In 2019, seven out of 10 companies reported that they weren’t seeing significant economic returns on their artificial intelligence (AI) investments. Today, 92% of large companies say they are seeing those returns. Plus, 92% plan to increase their AI investments over the coming year. 

This marks a major industry shift, and it’s especially promising for curious business leaders across the country. If you’re considering your own AI implementation, it’s important to know what to expect. Today, we’re sharing six expert tips. 

Selection & Process Improvement Case Study

In helping the client get its project back on track, one of our primary focus areas was decreasing their customization needs by improving their processes to align with the system's best practices.

6 Tips for a Successful AI Implementation

1. Get Input From All Departments

AI tools will affect every department across your enterprise, not just your IT team. That’s why you need everyone’s input as you evaluate solutions and create an AI implementation plan. 

In addition to discussions with C-suite executives, make plans to talk with your area managers, department leaders, and team members on the individual employee level. What pain points do they want to solve with AI, and what efficiencies do they want to gain?

You can also ask for suggestions from your end-users and customers to discover how AI tools can help you better serve them. 

Additionally, you can bring in outside parties, such as tech experts and enterprise software consultants. A well-rounded team is critical to selecting tools that will benefit everyone and driving user adoption across your organization. 

2. Define Business Outcomes and Objectives​

It’s easy to get caught up in the bells and whistles of AI, without considering how those features will help you reach your unique business goals.

Before you start meeting with vendors and discussing solutions, take the time to map out your overarching objectives. Then, discuss specific AI milestones you’ll need to meet to get there.

Instead of focusing on broad, high-level goals, make them granular and specific to each business level, choosing specific tasks that you can associate with measurable goals. This way, you can link AI outputs to actual use cases and link those to one another. As you do so, you’ll be that much closer to achieving your larger initiatives. 

For instance, do you want to improve the timeliness of your customer support by allowing your agents to focus primarily on bigger issues? If so, then you might consider implementing an AI chatbot to address simple FAQs. A milestone for this project would be a checkpoint that measures how accurately the bot can answer questions in a given timeframe. 

Without a basic roadmap in place, you’re more likely to choose the AI solution that sounds the most advanced or matches your competitors. In reality, your use cases are different than your competitors, and you need AI that’s customized to those needs. 

3. Understand Data Fluency

AI tools and technologies enable you to gain actionable insights about your everyday operations.

However, these insights are massive in number and scope. They’re also constantly changing. As such, it can be difficult to understand how to analyze and use them in the most effective way.

According to one report, 80% of executives said their business would lose its competitive advantage if it didn’t start using its data the right way. Additionally, 70% of organizations still see business adoption of big data as an ongoing struggle. 

Before you can reap the benefits of an AI implementation, you need to have the right data strategy in place. This means developing data fluency within your organization, so you have the analytical skills required to interpret a variety of data insights. 

4. Set the Right Budget and Timeline

Many AI projects go off-track because there’s either not enough money set aside to cover them, or they end up taking much longer than anticipated. 

Not only should your budget cover the AI tools and technologies you need, but it should be able to pay for any expertise you require. Many small businesses aren’t equipped with enough IT personnel to complete a project of this scope and will need to hire additional resources. 

Likewise, make sure your timeline is also sufficiently padded. Don’t underestimate how much time you’ll need to perform data management and cleansing before the engineer or analyst builds the AI algorithm. While open-source tools and machine learning software can expedite this process, it can still take longer than you realize. 

As you train your AI models to make predictions and meet business outcomes, the proof-of-concept (PoC) stage can be lengthy, too. You’ll need multiple data sources, tools, technology platforms, and libraries in place to achieve accurate outcomes.

By keeping your budget and timeline realistic, you can work confidently toward each stage without worrying about scarcity. 

5. Integrate Other Business Systems

While AI can be a powerful technology on its own, it becomes even more robust when you combine it with other business systems.

For instance, the benefits of merging AI and ERP are substantial. Users who are already familiar with their company’s ERP system can leverage new AI capabilities to perform advanced data analytics.

As you look for ways to integrate these systems, you may need to reengineer your existing workflows to maximize the use of all technologies. 

6. Don’t Make it a Race

Though they’re getting more hype than ever before, most large-scale AI projects are still in their early piloting phases. As companies around the world continue to experiment with AI and its capabilities, don’t feel pressured to rush into things. 

When you’re ready to scale your project beyond a PoC or lab environment, you’ll need tools that can help you simplify and standardize your AI processes, including:

  • Model building
  • Training
  • Deployment
  • Monitoring 

Machine Learning Operations (MLOps) tools can help you deploy and maintain AI and machine learning models in production. By deploying these, you can operationalize your AI processes and glean real business value from them. 

Your Partner in AI Implementation

The right AI implementation strategy can help you automate manual operations, improve production times, and delight your customers. Yet, as with any enterprise software project, proper planning is key.

Contact our enterprise software consulting team below to learn more about the future of AI and where it could take your business. 

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