Artificial Intelligence

8 big mistakes one should avoid in AI development

The excitement created among businesses by AI technology continues to grow day after day. The IDC’s latest prediction highlights that business will spend on cognitive and AI systems like deep learning and chatbots, as well as the required infrastructure to support these technologies. The cost is estimated to be at least three times more from the $24 billion forecast for this year to $78 billion in 2022.

Also, AI adoptions have now gone from early adopters to mainstream businesses coming from almost all industries. They are exploring new AI pilot projects and putting AI into action for their business processes.

But with the growing and shifting trends in AI, the related risks have increased too. Although the technology isn’t new, its adoption at massive levels is. Thus, there are sure chances of mistakes being made in any AI development project. Is your business the one with plans to apply AI to its operations? If yes, this post is going to point out key mistakes which are most likely to be made by your AI developers. Its purpose is to do not let your money go wasted at the end of the day.

1. Taking on more than you can handle

Advancement in AI technology is exhibiting limitless possibilities. But that does not mean you start to consider each and every possibility. You should not opt for an AI development project that transforms your entire business-decision making process in a single shot. It’s impossible and unbelievably expensive. And you shouldn’t be trying to implement it without a clear idea of what you want.

The best way is to start with small progressive steps. More operations and project ideas can be introduced once you start gaining expertise.

It’s best to start with the low hanging fruit that you can easily catch without putting too many efforts.

2. Investing in a one-off AI system

An AI that does not help you create an overall process to develop further AI and not the part of the existing data pipeline, would be a one-off system. And it will not take you too far. You will succeed only when you think of the sustainability and lay the foundation for your AI asset while considering all probabilities with each individual project.

Sustainability also means that you invest in a system that generates enough ROI that you can invest again to develop and scale it out further. When it happens, your business comes up with an AI capability that gradually serves the whole business, not like a new tool for a specific requirement only.

3. Beginning without the right infrastructure

AI is different from the core web and software development technologies which is already available in the market. When it’s an AI project, you would need to invest in both core and more advanced digital technologies creating the right infrastructure. Companies which do not have the expertise or the exposure in cloud computing, mobile software, web, big data, and analytics are likely to experience three times more difficulties than those with them. 75% of organizations adopting AI depended on what they learned from building existing digital capabilities.

4. Beginning without data

By far the majority of AI systems are ML systems and, they need data to do jobs. But in most of the cases, a company would use the same public data which is also used by their competitors. This delivers moderate or no result helping in improvements. To get better results than your competitors, your AI should be based on the data better than them. And to get better data, your company will require working on its own unique data that will be ready for the AI only after going through cleaning, normalization and preparation processes. Also, you will need making big investments for collecting, cleaning data for your AI system.

5. Not defining ways to assess and measure success

Before a business starts investing in its AI, it needs to have a hypothesis for how decisions, sales, customer support, etc will be improved with the use of the technology. A hypothesis has to be tested in action and evaluated for its results.

So, what’s the hypothesis? It’s nothing but a proper plan on how the success of a project will be measured in terms of both adoption and outcomes. The decisions backing an AI implementation should be data-driven rather than intuitions or rough estimations. If you ignore data routinely, even smartest AI tool will fail to help you.

6. Beginning cluelessly whether it can really help or not.

The currently available form of AI is way behind what Ironman’s Jarvis can do. It doesn’t come with a magic spell that does anything. However, there have been significant advancements in AI in the last few years; it still cannot be applied to any given problem. A business must know what AI can deliver and, how it will integrate into its existing systems and processes.

You should not adopt AI just because you have read that all other companies are adopting it. If you do not know whether AI will help you improve a process or not, do not go for the technology.

7. Starting without the right pools of people

Success will not depend on the best of technologies putting into action but on who is handling the technology. To implement an AI successfully, you are going to need a team of data sciences experts. In case you don’t have a team with the expertise, you should build up the expertise in the IT team. But if you go without a dedicated data science team, you are surely going to make a big mistake.

8. Building to expand AI capabilities for different functions

Even pre-built AI services take time for implementation. We have learned this from the highly popular IBM Watson. Thankfully, big companies are already working on providing ready-made solutions built into SaaS offerings like Salesforce, Dynamics, and Adobe Marketing Cloud. The ML services from Azure, AWS, and Google are already available in the market. They do not require a business to build everything from scratch. If the business still does that, it’s surely wasting its money.

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