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From Alexa to social media feeds, artificial intelligence (AI) has found its home in our daily life; and, Business world is no exception. As AI success stories are doing the rounds, more small and medium-scale companies are aggressively embracing AI for their business benefits. But there are some other large enterprises that are waiting for the technical maturity of modern AI to take part in the AI game.
Does your organization – like many others – want to implement AI solutions into the workflows as quickly as possible? If so, here are some tips to draft a successful AI strategy.
Learn the nitty-gritty of AI
Being the fastest-growing niche, there’s so much to know and learn about modern AI. Hence knowing the pulse of AI is imperative. You can utilize either online courses or workshops to learn everything from basics to advanced aspects.
Chart out the problems/purpose
To start off, find the answer to: “Is the organization ready to implement AI?”. For this, you must pick up a problem(s) you’d want to solve with AI. Maybe you want to reduce ‘repetition’ from the workflow, or you want to give a fool-proof backbone to the products or any reason that may scale up your organization. Remember, not every business requires or is using AI…yet!
Understand your audience/customers
So, you’ve figured out the purpose to create your own AI. Great! Now, it’s time to think about the end-users – your customers! Again, equip yourself to find another set of answers to: “What problems can AI solve for your customers?”, and “Will it strengthen customer experience?”.
Evaluate the industry trends
Conduct a niche analysis of the latest trends and competition. Your end goal should be to make a better AI-mediated product or solution to whatever is already available. Parallelly, measure your internal capabilities to support the decision to bring in AI to your organization.
Set up a pilot project
Go with a tried and tested methodology to run the pilot project. SEMMA and CRISP-DM are two great models you can rely on. SEMMA – Sample, Explore, Modify, Model, and Assess – is the procedure-focused standard methodology. If you want to bring in result-oriented approach, try CRISP-DM or ‘Cross-Industry Standard Process for Data Mining. Be sure to include experts who’re aware of the business and AI while running the pilot.
Find High-quality data and integrate
Before choosing machine learning algorithms, define the amount of data required and the potential sources. For instance, data scientists rely on open sourced data, artificial data, or other external datasets. Next, integrate these data sets and figure out inconsistencies or errors.
Choose the best algorithms
Sure, there is no shortage of algorithm. But while choosing the one, consider these factors: result accuracy, training time, linearity in usage, number of feature and parameters etc. Some of the widely used algorithms are Convolutional Neural Network (CNN), Recursive Neural Network (RNN), Multi-Layer Perceptron (MLP) etc.
Build your infrastructure with balance
While building and maintaining your own AI infrastructure, make sure it has enough bandwidth for storage, GPU, and networking.
In short, if AI looks promising for your organization, then go all out to try it.