4 Problems When You Don't Prepare for An AI Project

If you’re reading this, you’re probably already exploring how AI can benefit your company. Should you automate your warehouse? Can AI help your financial department? The excitement of AI's potential can lead to hasty implementation, in places where it might not be the answer, and that’s where things go wrong.

Without a solid plan, AI projects can quickly become costly failures, leading to wasted resources, frustration, and skepticism about AI's value.

At ERP Suites, we are proud to be on the cutting-edge of utilizing this technology in tandem with JD Edwards. We’ve partnered with satisfied customers for their own AI projects. We’ve created a digital assistant to help across the business spectrum. We continue to innovate in AI and would love to know what project you’re facing.

In this blog, we’ll help you identify four pitfalls you could encounter without properly planning your AI project, and how-tos for overcoming them.

Being Prepared for Your AI Project Increases Your Chances of Success

Just like with any new project you’re putting into action at work, you need to be prepared. An AI project requires a lot of details to make it happen. Maybe you want to start using a digital assistant for better financial forecasting. Is your accounting department on board? Or maybe you want to automate your warehouse to optimize operations. What machines and tools would you need?

Whatever your AI project is, your vision needs to be clear.

We see these four major problems when businesses don’t prepare for AI properly:

1. Lack of stakeholder buy-in
2. Unclear objectives and outcomes
3. Inadequate resource planning
4. Poor data infrastructure and quality

Let’s dive into each of these problems and how you can sidestep them.

1. Is Your Team Aligned on Your AI Project?

Who’s on board for your AI project? You’ve probably got at least one enthusiastic team member. But you also need:

•         The CEO, CIO, and CISO (Chief Information Security Officer)
•         The VPs of Marketing and Operations and Finance
•         Staff who will execute the project on the ground level

If leadership and upper management aren’t aligned on the AI project, there’s no trickledown to the rest of the staff. Plus, establishing a budget and resources gets tricky. Stakeholders need to understand how AI will benefit the business and what the ROI will be.

How to Gain Stakeholder Buy-In and Avoid Resistance

1. Educate Your Stakeholders: Host workshops or presentations to explain AI concepts and potential use cases.
2. Show ROI Potential: Give examples of how AI can improve efficiency, cut costs, or increase revenue.
3. Assign an AI Sponsor: Designate a C-Suite member to champion the project and keep the momentum going.

Getting the whole team on board will move your AI project move through the pipeline, from budget approval to resource allocation.

2. Does Your Team Know How Using AI Will Lead to Better Outcomes?

If you begin an AI project without clear objectives, you could end up with nothing to show for it. What specific business problems are you solving? What outcomes are you aiming for?

Unclear objectives can lead to:

• Scope Creep: The project keeps expanding without meaningful results.
• Wasted Resources: Time and money spent without addressing real problems.
• Frustration and Skepticism: Teams lose confidence in AI if they can’t see tangible benefits.

To avoid these pitfalls, set SMART goals: Specific, Measurable, Achievable, Relevant, and Time-bound.

Let’s say your company manufactures notebooks, and you’re currently experiencing low sales. Your current goal is: We want to use AI for marketing. But that’s just too generic. Instead, you need to identify:

• What marketing strategy will help gain customers?  
• What are your benchmarks for improvement?
• Can your goal be met with AI specifically?
• Why is AI the right tool?
• What’s your timeline?

Now you have this objective: “We want to use AI to reduce customer churn by 15% within 12 months by analyzing customer behavior patterns." See the difference?

Clear goals keep your project focused and ensure everyone understands what success looks like.

3. Do You Have The Time, Talent, and Budget to Get Your AI Project Off the Ground? 

AI projects require three key resources: time, talent and budget. Find yourself missing any one of these components and you’re facing an incomplete, frustrating project that drags on. Because implementing AI is not a quick fix. It’s a strategic initiative that requires time to execute.

You’ll need a robust team to bring your AI project to life. Many companies underestimate the need for specialized talent. Data engineers keep data pipelines reliable. Data scientists and AI specialists build and refine AI models. Cloud experts maintain servers and cloud systems.

None of this comes cheap. Do you have a realistic AI project budget? Is your current technology optimized for AI compatibility? If you’re missing even one component, your AI project is going to fail to deliver results.

4. Solid Foundational Data Infrastructure Will Give You a Quality AI Project

AI models are only as good as the data they’re trained on. Bad data leads to unreliable results, undermining trust in AI. But it might be hard connect the data in your legacy systems with AI technology.

Issues include:

• Data Silos: Information locked in separate departments.
• Inconsistent Formats: Data that needs cleaning before use.
• Latency Issues: Slow data transfer affecting real-time processing.

To avoid these issues, start with data cleaning. Accurate and up-to-date data is more useful for everyone. Take a look at your infrastructure. Do you need system upgrades to access AI features? Can your ERP system or operating system handle complex AI workloads? Data security is essential – you want to have strong encryption to remain in compliance.

Good data helps your AI project and your overall business be successful.

Prepare Your AI Project Carefully to See Success

You can make your AI project a success with the right preparation. Make sure your team is all aboard. Have a clear objective in mind and clean data on hand. Get all the necessary resources to execute. By preparing for AI today, you’re set up for long-term success.

Ready to map out how you can get your AI project off the ground? Check out our AI starter guide.