Like the old adage goes, time waits for no man (and no company, for that matter). Our analysis shows that if you haven’t fully embraced AI by 2024, it’ll be too late to catch up.
That’s because tech adoption usually has a 12-year window of opportunity. When some enterprises began exploring deep learning back in 2012, they started the clock for everyone else.
A looming deadline
If you look at the graph embedded below, you can see that those who wait to put AI into production miss out on cornering the market. Here the blue line shows the adoption of new technology while the yellow line shows market share.
In analyzing developments like these over the past couple of years, we’ve actually begun to see a Peloton effect. And this gap between the haves and have-nots is getting almost too wide to surmount, so now’s the time to take action (if you haven’t already).
Companies who were able to get in early have been doubling down on AI re-investments while over half of enterprises are still years away from the digital transformations necessary to be competitive.
The positive impact of embracing AI now
Companies solely focused on digital transformation have just a 5% chance of achieving significant benefits from AI. Those that also have a clearly articulated AI strategy increase their odds to 21%.
This increases to 39% with the broad application of AI and 73% when change management occurs. Businesses that make these kinds of adjustments to their processes are six times more likely to obtain a substantial financial advantage compared to those that do nothing.
The challenges around AI adoption
To those who aren’t yet using AI, the biggest blocker is when the company doesn’t recognize the need for it. Difficulties identifying appropriate business use cases and sorting out technical infrastructure are the other big hindrances.
Those at the evaluation stage also have their struggles. Chief among them are a lack of data or data-quality issues. Other problem areas include a dearth of skilled people, difficulty hiring critical roles, and legal and compliance concerns.
Those deeper into AI maturity have fewer issues to contend with, but they’re still there. Most are dealing with workflow reproducibility and inefficiencies around the tuning of hyperparameters.
The organizational barriers most face
Regardless of where they are in the adoption cycle, many companies struggle with AI. Here’s what we’ve found most often holds execs and managers back:
The processes & procedures you’ll need to reset
In addition to these operational challenges, cultural changes will also have to occur. Chief among them include an open discussion about your company’s AI strategy, interdisciplinary collaboration, greater transparency about AI’s impact on individual roles, and abandoning top-down decision making in favor of empowering front-line managers.
According to a recent study conducted by McKinsey and Harvard Business Review, only 8% of companies follow the above practices to enable widespread adoption. Many organizations are not AI-ready today, but should begin laying the groundwork to avoid critical blockers when the need becomes urgent.
Ready to get started? Plan to attend our free webinar Why You Need To Evel Knievel The A.I. Gap By 2024. It’s happening on March 3rd at 11am CST and you can reserve your spot here.
OUR WEBINAR PANEL
Steve Meier, Co-founder & Head of Growth, KUNGFU.AI
Steve has spent 15 years working in business development, marketing, product management, and creative strategy roles serving Fortune 500 technology companies. Steve's passion is applying emerging technologies creatively to find simple solutions to big problems. He’s worked closely with IBM Watson, leveraging Watson APIs to create innovative sales, marketing, and event solutions.
Nancy is committed to strengthening the organizational capacities and societal structures necessary to leverage significant "Fourth Age" technology advances. Nancy guides visionary C-suite leaders through our rapidly transforming society. Nancy will soon be launching her first book, “LeaderING: The Way Visionary Leaders Play Bigger.”
Paco has over 40 years of tech industry experience, ranging from Bell Labs to early-stage startups. Paco’s core expertise is in data science, natural language and cloud computing. He serves as an advisor for Amplify Partners, IBM Data Science Community, Recognai and Primer. In 2015, Paco was cited as one of the Top 30 People in Big Data and Analytics by Innovation Enterprise.