AI & ML in Business

Best Practices to Get Started With AI & ML in your Business

Are businesses getting to grip with Artificial Intelligence and Machine Learning? AI and ML have been creating a

Are businesses getting to grip with Artificial Intelligence and Machine Learning?

AI and ML have been creating a lot of buzz in the tech realm for the last couple of years. However, many businesses still aren’t clear about the long-term benefits of these technologies.

According to a new report by Microsoft named ‘Maximizing the AI Opportunity’, nearly two-thirds of business leaders lack clarity when it comes to potential returns from artificial intelligence.

The report surveyed 1000 business leaders in the UK. It may seem surprising but there are a lot of companies out there that still consider AI as an unjustifiable expense with more limitations than benefits. Many industry experts point out that AI shows promise but adopting it in now in its present state may lead to unpleasant surprises.

That said, businesses that have invested in AI in return for something did see tangible benefits. Many organizations don’t even realize that they are using AI in some form. As a matter of fact, getting started with AI is rather simple really.

This blog includes a few best practices enterprises can follow to get started with AI in the right manner.

Identify business problems

Before investing in AI, an enterprise should make sure that they have identified and individually assessed each of their business problems. This way it will be easier for them to determine if a specific business problem can be solved efficiently with AI. The problem can be anything from handling a lot of online customer chats to freeing up employees’ time so they can focus on core tasks; AI can solve pretty much all of it.

Assess enterprise readiness to adopt AI/ML

Once the enterprise identifies the problems that they can solve using AI, the next step is to assess the enterprise itself for its readiness to build and manage AI or ML-based systems.

Major cloud service providers like Microsoft Azure and AWS offer on-demand ML services for a number of purposes including image processing and speech recognition. In addition, tools and ML-centric software frameworks are also available for the enterprise itself to build custom ML models or in-house AI-based systems. For any of these systems to work, data play a crucial role.

The main question to ask here is whether the enterprise generates and captures the right kind of data to train predictive ML models to deliver the right results. Even if it collects the right kind of data, there should be copious amounts of them for the AI system to process. Both quantity and quality of data matter when AI-based analytics is involved.

Assess in-house skills to handle AI

Before getting started with AI, enterprises should make sure they have the right talents to manage their AI systems. It’d be a wise approach to assess in-house skills to handle the AI project. This way the enterprise would also be able to identify missing skills that they will need in their mid to long-term run with AI systems.

Microsoft’s report also found that many business leaders are not sure as to how they can provide employees with the skills needed to adapt to and manage the disruption caused by AI. There’s also the fact that employee roles might change once AI starts doing its job. This requires the organization to put considerable effort into training employees when needed and retain sufficient skills for stable functioning of the AI ecosystem.

Experiment with AI

AI’s only started being of use to businesses. As such employees may be hesitant to engage with it. It’s up to the organization to foster a work culture where employees can experiment with the implemented AI systems. The employees can start small, get familiar with the technology, learn how it works, and then tinker with it to get an idea of what it can and can’t do. The company can gather feedback from them and scale up to leverage AI better.

Ensure ethical handling of AI

At the end of the day, data-driven AI/ML systems are only as good as the data fed to them. Invalid, biased, or wrong data will drive an ML algorithm to deliver useless results. Many companies make this mistake of feeding flawed data into the system leading to poor results and ROI.

All factors considered, it’s a great practice for a business to use an AI/ML manifesto to ensure that the technology is used ethically and securely. The manifesto should lay out specific guidelines to overseers and everyone else in the company using the AI-system.

Conclusion

AI and ML tech are at their infancy even now and demands diligent human oversight. The technology should be nurtured to deliver unbiased, responsible outcomes that can give businesses a great edge in the market and trigger disruption. If the company is ill-equipped to leverage AI but is willing to invest nevertheless, it’s better to seek service from an established AI expert.

That said, it’d be a pleasure for AOT to provide you with assistance when it comes to new-gen technologies like AI, ML, and IoT. Feel free to dial us up to learn more about AI for businesses.

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