An illustration of 3 employees working on their computers and utilizing enterprise ai

Enterprise AI Best Practices

Throughout the world, C-level executives are feeling the buzz of AI. As a result, investment is improving and it’s even coming from companies that do not work in the tech world. Not to mention, the AI success stories are being shared as they are quite diverse in practice from Amazon’s AI-powered robots to using AI for predictive maintenance to reducing food spoilage worldwide.

The intelligence capabilities are changing the way business is done. Perhaps it’s not at breakneck speed yet, but it will soon be. In terms of data-driven decision making, AI is at the forefront whether it is dealing with finance or farming.

Soon enough, even customer service will be fully automated. These developments are all taking place because the support needed to facilitate AI such as the processing power, data storage, and development platforms are both becoming faster and more affordable. The time to capitalize on AI is now.

For industries that have yet to experience AI or ML, there is a challenge around some vendors offering generic tools that miss the mark in terms of providing an all-in-one and effective solution.

On the other hand, some companies want to use hand-picked opportunities and specific use cases to test the AI waters. With this approach, you will never really get the complete AI experience, even if you do gain a few valuable insights.

Instead of becoming a market leader, your company may become one of the many followers. But if you want to become an AI-supported business, then you need to change your mindset around how humans and machines currently interact.

It also helps to start implementing cognitive tools such as ML throughout every fundamental business process to ensure data-driven decision-making is supported at every level and with all core processes. In return, AI could drive new and improved business models. Of course, these aren’t steps to be taken lightly. Yet, to thrive in the increasingly-digitized world, these are the types of steps organizations should take.

Massive advancements are taking place in the fields of natural language processing, ML, deep learning, and computer vision, which makes it much more feasible to incorporate AI algorithms into your systems right now.

Get digitized.

In terms of AI adoption, it is the digitally transformed organizations who are in the lead. Companies in tech, telecom, and the auto industry have already taken the leaps. In other sectors, those who are the most digitized are already using cloud systems and more are often more receptive to AI technologies. Companies who have yet to complete their digital transformation appear to have a more difficult time transitioning to AI.

Pick the problems you want to resolve.

Now is the time to start exploring. Think of this as the AI discovery process. Some questions to consider:

  • How would AI help some of your existing processes around your products and services? What are a few use cases which could benefit from the helping hand of AI?
  • How can AI demonstrate value for your business?
  • Which types of activities would benefit from both automation and intelligence?

Discover how Enterprise AI can augment your business.

Get buy-in from your executives.

Without your leadership on board, then there won’t be an effective AI adoption. Companies that have had success with AI implementation have also had the full support of their C suite. It shouldn’t just come from the CEO and IT department—it needs the thumbs up from every executive at your company and, if you have one, your board of directors.

Consider the language barriers.

AI and ML vendors seem to speak a foreign language. To ensure minimal disruptions in the process, good communication is critical. It’s also essential for managing ROI expectations, keeping short and long-term objectives in check. At the initial discussion phase, convey the problems you are trying to solve and learn more about the experience your selected vendor/solution has had in solving similar issues.

Next, you’ll want to create an internal AI team to develop a strategy for how AI can best serve their respective departments’ needs. To take this a bit further, have representation from all of your organization’s departments, such as IT, web design, marketing, sales, accounting, customer service, and more. They will be the employees who can benefit and utilize AI once it is deployed.

Illustration depicting language barriers

Make sure everyone receives training.

Every person in the organization must receive on-the-job training and education, whether it’s virtual, in-person, via workshops, or even site visits to your vendor’s location and/or peers who have implemented the same solution. To leverage all of the features and benefits, every employee needs to learn how to apply the new AI solution.

Make businesses accountable.

Who will own the results of the implementation? For example, your analytics team? While this has been the case for many organizations, you’ll want business units to take the lead and be accountable for success.

This unit should also take responsibility for guiding the deployment from phase one, all the way to completion. One way to keep track is by utilizing a scorecard that has relevant performance metrics to ensure business objectives are aligned with the outcomes.

AI learns quickly, which means its potential to improve organizational decision-making will continue to grow. As with any new technology implementation, there may be difficult decisions and changes that come about affecting the culture, business roles, and workflows.

Thus, the implementation must be thoughtfully guided. Nonetheless, companies that follow the best practices and effectively implement AI will succeed in a world where the collaboration of humans and machines far outpaces anything either humans or machines could do alone.