Why Every AI Project Should Begin and End with ROI
In the modern business landscape, artificial intelligence (AI) is everywhere. From marketing and sales to customer support and software engineering, enterprises in every industry are developing and launching AI transformations at an unprecedented pace. But just because these transformations exist doesn’t mean they’re working.
Take the most recent report from the MIT Media Lab’s Networked Agents and Decentralized AI (NANDA) project. In it, a stark truth was revealed: 95% of enterprise AI projects stall before ever showing results.
Shibumi works with high-performing enterprises all over the globe that have successfully integrated AI into their operations, so we know that the problem isn’t the technology. It’s the way businesses adopt, integrate, and manage it.
For leaders with big aspirations, this statistic is a wake-up call. And for those who want to do something about it, we have a roadmap.
Reality Check: Why Most AI Transformations Fail
Only 5% of AI pilots achieved rapid revenue acceleration, according to MIT. AI isn’t plug-and-play; when confronted with an organization’s unique context and culture, AI projects often underperform.
There are several reasons for this. One is the tendency of leaders to jump on the AI bandwagon in a FOMO-driven, short-term impulse to stay ahead of competitors instead of taking the thoughtful approach that true AI transformation requires.
During the rush to transform, the idea of ROI often gets lost. But without ROI guiding the development and integration of AI into the business, transformations can easily get off track and fall short.
ROI can also be difficult to track. People need tools to support the development, adoption, and ongoing improvement and measurement of AI pilots. This includes data collaboration platforms and those that synthesize and contextualize the ROI of AI projects.
Why ROI Should Lead Every AI Transformation
AI pilots in business must lead with ROI because experimenting for the sake of innovation is no longer sustainable. AI projects are often costly to deploy, and when returns are unclear, projects are unlikely to see high adoption rates. In the end, they are abandoned.
When AI projects aren’t aligned with business outcomes, it is difficult to justify ongoing budgets and scale adoption. Repeat this cycle of boom and bust too many times, and people in the workplace can become fatigued and lose faith in the possibility of AI to improve operations altogether.
Your Unique Business Context: Where ROI Begins
The MIT report highlights some common leadership traps, such as buying into hype instead of taking the time to consider the unique business context and investing in areas that are highly visible rather than those that can deliver the strongest outcomes.
AI ambitions are often at odds with organizational realities. Leaders who develop AI with a considered, structured context can solve real operational bottlenecks. When AI pilots are developed thoughtfully and not in the spirit of FOMO, impact follows.
A structured, localized approach that includes the following steps can help guide the way to ROI:
- Pinpoint Real Pain Points: This is your why. Look closely at how your team operates and identify bottlenecks in daily operations that, if solved, could accelerate strategic goals.
- Audit Data: Identify internal data sets that can fuel the AI transformation and ensure it is clean and organized.
- Operate at the Right Tech Level: You don’t need to build highly custom AI models—but you might. Choose the tech that is right for your business.
- Prioritize the Human Element: Employee adoption is a requirement for ROI. Ensure you provide the right communications and training to your teams to ensure they feel empowered to use AI.
Measuring ROI
A recent IBM report showed that when it comes to AI, many executives are investing heavily—but only 29% say that they can measure its ROI confidently. Simultaneously, CEOs are forced to balance pressure for short-term ROI with longer-term innovation goals, which requires making difficult strategic decisions on where to put energy and budget.
In order to effectively measure ROI, leaders must:
- Establish a baseline before implementation
- Calculate total cost of ownership
- Identify and track actionable outcome metrics
- Include the human impact, including employee experience
- Track all of these in dynamic, easy-to-understand dashboards
- Reassess their projects regularly
Join the Top 5 Percentile with Shibumi
Shift the focus from simple technical delivery to measurable business value by integrating a solution that aligns AI transformations to long-term business strategy. Instead of just tracking software usage, Shibumi measures ROI and connects every AI project to overarching strategic goals.
From value modeling and metric tracking to idea intake and dynamic dashboards that provide visibility into ROI at every point in the lifecycle, Shibumi was designed to drive outcomes from AI investments. And we do it at every step; our solution features a workflow to evaluate proposals for viability and project their business impact at different time intervals. And when transformations underperform, we flag them and contextualize the metrics that are lagging. With this feature, the decision to sunset a project is never fraught.
In fact, our solution is all about making ROI from your transformations easy to obtain. With seamless metric tracking and visualizations, embedding AI into long-term strategy isn’t aspirational—it’s all right there, at your fingertips.
Shibumi embeds AI into long-term strategy, connects it to enterprise priorities, and rigorously tracks ROI. And that is what separates businesses that have simply bought into the hype from those that have truly enabled a competitive advantage through AI.
Unlock AI ROI once and for all with Shibumi. To get started, book a free, no obligation demo with one of our expert consultants here.