Countless business leaders have been thinking about how they can use AI and machine learning to add value in their company. We all know AI is both the present and the future. It will have a massive impact in virtually all areas of business.
The question then becomes, “How can we leverage AI technology to solve important business problems in ways previously not possible?”
It can be easy to get carried away by the ‘hype’ and jump into projects without rigorous upfront assessment of feasible use cases. Getting started with AI in your company is certainly a key strategic move, but you have to be aware of the lifecycle of any AI initiative.
Here are 3 questions to ask yourself before adopting AI in your company:
1) What data do we have, or can acquire, in large amounts?
This question is extremely important because, fundamentally, machine learning is using data to answer business questions. These business questions depend on the business problems you want to solve with AI.
Data can take the form of customer data, sales data, marketing data, photos, video, text, audio, and more. AI systems learn the patterns and relationships within the data to gain the ability to make predictions.
Data is the fuel that drives the performance of all AI and machine learning systems. The results you get – how well AI is able to solve business problems – depends largely on the quantity and quality of the data used.
2) What business problems could this data help us solve?
Suppose you have a large amount of audio data from customer service calls, and you want to automate some portion of your customer service department. You may be having issues with hiring, training, and managing large call centers. AI can help streamline a significant portion of customer service calls without the need for humans, meeting or exceeding their performance.
You could also have a large amount of product sales data from a 10-year period, and you want to answer the question of whether a new product launch will be successful (you define what success means based on KPIs). You may be struggling with a history of failed product launches. AI can help you determine which products are most likely to fail, and which ones are most likely to be successful.
There are countless questions AI can help you answer, given sufficient amounts of high quality data.
3) Do we have in-house technical talent?
If you are just getting started with AI and machine learning, you may not have data scientists or machine learning engineers in your company yet. Once the initial AI strategy phase is complete, you need technical talent to implement the technology for your use case(s).
There are multiple ways to approach this. You could re-skill and train 1-3 software engineers already in your team. This does not necessarily mean they relinquish their current responsibilities. They are simply learning new machine learning skills to help implement the company’s AI initiatives.
This is good because you don’t need to allocate significant resources for hiring scarce AI talent, and you develop your own talent in-house. This also helps your team learn new skills, grow, and stay engaged on the job. Working on AI is also extremely empowering and satisfying for many software engineers. Many of them even have the goal of shifting their career towards AI, and have begun working towards it on the side.
The downside is that it may take a while for them to pick up these skills and implement the AI use cases. AI has a large learning curve, and it’s often counterintuitive from a technical perspective. This is where you may want to bring in AI consultants for the initial phases of the projects while your own team’s skills and capabilities ramp up. AI consultants can help you identify use cases for AI in your company, train technical talent, coach business leaders on AI leadership, and help with the technical implementations.
These 3 questions encapsulate a high-level framework to help you think through the value of AI in your company.
If you need help to accelerate your company’s machine learning efforts, or if you need help getting started with enterprise AI adoption, send me a LinkedIn message or email me at email@example.com and I will be happy to help you.
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