Artificial intelligence (AI) is taking the technology industry by storm. We see a surge in solutions embedded with virtual assistants and chatbots, with large enterprises integrating AI across the entire tech stack. A recent report suggests that the global AI market will have a valuation of $190.61 billion by 2025, and the forecasted annual growth rate will be around 33.2%.
Artificial intelligence and related technologies are making our existing solutions even more intelligent and are helping us unlock the power of data. The machine learning algorithm, computer vision, natural language processing, and deep learning are now easy to bake into any solution or platform.
Artificial Intelligence can disrupt critical business processes like collaboration, control, reporting, scheduling, and more. In this blog, we will discuss ways for organizations to implement AI efficiently and effectively.
Research and Understand
First and foremost, get acquainted with what enterprise AI can do for your business. In addition to consulting with pure-play AI companies who can advise you on how best to go about this, you can also refer wealth of online information available to familiarize yourself. Some universities like Standford have online papers and videos on AI techniques, principles, etc. Your tech team can check out Microsoft’s open-source Cognitive Toolkit, Google’s open-source TensorFlow 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 will know what you are getting into as an organization, how to plan for it, and what to expect at the end of it,
Pin-point 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. Think of how to add AI capabilities to your products or services. Build specific use cases in mind around how AI can solve some of your challenges and add value to your business. For instance, if you review your existing tech program and its challenges, you should have a strong case around how image recognition, ML, or others can fit into the product and how useful it will be.
Attribute financial value
Once you have those use cases ready, assess the potential business impact of those and project the financial value of the AI implementations identified. Tying business value to AI initiatives will ensure you are not lost in details and always put outcomes at the center. The second part is to prioritize AI initiatives. Put all your initiatives in a 2X2 matrix of business potential and complexity, and that will give you a clear picture of which ones to go after first.
Identify skill gaps
Once you have prioritized your AI initiatives, it’s time to check if there are enough ingredients in the kitchen. It’s one thing to be wanting to accomplish something and the other to have an organizational capability for it. Before launching a full-blown AI implementation, you can assess your internal capacity, identify skill gaps and then decide on a course of action. You may hire additional resources, or you can tie up with pure-play product engineering companies specializing in AI.
Pilot under the guidance of SMEs
Once you are ready as a business, start building and integrating AI within the business stack. Have a project mindset, and importantly ensure that you don’t lose sight of business goals. You can consult with Subject Matter Experts in the space or external AI consultants to ensure that you are on track. Your pilot will give you a taste of what long-term implementation of an AI solution will involve. The pilot will make the case even strong, and you can decide if it still makes sense for your business. But for the pilot to succeed, you will need a team of your people and people who know AI to keep it impartial. Having external SMEs or consulting partners is a great value add at this stage.
Massage your data
High-quality data is the basis of a successful AI/ML implementation. It is critical to clean, massage, and process your data to get better results. Usually, data for enterprises is in multiple silos and various systems. Form a small unit, especially cross-functional, to integrate different data sets, resolve inconsistencies, and ensure that the output is high-quality data.
Take baby steps
When you start, start small. Apply AI to a small data set to test thoroughly. Then incrementally, you can increase volume and collect feedback continuously.
Plan for Storage
Once your small data set is up and running, you need to start thinking about additional storage to implement the full-blown solution with complete data input. The algorithm’s performance is equally important as its accuracy. To manage large volumes of data for better accuracy, you need a high-performing solution supported by fast and optimized storage.
Manage the Change
AI provides better insights as well as automation. But it’s a big change for employees as it expects them to operate differently. Some employees are warier than others, and they must accept the change positively. You will need a formal change management initiative to introduce the new AI solution augmenting their daily tasks.
Build Securely and Optimally
Usually, companies start building AI solutions around specific aspects or challenges without studying the limitations or solution requirements as a whole. It will result in sub-optimal or dysfunctional solutions and sometimes insecure too. You will need a balance of storage, the graphics processing unit (GPU), and the network to achieve an optimum. Security is also mostly overlooked, and most companies realize that post-implementation. Make sure you have security safeguards in place like data encryption, VPNs, anti-malware, etc.
AI implementation is no cakewalk, and challenges may arise at every step. But with every technology, the challenges associated with the adoption are the most difficult to tackle. Data literacy and trust are the two pillars of introducing any new technology. Another important aspect of AI initiatives is that it matures with your data management strategy. You will need both of them to run in parallel for success.
Disclaimer Views expressed above are the author’s own.
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