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Aditi Saha

Becoming an AI organization

AI may sound like something from the future, but nowadays, it’s becoming a must to gain a competitive advantage. As the industry is booming and the budgets are quickly expanding, this is fueling the expansion of AI-based solutions.

Demystify AI

One of the first steps to becoming an AI organization is understanding what AI can and cannot do. Knowing its limitations helps you manage your expectations and realize its adoption isn’t going to make all the problems magically go away. Another concept to keep in mind is not to fall in love with the tools. Instead, focus on the problem you’re trying to solve. Remember that AI is just another set of tools, and there are as many solutions as possible problems. So look for the one that better suits your needs.

Focus on the proper business case

The second challenge to tackle is to find the right business use case to apply AI. Your first AI project shouldn’t be the most ambitious one; try starting small and look for those opportunities with higher ROI.

There are two possible approaches here, you can attack a problem you’ve already identified, or you can make a first explorative phase in your AI journey and try to discover the most appropriate use cases with which to start. We always share our work and experiences. Check them out; you might feel identified with some of them!

Get your senior management on board

Business leaders must understand how AI will affect their companies and get prepared, so they won’t be left behind. A clear understanding also means everyone knows about its limitations and makes it easier to get rid of unrealistic expectations. You also have to state clearly to all the stakeholders how you measure the success of the project. Getting your senior management AI literate in the subject is always going to be in your favor. Here are some guides to the hottest topics in AI.

Take a look at your data

One of the mantras within the AI world is garbage in, garbage out. Even though I strongly agree with this statement, because the input we give to our models is one of the essential points to obtain good results, you don’t have to fall into the data perfection trap.

Obtaining more data and improving quality is an iterative process in your AI journey.

Build the right team

To get your machine learning project off the ground, you must have a team with a broad range of skills, including non-technical roles. This means having the right combination of people who can identify the limitations of both the technical side and the business side.

Interdisciplinary collaboration in AI projects helps you cover many aspects so that your project is on the right path to success, utilizing the best of each profile.

Another aspect of planning is devising is a system that allows collaborators to work in a coordinated way with a goal-oriented mindset.

Common pitfalls to avoid

Since AI is still in its early stages in the business world, things going wrong is always a possibility. So here are some common pitfalls to avoid!

All-in projects

I want to start with a piece of handy advice: don’t try to boil the ocean on day one. You are definitely not going to transform all the processes in your entire organization in a blink.

The key to achieving remarkable results is to find the low-hanging fruit projects. Go tier by tier, as opportunities may not be straightforward to notice. Here’s where your domain knowledge of the company can shine. You should analyze all the high-cost components of your operation and think whether something could be better by using data or partially automating a process.

Look for high-impact projects that make the investment worth it. But don’t overdo it by going with the most ambitious venture; prioritize opportunities with higher RoI first.

Starting without the right people in the right place

When starting an AI project, one of the first questions is whether to outsource or build an in-house data science team. The answer depends greatly on the degree of maturity your organization has concerning its data, technical capabilities, and culture.

While building an in-house data science team sounds appealing in the long run, based on our experience, outsourcing seems like the right decision to start this mind-blowing journey, especially when you have time constraints.

Remember that partnering with an AI consulting firm means fast-tracking your AI journey. Specialized companies provide you guidance, knowledge, and tools based on their experience on similar successful projects and shorten the path to success and achieve outstanding results.

If we look at the international evidence, outsourcing is a growing trend, especially for companies that want to minimize their non-core activity cost structure.

Stay halfway

Having a great idea to introduce AI in your organization is not enough to achieve results in your bottom line. You need the correct implementation too. Machine Learning models are intended to guide us in our business decisions. Not everything is about predictions, but what we do with them.

Conclusions

Nobody said that becoming an AI organization is an easy task to implement and deploy. However, it is a path worth going through to obtain a competitive advantage that differentiates you from your competitors and increases your revenue.

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