AI implementation challenges: Why AI projects fail to deliver ROI
- When AI implementations within a business fail, it’s often because the effort wasn’t focused on solving a specific business problem or creating a defined new opportunity.
- Weak governance, shadow automation, poor data readiness and an unfocused AI strategy can also lead to poor ROI on enterprise-level AI investments.
- To generate ROI from your AI use, focus on implementing AI in a way that serves your overall strategy and goals and consult an AI advisory firm for additional guidance.
Many companies are spending more heavily than ever on AI technology to use inside their businesses. But too often, implementation challenges turn AI investments into expensive failures.
Why do so many AI implementations fail to deliver ROI or produce meaningful business value? Keep reading to find out, plus what to do about it.
Why AI implementation is about business transformation and not just technology
AI project failures often start well before you actually begin implementing an AI tool inside your business. Many wasted AI dollars can be traced back to simply not understanding why you are investing in a particular AI tool, what a successful implementation looks like and who is responsible for leading the charge.
Before committing to implement a new AI solution, consider factors like:
Aligning AI initiatives to business goals
Don’t ever invest in an AI tool (or any other technology) simply because it’s new and getting buzz. This is a huge failure point because it often turns into a solution in search of a problem.
You should only spend money on AI tools that create defined new opportunities or solve specific problems inside your business. Any investment that doesn’t advance your goals or fits into your business strategy is likely to disappoint.
Defining success before implementation begins
If you don’t know where you’re going, it’s difficult to determine whether you’ve arrived. Companies that don’t have a clear understanding of how to measure or assess the success or failure of an AI implementation will be left struggling to figure out whether the work has been worth it.
Why executive sponsorship matters
AI adoption challenges can also arise from a lack of executive leadership. If no one within your leadership team is willing to champion AI projects, it may be difficult to get any sort of organized effort off the ground, and it will also interfere with governance, risk management and broader team acceptance of the technology.
A champion often starts off by testing an AI tool themselves, then spearheads a pilot program to further prove the tool’s value and finally leads the whole organization in implementing the tool at the enterprise level.
The biggest AI implementation challenges that limit ROI
Once your team actually starts implementing AI tools, those implementation efforts can fail for several reasons. Key AI deployment challenges that limit or damage ROI include shadow automation, weak governance, poor data foundation and lack of a cohesive enterprise AI strategy.
Weak AI governance
Establishing effective AI governance is essential to the success of any AI implementation effort. Without strong governance policies, risk can quickly outpace any value that may be gained through haphazard usage, compliance problems, potential cybersecurity and data privacy threats and other challenges.
Shadow automation
The term shadow automation is a poetic way of saying that your team is going to use AI even if you don’t have formal AI policies in place. This happens everywhere and is a huge problem because when team members use AI tools outside of governance, you have no insight into what they’re doing or which tools they’re sharing your data with.
Shadow automation can also represent a regulatory compliance issue, as there’s no guarantee that AI operated outside of policy guidelines is meeting compliance standards. It can also waste time: A team might informally adopt an AI tool, then be forced to abandon it midstream when its larger organization formally chooses to implement a competing AI tool at the enterprise level.
Weak data foundation and poor data readiness
If you don’t have a strong data foundation like a data lake house, your AI efforts will almost certainly fall short. Some of the most innovative AI uses today come from companies feeding AI tools data about not just the daily operations of their businesses, but strategy, history and institutional knowledge to help their whole teams become smarter.
Without a high level of data readiness, you can’t do any of this.
Lack of a cohesive AI strategy
Aligning AI spending with your overall business strategy isn’t just essential at the beginning of an implementation effort, but on an ongoing basis. A strategic focus keeps your AI use directed toward where it makes the most impact, rather than burning through tokens on nonessential tasks or work that doesn’t deliver ROI.
AI is expensive and getting more so — so you want to use it only where it makes the most sense to do so.
How implementation challenges affect business performance
AI implementation failures can cost your business time and money. You can spend heavily on AI only to not see that investment translate into business value — leaving you poorer than when you started and with fewer resources to try again.
A failed or ineffective implementation can also see you start to fall behind your competitors. Many newer companies are being built as AI-native businesses from the ground up, so for established firms looking to integrate AI more deeply into their operations, the clock is ticking. You can’t develop enterprise-level AI maturity overnight, and if you keep falling farther behind, it gets harder to catch up.
Finally, there are major risks around challenges like unsanctioned shadow automation. This is a genuine danger to your business from a compliance, cybersecurity and data privacy standpoint, not just if you’re in a regulated industry but for any organization that values the security of its internal, proprietary and customer data.
How businesses can overcome AI implementation challenges
Taking a thoughtful, strategic approach to when and how you invest in AI can dramatically boost your chances of successfully integrating the technology into your business. Key steps include:
- Focusing on problems and solutions: Your AI efforts should always be aimed at solving specific business problems or pursuing defined opportunities.
- Developing a clear AI strategy: AI implementations should be in service to your overall business strategy and thought of as a part of how you reach your big picture goals.
- Creating an effective governance policy: Good governance gets your team aligned on how you use AI, creates visibility and prevents major risks.
- Developing a strong data foundation: An AI-ready data lake house avoids the garbage-in, garbage-out problem inherent to AI technology and allows you to use AI to understand both your business and your customers or clients more deeply than ever.
- Providing training and managing change: AI adoption is a marathon, not a sprint. Train your team on new AI tools you implement and empower change champions to lead adoption efforts and build excitement among the rest of your workforce.
- Seeking guidance from an AI advisory firm: AI technology is so complex and fast evolving that even experts have a hard time keeping up. To implement a more effective AI strategy, seek guidance from an AI advisory firm that can help you better understand where the technology is going and how it can be best used inside your business.
How Wipfli can help
We advise businesses and organizations on how to successfully implement AI. Let’s talk about your goals and how a strong AI strategy can help you achieve them. Start a conversation.
