10 steps to effectively implement artificial intelligence in your business

Artificial Intelligence (AI) is taking the tech industry by storm. We are seeing an increase in integrated solutions with virtual assistants and chatbots, with large enterprises integrating AI across the entire technology stack. A recent report suggests that the global AI market will have a valuation of $190.61 billion by 2025, and the expected annual growth rate will be around 33.2%.

Artificial intelligence and related technologies make our existing solutions even smarter and help us unlock the power of data. Machine learning algorithm, computer vision, natural language processing and deep learning are now easy to integrate with any solution or platform.

Artificial intelligence can disrupt critical business processes such as collaboration, control, reporting, planning, etc. In this blog, we will discuss ways for organizations to implement AI effectively and efficiently.

Search and understand

First, familiarize yourself with what enterprise AI can do for your business. In addition to consulting pure AI companies who can advise you on the best course of action, there is also a wealth of information available online to familiarize yourself with. Some universities like Stanford offer online articles and videos on techniques, principles, etc. of AI. Your tech team can check out Microsoft’s open source Cognitive Toolkit, Google’s TensorFlow open source software library, AI Resources, The Association for the Advancement of Artificial Intelligence (AAAI)’s Resources, MonkeyLearn’s Gentle Guide to Machine Learning and other paid and free resources available. More research gives you a head start, and you’ll know what you’re getting into as an organization, how to plan for it, and what to expect at the end.

Identify the use case

Once you know what AI can do, the next step is to identify what you want AI to do for your business. Consider how to add AI functionality to your products or services. Build specific use cases in mind for how AI can solve some of your challenges and add value to your business. For example, if you’re reviewing your existing technology program and its challenges, you should have a solid case for how image recognition, ML, or others can fit into the product and its usefulness.

Financial value of the attribute

Once these use cases are ready, assess their potential business impact and project the financial value of the identified AI implementations. Tying business value to AI initiatives will ensure that you don’t get bogged down in the details and always put results at the center. The second part is to prioritize AI initiatives. Put all of your initiatives into a 2X2 matrix of business potential and complexity, and this will give you a clear picture of which ones to pursue first.

Identify skills gaps

Once you’ve prioritized your AI initiatives, it’s time to check if there are enough ingredients in the kitchen. It is one thing to want to accomplish something and another to have the ability to organize it. Before launching a full AI implementation, you can assess your internal capacity, identify skills gaps, and then decide on a course of action. You can hire additional resources or partner with pure-play product engineering companies that specialize in AI.

Pilot led by PME

Once you’re ready as a business, start building and integrating AI into the business stack. Have a project mindset, and most importantly, make sure you don’t lose sight of business goals. You can consult with subject matter experts in the space or external AI consultants to make sure you’re on the right track. Your pilot will give you a taste of what the long-term implementation of an AI solution will entail. The pilot will make the case even stronger and you can decide if it still makes sense for your business. But for the pilot to be successful, you’ll need a team of your people and people who know AI to keep it unbiased. Having external SMEs or consulting partners is a great added value at this stage.

Massage your data

High-quality data is the foundation for a successful AI/ML implementation. Cleansing, massaging, and processing your data is key to getting better results. Typically, business data resides in multiple silos and various systems. Form a small unit, especially cross-functional, to integrate different datasets, resolve inconsistencies, and ensure the result is high-quality data.

Take baby steps

When you start, start small. Apply AI to a small dataset to perform in-depth testing. Then, gradually, you can increase the volume and collect continuous feedback.

Plan storage

Once your small dataset is up and running, you need to start thinking about additional storage to implement the full solution with full data entry. The performance of the algorithm is just as important as its accuracy. To handle large volumes of data with greater accuracy, you need a high-performance solution backed by fast, optimized storage.

Manage change

AI provides better insights as well as automation. But it’s a big change for employees because they are expected to operate differently. Some employees are more suspicious than others, and they need to embrace change positively. You will need a formal change management initiative to introduce the new AI solution augmenting their daily tasks.

Build safely and optimally

Usually, companies start building AI solutions around specific aspects or challenges without investigating the limitations or requirements of the solution as a whole. This will result in sub-optimal or dysfunctional and sometimes also insecure solutions. You will need a balance between storage, graphics processing unit (GPU), and network to achieve an optimal level. Security is also mostly overlooked, and most companies realize this after implementation. Make sure you have security measures in place such as data encryption, VPNs, anti-malware, etc.

Implementing AI is no small feat and challenges can arise at every step. But with every technology, the challenges associated with adoption are the toughest. Data literacy and trust are the two pillars for the introduction of any new technology. Another important aspect of AI initiatives is that they mature with your data management strategy. You will need both to operate in parallel to be successful.

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