According to an Accenture report, 75% of C-Suite executives think that their business will go out of business within five years if they fail to scale AI. Automation and AI implementation is no longer an option for businesses. In fact, it has become the need of the day. Artificial intelligence is rapidly changing the way the world does business, and organizations that fail to adopt AI will soon be left behind.

According to Josh Perkins, Chief Technology Officer at AHEAD, “Artificial intelligence is designed to reveal what you can’t see due to the sheer volume of data that is available. If there’s one reason IT leaders should accelerate the broader adoption of artificial intelligence, it’s the ability to uncover opportunities that generate real business value through insights and efficiencies.”

The key to successfully scaling an AI project is identifying which challenges you will face along the way and how to solve them. Remember that no two companies are alike and that no two AI projects are exactly the same, so it’s important for enterprises to pick out solutions that fit their specific needs.

If you are struggling with AI adoption or finding it tough to scale your AI projects then, this article is for you. In this article, you will learn about seven ways to accelerate AI adoption and scale AI projects successfully.

7 Ways To Speed Up AI Adoption and Scale AI Projects Efficiently

Here is how you can accelerate AI adoption and scale AI projects correctly.

1. Start With Best Use Case

One of the main reasons why AI projects fail is because project managers tend to bite off more than they can chew. Most machine learning and AI initiatives work well when they are created with specific use cases in mind. The mistake most project managers make is they don’t know where to start. In order to drive your AI projects to successful completion, you need to first find the best use case and partner with business leaders. They will also have to engage a broader ecosystem to get valuable insights, technology and talent. Set clear goals and milestones to keep your team focused otherwise, your AI projects can easily get derailed from the path.

2. Create a Playbook

There is no denying the fact that developing a team is important for the success of your AI project but it should not come at the cost of laying down a process for your team members to follow. That is the same mistake most project managers make when managing AI projects. Once you have a team, you need to provide them with the right training, create an AI strategy and establish internal and external customer communication channels. In addition to this, you also need to understand which external partners are critical for your project’s success and how you can take them onboard.

3. Adopt Multi-Pronged Strategy for Skill Development

Completing AI projects or scaling them is not easy. You need data experts, security analysts, process automation professionals, human computer interaction designers as well as robotics and machine learning engineers in your team. Finding individuals with these skills is not easy. Even if you manage to find them, they will cost you a lot and if you don’t have the right retention plans, you will struggle to retain them. Since AI-based algorithms are resource-intensive there is a need to use a dedicated server.

4. Prioritize Data Delivery

AI and machine learning models are as good as the quality of data you feed them. If you train your machine learning models with poor quality data, it will become biased and deliver inaccurate results. On the flip side, if you feed AI and machine learning models with high-quality data, these models will work perfectly. That is why it is important to invest resources in data collection, transformation, cleaning, and normalization. Once the data you feed don’t have inconsistencies and issues, your machine learning and AI-based models will work flawlessly and deliver desired results. You also need to take into consideration the way your AI activities are impacting other existing business processes.

5. Amplify Your Data Sources

Let’s say, you have improved the quality of data you are feeding to AI-based algorithms. Is that enough? No. In fact, you will have to extend the number of data sources and collect different types of data. The more diverse your data sources are, the more depth your AI-based algorithms will have and the better they will perform.

The more mature your AI algorithms are, the closer your enterprise will go towards digital maturity. Make sure you evaluate the authenticity and accuracy of each data source before feeding its data to your AI-based models. Since most data is unstructured, you will first have to organize it so you can extract useful insights from that dataset.

6. Keep An Eye On Cultural Shift

Not every business can afford to hire the best AI subject matter experts or have them in their own team. The good news is that they can overcome this issue by data democratization, which has become the hottest trend in artificial intelligence right now.

This not only allows you to take your business to new heights but also makes artificial intelligence more accessible for the masses. The direct consequence of the latter is anyone can take advantage of AI benefits irrespective of whether you are an assembly line worker or working as a sales agent in the sales department. Due to this, the real benefits of AI trickles down lower in the organization hierarchy.

7. Conduct Performance Reviews

Just like your employees, it is important to conduct performance-based reviews for your AI-based models. This will give you a clear picture whether your AI models are actually working or not. Even if they are working, what is their effectiveness? More importantly, it will provide you with opportunities to make future improvements to your artificial intelligence algorithms. Make sure it is not a one-off activity and make it a periodic affair to get the best results out of your AI systems.

How do you advance your AI adoption and scale AI Projects Company? Share it with us in the comments section below.