Every day brings new headlines about AI. Some shout about the endless potential, how fast AI is moving, and the achievement of new horizons every day. Others, such as the recent MIT AI report, “The GenA Divide: State of AI in Business 2025”, land with a thud for noting that 95% of AI pilots are failing to deliver a financial ROI.
One can be forgiven for feeling the whiplash between hype and skepticism. The truth, as frequently occurs, sits between the extremes, and there are tried-and-true ways to unlock value.
I want to give you my insights into the obstacles companies are facing when adopting AI, and point you to the recent MarketingProfs Friday Forum webinar that Wrike sponsored, which will give you even more understanding of how Wrike is helping companies improve ROI with Wrike AI.
Critical stumbling blocks that hamper expansive AI adoption
The 95% failure rate in the study is sensational, but there are a lot of truths buried in the study. In reality, deploying AI is no different than any other transformation. Organizations still need to follow business transformation best practices by first strengthening the foundation of their knowledge, data, and workflows to unlock full value.
The details in the study highlight several critical steps that business transformation leaders know need to be done well:
- Set a north star.
- Pick an important problem.
- Focus, learn, and then scale.
- Prove returns and share broadly.
When these steps aren’t taken methodically, with careful measurement of results, they’ll stall at the starting line. Effective transformation requires a willingness to invest in the underlying infrastructure that makes AI adoption possible, as well as planning before the transformation and measurement afterward. Without this groundwork, even the most promising AI initiatives can quickly lose momentum and fail to deliver meaningful impact.
Data gaps that undermine AI initiatives
The MIT study highlighted the core issue for 95% of the companies in their dataset, where AI simply isn’t delivering: the “learning gap” for both tools and organizations. “MIT’s research points to flawed enterprise integration,” reported Fortune. Aditya Challapally, the lead author of the report, noted: “Generic tools like ChatGPT excel for individuals because of their flexibility, but they stall in enterprise use since they don’t learn from or adapt to workflows.”
We have known this for over a decade, given our investments in ML/AI: AI is only as useful as the data it can use. That’s why we’ve been innovating fast to ensure our customers — and our own teams here at Wrike — can take advantage of internal data to drive ROI.
Our recent survey on the current state of AI revealed that many teams are struggling with disorganized knowledge, fragmented systems, and a lack of ownership over documentation and data management. For instance, while 74% of employees say their company treats knowledge and data as valuable assets, only a fraction are managing them in a way that supports AI readiness. Employees go so far as to say their organizations have lost critical information over the last year due to preventable issues, with the top culprit being information scattered across siloed tools and platforms.
Wrike puts your data at your fingertips
We’ve long known that data and context are the keys to helping organizations and users really see the benefits of AI. So here’s what we’re doing at Wrike to help our customers and our own teams put critical internal data to good use and drive ROI.
We’re actively enabling easier access to information, standardized documentation, and consistent capture of project details — all of which are critical to powering more effective AI-driven workflows. We’re using our suite of products to help customers strengthen documentation, data consolidation, and workflows to give AI agents the context and structure they need to drive meaningful results.
Two of our Wrike marketing team members, Nancy Boas and Michael Mares, went into great detail at the recent MarketingProfs Friday Forum, and our CMO Christine Royston gave an excellent overview of the event in her blog post. Nancy and Michael highlighted all of the Wrike tools that are helping our customers move from entry-level AI features to more sophisticated use cases.
| AI tool | Benefit |
| Conversational AI for querying and acting on internal tasks, projects, and spaces | |
| Klaxoon AI-powered visual collaboration tool | Logically clusters ideas and turns whiteboard ideas into executable projects |
| MCP Server | Connects Wrike to external data and AI assistants for unified insights |
| AI highlights | Surface the most important insights, key trends, risks, and recommendations from dashboards |
| Generative AI | Generate briefs and plans, translate or adjust content tone, summarize comment threads |
| AI-powered actions (in Labs) | Natural language-based creation of request forms, widgets, and dashboards, onboarding assistance, and inbox cleanup |
Here at Wrike, we’ve laid the right foundation for our teams to use and scale AI, and we’re the perfect test use cases for our own products and following the transformation steps:
We set a north star target for all of our internal teams: Sponsor AI-driven projects, deliver real returns, and put Wrike in the middle as a source of context and insights.
Our marketing team jumped on the challenge with an initial goal of reclaiming 10-15% of their time using AI.
Across our marketing team, our designers are now using AI to handle tagging, resizing, and formatting so team members can stay focused on bigger-picture design tasks. We’re using Wrike Copilot and an internal knowledge base portal to save considerable time we would have previously wasted looking for case study information, task details, or other data. Our SEO is optimized using AI assistants, and we use chatbots for funnel conversions. We use real-time, AI-powered dashboards to highlight at-risk or blocked projects, and our AI-augmented workflows are helping team members reclaim time. These initiatives rely on bringing together performance data along with workflow information in Wrike to both save time and generate richer insights.
The end result has been that the team has far exceeded their initial ROI targets and is still pushing for more. I am using this as an opportunity to share broadly and challenge teams to uncover the next promising use case.
Adopting AI successfully isn’t about chasing the latest trends or engaging AI for AI’s sake; it’s about laying a solid foundation, being intentional with your goals, and ensuring your data and workflows are truly ready for transformation.
While the challenges highlighted in the MIT report are certainly real, they’re not insurmountable. We’ve seen it in action with our teams here at Wrike, with customers like Jellyfish, and we’ve heard anecdote after anecdote from our customers, reinforcing the lessons that AI adoption requires careful knowledge management, integration, and thoughtful measurement.
As AI continues to evolve, the organizations that thrive will be those that treat it as a long-term journey, not a quick fix. With the right approach, teams can unlock new efficiencies and ultimately deliver better outcomes for their business and customers. And with the right work management platform, teams can gain deeper context.
If you want to explore these strategies more deeply and see practical examples in action, I encourage you to watch the Wrike session at the MarketingProfs webinar.
