AI Implementation Strategy

Artificial intelligence or AI is leading the entire world into a new industrial revolution dubbed Industry 4.0. Advanced machine learning systems and AI solutions now work together to help businesses across many different industries improve efficiency and productivity.

With the use of automation, robots, and similar technologies, businesses can improve production and solve various challenges predictively. AI offers all kinds of benefits, and if you’re thinking about adopting this technology, you have to prepare your existing system to make the transition as seamless as possible. Here are the steps you should take to ensure the best results.

Tune in Your AI Strategy With Adequate People and Business Objectives

Developing an AI solution for your business is an expensive and time-consuming process. You’ll likely need to include multiple investors and stakeholders, as well as members of your IT and marketing departments. Everyone will have to work together to ensure that the AI solution is aligned with the overall business strategy. Moreover, everyone involved will have to understand how it works and what their roles and responsibilities are.

If you fail to cover this step properly, the implementation will likely fail short of expectations. Every AI system needs clear goals to make accurate decisions and guide the learning process in the right direction. Take your time, ensure that everyone involved understands their obligations, and monitor their performance to minimize mistakes.

Data Generation Strategy

You must know by now that AI model accuracy largely depends on the data quality. It needs the right data to propose solutions to real problems. As a result, AI can find various issues and solve complex business problems far more efficiently than any human.

As mentioned above, AI technology costs a lot of money. If you don’t implement it correctly, it can quickly turn into a waste of resources. Before you even think about investing in AI, you should first identify issues where the solution could offer significant results. The bottom line is you have to consider the size and scope of the project, as well as the impact it will have on your ROI before trying to implement an AI system.

Starting Strategies and Use Cases

AI comes in many shapes and forms, and it can apply to many different things. For example, some businesses use it to improve product development, while others use it to make smarter business decisions. Once you’ve set clear company goals and found areas where AI could make a difference, you can create a starting strategy and work your way up from there. Here are some of the most popular AI use cases among businesses around the world:

  • Product development improvement
  • Developing more advanced services
  • Making smarter business decisions
  • Automation of repetitive tasks
  • Manufacturing process automation
  • Predictive maintenance

Of course, most AI solutions can help you improve your business on multiple fronts. With that said, you should first figure out which processes need help the most. As the AI starts finding solutions, you can scale things up and add new priorities to improve your operation further.

Ethical and Legal Issues

Once you’ve got strong AI data strategy in place, and you figure out its main applications, you must ensure that the solution meets ethical and legal standards. However, that is often easier said than done.

Most AI systems run into some kind of legal issues at one point, and it’s your job to limit its use of data. For example, you have to monitor how it uses data to eliminate bias and discrimination. You’ll also want to make sure that it doesn’t break data privacy rules or uses data it shouldn’t have access to.

Infrastructure and Technologies

AI uses data to find issues and propose the best solutions, which is why you have to use the right technologies to provide data on all levels. Most AI solutions use ML and deep learning to filter through data on four different levels. They are:

  • Data collection
  • Data storing
  • Data analytics
  • Insight creation

All four stages are equally important and can have a drastic effect on the results. The first step is to create multiple data pipelines and use various filters to increase data quality. Once the AI starts collecting data, it needs secure storage to segment data and prepare it for the next step. Data analytics will filter the data further and find potential problems. Finally, once the problems are found and defined, the AI will propose solutions to improve the operation.

Required Skills

Making a transition from a traditional business model to a modern AI-driven company requires a whole new skill set for most staff. You’re probably looking at training costs, and months of sessions before your IT and engineers get the hang of how the tool works. Moreover, some team members won’t be able to keep up with the new requirements, so you’ll have to think about hiring new staff or even partnering up with an AI provider for better results.

Changing Things

Let’s say that you’ve got the AI running and that it’s doing an excellent job of finding problems and providing solutions. Implementing these new advanced solutions will still come with all kinds of challenges. That’s why you’ll have to sit down and think hard about your next steps. Some solutions will cost a lot of time and effort, while other, simpler issues are usually not that hard to correct. The bottom line is, you have to be prepared to make some difficult decisions that will change the way your business operates but will offer better results.

The most important thing to consider is that you’ll have to work closely with other team members and improve overall communication. Some solutions might impact human jobs with the use of automation, so you might have to lay off some staff. The process of change management will surely come with a few challenges, but the results are often well worth the effort.

The Work Never Stops

You should also know that AI requires a lot of maintenance and continuous work if you want to grow your business. You will have to change your AI strategy according to your business needs. Some hard work can help you create a unique system that gives you an edge over the competition.

Pitfalls to Avoid

Once you start using AI to improve your business processes, you’ll find yourself in a tough situation at some point. The first issue you’ll encounter is figuring out if the solution is worth it. The worst thing that can happen is you spending a fortune on an AI solution, only to figure out that it’s not doing a great job after a few months.

The next issue revolves around skills. Even if you do a great job of training employees, some of them won’t be able to catch up. If you can’t fill the void, you should hire new people rather than trying to retrain your existing staff. Without an excellent understanding of how AI works, undertrained staff can end up harming the operation rather than doing any good.

Lastly, once you get the AI going, and it takes over your manufacturing software looking for ways to improve it, you should use the tool to its full extent. Many businesses make the mistake of thinking that AI is used only for the automation of repetitive tasks and to reduce costs.

While that is an excellent starting point, AI can help you in many other areas. That includes improving customer interaction, helping build trust, and creating a knowledge base for self-service customer support. The more processes you turn over to the AI, the better results it will offer in the long run.

Final Words

Think about your AI strategy as an ever-evolving set of instructions that help point your AI in the same direction as your business goals. The right setup and data quality can help you improve business function and keep growing your business on the best practices.

So, if you want to reimagine your business using an AI solution, you should ensure that the effort will payout in the end. Don’t spend your money on an AI solution if you don’t need it. Your investment can only pay off if you apply it to the right strategy.

Author bio

Travis Dillard is a business consultant and an organizational psychologist based in Arlington, Texas. Passionate about marketing, social networks, and business in general. In his spare time, he writes a lot about new business strategies and digital marketing for Finddigitalagency.

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