companies that excel in AI aren’t just those with the best technology; they’re the ones that build a culture of internal enthusiasm and ownership around AI. These organizations empower their employees to experiment with AI tools, learn from failures, and celebrate successes. This creates a flywheel effect where employees become evangelists for AI, driving broader adoption and deeper integration across the organization.

• Success in this era is about improving, enriching, and deploying your data into AI models: Everyone’s talking about models and prompts, but the real value lies in internal data and knowledge management. AI models are becoming commodities, but what differentiates the leaders from the laggards is how effectively they can feed their AI systems with high-quality, well-organized data. This means investing in data infrastructure, cleaning and enriching data sets, and ensuring that your organization has a robust knowledge management system in place.

• Simplicity breeds actionability: While many are making AI sound complicated, increasing adoption and finding productive use cases requires breaking the problems down into incredibly simple terms. The core pillars of success are clear: the AI model itself (a commodity), the prompt (important), and the data (critical). By focusing on these fundamentals, organizations can cut through the noise and start seeing real results from their AI initiatives.

Ultimately, those who solve the AI Inaction Problem fastest and most effectively will win. The only question is do we have the intestinal fortitude to do what’s necessary to solve that problem in our organizations?

What questions do you have about AI and the future of work? Comment on this post or send me an email at timhickle@gmail.com. I just may feature your question in a future article.