Why Most Enterprise AI Projects Don’t Make It Past Year One

Organizations invest a lot of money into AI projects from a place of excitement. Leaders get together for kickoff meetings, vendors perform exciting pitches, and there is a lot of buzz about what’s to come. But then the tide changes. The follow-through becomes lackluster, not enough people adopt it, and by the end of twelve months, it’s fallen off the strategic plan. It’s a sad reality that occurs across industries more often than it should.

The Numbers Aren’t In Anyone’s Favor

The statistics surrounding ai implementation aren’t great. The enterprise ai failure rate is discouragingly high before it even gets to the full implementation stage. Many ai projects fail to ever meet their intended business outcomes and this includes large scale projects with financial and executive sponsorship. This isn’t to say that the technology doesn’t work, more often than not, it works like a dream in sandbox environments, but instead, comes up against the realities of what it means to actually function in the business world.

When the Demo Doesn’t Mirror Reality

Demos show ai handling predictive requests with ease at every level of an organization. Sales teams can make projections; customer service can receive responses; operations can find efficiencies. Demos operate in closed environments where data is clean and use cases are straightforward. The reality in which businesses operate is much messier. Data exists in silos; processes differ from department to department, even from employee to employee; edge cases exist all the time in which the ai that could handle the demo situation cannot provide satisfying output.

The gap between demo handling and real-life operation blindsides many teams who expect the technology to work the same for them as it did for their operation’s sales team (for example) without ever needing extra help along the way. When results inevitably fall short, teams get frustrated quickly.

No One Considers the Integration Challenge

Here’s the problem, most organizations already have numerous software applications and established processes in place. Now they need to integrate ai into the mix or change processes in which ai can be integrated. This technical challenge falls on the IT teams to determine how to integrate, but bigger than that, is the process integration challenge. People have been empowered to do things a certain way for so long that now they’re being told there’s a better way, and ai is here to help.

Some buy in quickly; some see their work doubled unnecessarily with things they’re already doing well by themselves, and it takes longer than anticipated with scheduled calibrations to bring people around, or at least all on the same page. Calibrations work when people are doing the work, not just attending trainings with little practical experience in the moment. So, over time, while leadership observes from afar, the technology they’ve implemented, and equipped the teams to use, renders no tangible results.

The Data Quality Kick To The Guts

Ai needs data to create good output. That’s simple enough until organizations look at their data situation. Outdated formats, discrepancies across departments, or lack of sufficient amounts render organizations on a massive journey, and pivot, just to get their data ready for ai use. This journey requires time and investment that’s not always factored into the original scope of implementation (if at all).

Organizations realize it’s going to cost more and take longer to get their data ready for ai than implement ai itself, and they realize this several months in when they’ve already invested time, and people’s good faith, into the project without meaningful progress.

When Executive Sponsorship Fades

Executive sponsorship is strong in the beginning stages as big wig leaders attend meetings, approve budgets and communicate meaning about the effort at hand. But leaders have a lot of competing priorities on their plates. If something goes awry or exceeds projections (which is common), that strong support weakens as other pressing needs emerge, sidelining the ai project, thus deprioritizing it from strategic discussion.

Without consistent leadership support, projects flounder for resources, momentum and organizational significance. Teams dedicated to implementation feel the crumbling of effort and wonder if their efforts are in vain.

What It’s Really Going To Take To Succeed

Projects that survive year one have certain qualities that allow them to excel. They don’t try to do everything right away, they scope realistically; they invest time and money getting data prepared instead of assuming all will be fine and dandy; they establish feedback loops to communicate shortcomings only to see improvements, and most importantly, they maintain consistent leadership dedication even when progress is slower than expected.

Time from purchase to business value is longer than planned. Organizations that realize this beforehand, and allow for this surge, have much better success levels getting to the other side. The technology works when it’s implemented properly with adequate support; it’s getting there that takes longer than expected because people assume too much based on idealized demos instead of realistic expectations.