How long should your AI transformation take? Longer than you think.

For at least the past two decades, enterprises have been doggedly pursuing digital transformation: automating manual processes, digitizing physical records, and rejiggering workflows to take advantage of the productivity boosts technology can bring. 

Now, many of them are looking to take the same approach with AI. But AI transformation is different, because the technology is radically different: You’re not going to ask your ERP platform to draft a press release or vibe-code a proof of concept. Your HR software isn’t popping up on your desktop, asking how your day is going and what it can do to help. The foundations of your CRM application aren’t changing every month or two.

With generative AI technology in the hands of hundreds of millions of users, expectations for what this technology can do, and how quickly it can do it, are off the charts—and often out of touch with reality. 

“Some people act like generative AI is like a genie that can grant them three wishes,” says Varun Saket Valiveti, product manager and business analyst at AI consultancy Kanerika. “‘Hey, I need these three things done, and I need them done in a week.’”

News flash: It’s going to take longer than a week. And if you’re looking for reasons why 95% of AI projects have so far failed to deliver value, unrealistic expectations and over-amped ambitions are good places to start.

“The technology itself isn’t the problem, but the pace and immediacy of its advancement are creating an overstimulated environment where leaders feel pressure to jump in,” says Venkat Venkataraman, vice president of Product (AI Platform & Strategy) at Freshworks. “Even when the business goals and outcomes are clear, many companies underestimate the complexity of deploying AI at scale and the necessity of integrations, behavior change, training, workflow, and process change, among other things.”

As a result, AI transformation timelines will vary widely, depending on the scope of your project and the maturity of your organization. Tightly focused AI initiatives with clearly defined objectives have the best chances for success. But if your data isn’t AI-ready, your change management processes aren’t well developed, or your executive leadership doesn’t have a good handle on what they’re hoping to achieve, your AI transformation efforts will stall or fail entirely.

It’s the data, stupid

The success or failure of an AI project is heavily dependent on the quality of the data you’re feeding it, says Gaurav Verma, chief marketing officer for Kanerika, which helps midsize and large enterprises assess their AI maturity and begin implementing solutions.

“Most companies don’t have the right data infrastructure in place for digital transformation, let alone for AI,” he adds.

According to Hubspot’s 2025 AI Data Readiness Report, less than a third of enterprises say their data is even moderately ready to be used with AI, and less than 10% say it is fully ready. Gartner predicts that 6 out of 10 AI initiatives will be abandoned because their data isn’t up to the task. Verma estimates that only 1 out of 5 companies are ready for AI when Kanerika first engages with them.

Read also: The strategic vs. tactical divide with AI

Data was top of mind even before Justin Levitz began transforming iPostal1’s support operations using Freshworks’ AI-powered platform.

“In order to do AI correctly, you have to institutionalize and centralize knowledge,” says Levitz, who is process technology leader for the virtual mailbox company. “The quality of your knowledge will determine the quality of your bots.”

His first move was to promote a member of his team to the newly created post of knowledge manager, and begin building connections between the Freddy AI chatbots and iPostal1’s internal knowledge bases. Now when information is added or updated, the support bots can access it immediately. 

The groundwork of building a solid data foundation paid off. The iPostal1 support bots now handle more than 360,000 conversations each year and resolve more than half of customer queries without the need for human intervention. 

Transformation is a skill, not an event

Transformation can take many forms, says Matthew Grant, a communications specialist for SAP LeanIX, which helps enterprises transform their technology architectures. It might be implementing a new system that digitizes formerly manual processes. It might entail moving mission-critical infrastructure from on premises to the cloud. Transformation might be driven by acquiring a new company, or being acquired. And of course, it may involve trying to redefine your company around a revolutionary new technology that is constantly changing.

But while the specifics of each transformation may vary, organizations that successfully navigate these changes have one thing in common: They approach transformation as an ongoing capability instead of an individual process. 

“Transformation is not a project you do occasionally and then you’re done,” Grant says. “It’s a constant. And that means you have to be constantly inventorying your application landscape, mapping your processes, and enabling your staff.”

Before you begin to automate workflows with AI, you need to understand what you’re trying to automate. If you don’t have a handle on all the software you’re currently using, haven’t fully documented your processes, and have not prepared your workforce for the changes AI will bring, you haven’t done the necessary groundwork.

“Is your house in order enough that you can hand it over to an alien intelligence that understands what you want to have happen?” Grant asks. “If you don’t put in that work, AI is not going to deliver the results you’re looking for.”

And that’s just the first step. For AI to be truly transformative, organizations will need to re-imagine traditional roles and workflows, says Robyn Agoston, a future of work strategist at Human Fluency AI and former global head of transformational change at Novartis.

“AI has the power to fundamentally change how we work,” she says. “If you’re looking solely at the technology, you’re missing half of the equation. What’s your operating model? How are you redesigning jobs? I don’t see organizations taking a holistic, systems-wide approach to AI, which is why they’re not seeing the value.” 

Incremental innovation is key

Many AI projects fail to deliver because organizations are trying to do too much too soon, says Agoston. Executives feel pressure to implement AI now, without establishing clear objectives or well-defined metrics for measuring success.

“There’s an old proverb that says you need to eat the elephant one bite at a time,” she adds. “What’s happening with AI strategies now is that they’re trying to serve the whole elephant, and then wondering why nobody can digest it.”

AI transformation should be approached in three phases, advises Kanerika’s Valiveti. The first phase is as a simple productivity enhancer; for example, IT support staff querying a chatbot on how to restore systems after a network outage. Then it becomes intelligent—AI-powered monitoring detects an increase in complaints that the HR portal is unavailable. Eventually, the AI becomes autonomous: Agents diagnose the outage, restore the systems, and let IT staff know what they’ve done.

But before you can get to the autonomous phase, you first have to go through productivity and intelligence. You can’t skip the line and go straight to agents.

“AI is really going to hit when you pick a very specific workflow and make that more efficient,” says Verma. “You need incremental innovation. And then you can combine those incremental innovations into a much bigger agentic AI solution.”

Transformation never rests

Taking an incremental approach also means the process will never truly end. There will be no ribbon-cutting ceremonies or popping of champagne corks announcing that your AI transformation is finally complete. 

Organizations hungry for quick returns on their AI investments need to take the long view, advises Andrew Blake, chief data officer for Opening Bell Ventures, a consultancy focused on business transformation.

“People don’t talk enough about the long-term implications of AI, how it’s going to fundamentally change our relationship with work,” he says. “If I were leading a corporation undergoing AI transformation, I’d focus less on my six-month or one-year returns, and more on what I’d want my company to look like in five years from a staffing, product, or process point of view. That’s when AI will become a much more profound and disruptive thing.”

You’ll know AI will have transformed your work when you can no longer envision getting work done without it, says Agoston. 

“Can you imagine doing your job without email or the internet?” she asks. “AI will get there, but only if you’re intentional about using it in the right way.”