Where do I start with AI?


This post was originally published by Marc Nehme at Towards Data Science

Entering the world of artificial intelligence

Over the last 7+ years, I’ve worked with many leaders who were looking to embark on their first AI journey. Most of them did not know how or where to start. Some thought they did. In order for your business to realize the gains possible using AI, you need to make sure you start your journey on the right path. Here are some key things to consider when beginning your journey…


You need to be able to clearly articulate what your ultimate goal is. If you cannot do that, then you are not ready to actually start the journey. Your goal should NOT be “I want to run an AI project”. That is too vague and not outcome or results driven. That is a red flag and often a recipe for failure.

Clear goals can also be at different levels. A targeted, yet somewhat high level goal could be “I want to improve the customer experience”. The general outcome is clear — “improve the customer experience” but it is still vague in the sense of there are no dimensions to the goal. A more specific goal would be “I want to improve the customer experience in the call center”. A very specific goal would be “I want to improve the customer experience in the call center by reducing call handle times”. The more specific you can get, the better off you will be downstream.

Again, attempting to start a project using AI without a clear vision and goal will likely not provide you with optimal results. Do not try to implement AI just to “do it”. Start the journey with specific goals in mind.

Identifying areas of optimization

The next step is identifying where the pain points are. In this example, you’ll look at the end to end customer experience. You’ll identify what is optimal and non-optimal, the as-is and to-be scenarios, the actors involved, and more. This can be achieved through design thinking sessions — I will not get into that topic here. There are a lot of online resources that discuss and explain design thinking. IBM has a phenomenal design thinking program to help customers get started on their journey.

Let’s assume you go through the design thinking session and have those areas identified. In that session, you’ll also identify the AI capabilities to optimize those areas. For example, implementing a conversational AI assistant will help your call center agents get answers to questions faster, which will reduce call handle times. Always identify the use case/area of optimization first THEN map technology to solve it. Do not do the opposite!

A big decision

You also need to decide how you will handle the implementation. There are several choices here.

1 — Build your own AI practice and develop your own AI technology

This will often require the most time and investment to get going. As a leader, you need to assess many things such as: Do you have existing AI skills internally? If not, how will you acquire them? What technology do you have available to you to get started? How long will this all take compared to your timeline? Will this deliver the best results? Does this align with our company strategy and vision? Being 2021, there are many companies out there who already have mature AI platforms. You need to assess if reinventing the wheel for your own purposes is necessary.

2 — Build your own AI practice using an existing AI vendor platform

Here, you up-skill your employees on another AI vendor’s platform, such as IBM Watson or Microsoft AI. In this option, your bigger investment is in your own employees knowledge. The benefit here is that you do not need to develop the AI technology — just learn how to use it. Your employees would customize the virtual assistant, in this example, to satisfy your use case. For example, using IBM Watson Assistant (an AI-powered virtual assistant), you can leverage the existing cloud service APIs (via SaaS model). You can also deploy Watson in your own cloud on Azure, AWS or GCP, or you can deploy Watson on premise if you so choose. In either case, your team can focus on building the virtual assistant to meet your use case. They do not need to worry about developing or maintaining the AI service itself.

3 — Hire professional services to do the implementation for you

As a leader, there are situations where you simply do not have the time or staff to go with options 1 or 2. AI can also be daunting depending on your skill set. You can choose this option of hiring professional services to implement the virtual assistant for you. Here, you still leverage an existing AI platform in the market, but you will hire a company to tailor the assistant to fit your use case. This should give you some peace of mind since you have hired professionals. They will guide the implementation every step of the way. Your main involvement will be to provide the domain subject matter expertise to the implementation team.

4 — Build your own AI practice and hire professional services

This is a hybrid model of options 2 and 3 above. You can hire professional services to guide the implementation while your internal team up-skills side-by-side with the professionals. There are many benefits to this approach. The main benefit is you get the professional assistance you need along the way while training your own staff to be self-sufficient. Here, you balance the main project deliverable of a virtual assistant along with up-skilling your team. You’re accomplishing two major goals, simultaneously.

There are many aspects to be considered when making your choice. You need to assess budget, timeline, strategy, staff, etc. There are also different ways you can take your journey. For example, you can start with option 3 for your first release and then pivot to option 4 for the second release. Ideally, you want to pick one strategy and try to stick to it to maximize value.

In the end

The bottom line is you need to ensure you are starting your AI journey for the right reasons, with quantifiable goals, meaningful business outcomes and the right implementation plan. Seek professional help if you are unsure. There are also many online resources to help educate you and get hands-on with AI, for free.

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This post was originally published by Marc Nehme at Towards Data Science

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