5 Enterprise AI Best Practices
ai best practices

5 Enterprise AI Best Practices

Artificial intelligence is a massively growing aspect of the technology industry. At conferences and trade shows, artificial intelligence is forming the foundation for new products and services.

Companies integrate products and features to create virtual assistants, and chatbots answer customer questions on a range of tech support issues to product information. Simultaneously, many companies are working to integrate AI as an intelligence layer across their entire tech stack. 

Machine learning, computer vision, and natural language processing have all progressed in recent years, making it simpler than ever to incorporate an AI algorithm layer into your app or cloud platform.

Practical AI applications for companies can take several forms, depending on your organization’s needs and the BI insights obtained from the data you collect. Companies can use AI for various tasks, including mining social data, driving interaction in customer relationship management (CRM), and optimizing logistics and productivity in asset monitoring and management.

Machine learning has and will continue to have a pivotal role in artificial intelligence development. It incubates AI startups and helps companies incorporate AI on top of their existing products and services.

Currently, recent progress in machine learning is driving the interest and use of artificial intelligence. There is no single breakthrough you can point to, but the business value we can extract from ML is now off the charts. What is going on right now regarding enterprise business processes such as planning and control, scheduling, resource allocation, and reporting may be disrupted. We’ve gathered some expert advice to clarify the steps companies should take to incorporate AI into their operations and ensure effective implementation.

Top 5 Enterprise AI Best Practices

1. Get Familiar With AI

Modern AI can do a lot to grow your business, but it requires time to understand AI’s scope and how you can implement it. The TechCode Accelerator offers startups an array of resources through partnerships with Stanford University and AI-focused corporations. Take advantage of the wealth of online information, remote workshops, resources, and courses to gain familiarity with AI’s basic concepts. Doing this can be an easy way to start integrating predictive analytics within your organization.

2. AI Can Solve All of Your Problems Quickly

Once the basics are straightforward, any company’s next step is to begin exploring different ideas. Think about ways you can incorporate AI into your new products and services. More importantly, your business should consider particular use cases in which AI might solve business problems or bring value. 

When working with any organization, we start with an overview of its vital tech programs and issues. We want to show how natural language processing, image recognition, machine learning, and other technologies integrate into such products, typically through a workshop with its management. The details vary from industry to industry. If the company does video monitoring, applying machine learning to the process will add value.

3. Prioritize Concrete Value

You must evaluate the market and financial potential of the numerous AI implementations you’ve found. It is easy to get caught up in “pie in the sky” AI discussions; the researchers emphasized the importance of explicitly linking the initiatives to business value.

To prioritize, build a 2×2 matrix with the dimensions of potential and feasibility. This matrix should allow you to prioritize near-term visibility and determine the company’s financial value. Managers and top-level executives must usually take responsibility and accept these moves.

4. Acknowledge the Internal Capability Gap

There’s a big difference between what you want to do and what you can accomplish in a given time if you have the right organizational skills. Before diving into a full-fledged AI implementation, a manager should know what it can and cannot do in terms of technology and business processes.

The first step in resolving the internal capability gap is identifying what you need to acquire and processes you need to build before you start. Established projects or teams, depending on the company, may assist specific business units in doing so organically.

5. Call-In Experts to Set Up a Pilot Project Easily

It’s time to start developing and integrating the company until it’s ready from an operational and technological perspective. Starting tiny, having project objectives in mind, and, most importantly, being mindful of what you do and don’t know about AI are the most critical factors here. Bringing in outside experts or consultants who know the topic well can be highly beneficial in this situation.

Typically, 2-3 months is a good range for a pilot project. Companies need to build insufficient bandwidth for storage, the graphics processing unit (GPU), and networking to achieve this balance. Security is an oft-overlooked component as well. AI requires broad access to swaths of data to perform the task well. Ensure that you understand the types of data involved and usual security safeguards such as encryption, VPNs, and anti-malware.

In Summary

Bringing external experts and internal team members together and a tighter time frame will keep the team focused on business goals. After completing the pilot, you should decide the longer-term aspects of the project and whether it is valuable enough to make sense for your business. It’s also essential that experts from both sides—the people who know about the company and the people who know about AI—are merged on your pilot project team.

Want to start implementing some of these best practices for artificial intelligence into your enterprise? Contact Byonic.AI today for a demo and get assistance in prioritizing AI-based solutions for greater results. Schedule a call today.