Pathway to Successful AI Implementation in IT Infrastructures

Sanam Malhotra | 24th March 2020

Artificial intelligence (AI) is at the cutting edge of innovation, especially for the IT industry. From data centers to communication services, IT services are witnessing an influx of AI applications across verticals. As an emerging provider of artificial intelligence services, Oodles AI highlights key business prerequisites to trigger AI implementation in IT services.

 

Steps for AI Implementation in IT Businesses

1) Get educated about AI

Gaining knowledge about AI’s basic principles, underlying technologies, and techniques prepares the groundworks for proper AI adoption across businesses. Interactive sessions, workshops, and webinars can be organized to teach and train employees for handling realistic AI projects with efficiency.

In addition to business-oriented training, AI brings double-edged benefit to employees as well as employers, as suggested by Ginni Rometty, CEO of IBM,

“Some people call this artificial intelligence, but the reality is this technology will enhance us. So instead of artificial intelligence, I think we’ll augment our intelligence.”

 

2) Solve a specific business problem

Once developers and strategists acquire fundamental knowledge about AI, its frameworks, supported programming languages, and libraries, it’s time for problem mapping. It involves the identification of a specific business sector to implement AI. It is an ideal starting point as compared to large scale automation that may backfire due to inadequate resources.

Here’s how to get started-

a) Take a holistic view of your organization’s programs and problems

b) Associate each problem with an AI solution. For instance, a company dealing with hardcore software development can automate the bug identification process with machine learning algorithms.

c) Ensure that problem specified should be backed with relevant data to trigger the model training process

 

3) Start with a smaller use case

The integration of AI-powered automation at an enterprise scale requires complete synchronization between data and model development. Therefore, it is advisable for organizations to trigger AI implementation with smaller applications to better evaluate outcomes and KPIs. Under AI, machine learning solutions and chatbot development services are major enablers of automation for business processes.

For IT services, here are some effective AI initiative-

a) Machine learning development for software development

b) Chatbot development and integration for customer relations

c) AI Configuration with Raspberry Pi for IoT Applications

d) AI-powered test automation tools

 

4) Locate relevant data

Data is the most critical building block for AI models. Building a domain-specific AI solution requires a seamless flow of relevant data from one or multiple sources. Businesses need to follow a step-by-step approach to handling data for model development.

Here’s how developers can channelize data-

a) Prepare data with meta tags and labels associated with specific use cases to accelerate the input process

b) Flag any customer-sensitive information to ensure compliance

c) Choose an appropriate development environment for the type of data, such as videos, images, textual data, etc.

d) Assemble your AI model close to the data repository to reduce latency and bandwidth requirements.

e) Always use a subset sample of the data to run predictive models efficiently

Once businesses develop a functional domain-specific model, it is vital to maintain a continuous flow of data to generate greater accuracy and value. For this, businesses should focus on investing in self-learning models that attain flexibility as new data is fed into the model. Long-term data storage strategies are another crucial factor to ensure business agility in the long run.

 

Initiate AI Implementation in IT Infrastructures with Oodles AI

The IT industry is the cornerstone of global technological development. With the advent of AI, IT businesses are witnessing a major boom in investment, deployment, and execution of AI solutions across verticals. We, at Oodles, are capturing growth for IT sector by developing proprietary AI and ML solutions including-

a) Machine learning development for enterprise software

b) Integration of AI models with IoT devices

c) Chatbot development and integration for communication services, and more.

In addition, our AI capabilities encompass computer vision, natural language processing, recommendation engines, and predictive analytics. Reach out to our AI development team to know more about our artificial intelligence services.

About Author

Sanam Malhotra

Sanam is a technical writer at Oodles who is currently covering Artificial Intelligence and its underlying disruptive technologies. Fascinated by the transformative potential of AI, Sanam explores how global businesses can harness AI-powered growth. Her writings aim at contributing the multidimensional values of AI, IoT, and machine learning to the digital landscape.

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