Despite being in the nascent stage, AI has already made a mark
in the world of technology. Enterprises across the globe are in a hustle to incorporate this technology
into their business process. However, one cannot deny that AI implementation is
still limited to the digital giants like Google, Facebook, Microsoft or Amazon. 

A report by Statista,
an online statistics and market research panel, suggests that advertising, finance, healthcare, consumer, and
aerospace are the leading sectors to adopt AI. The gain by early adopters and
fear of missing out
is prompting enterprises to incorporate AI technology into their business
system. Let us understand the key steps towards the successful AI adoption in a

Key Steps towards Successful AI Adoption

1. Understand Artificial Intelligence (AI)

Before implementing AI in your business, it is necessary to
understand the basic concept of AI and how different it is from ML. 

AI and ML are the two-terms that are often used interchangeably.
Although the terms are correlated, they have different applications. 

Artificial Intelligence is the technology to simulate human
thinking capabilities, behavior and problem-solving. It is a bigger concept
that makes computer systems mimic human intelligence. Machine Learning (ML), on
the other hand, is an application of AI that allows the machines to learn from
past data and experiences without being programmed explicitly and make
decisions without human participation. 

Technology helps improve your
business in ways like:

2. Business Value Evaluation

Prior to successful
AI adoption in your business process, it is really necessary to learn the
goals you are targeting and how AI can help you achieve them. To understand your business needs, you have to understand a
few things completely,
such as:

Business value evaluation helps you to examine business problems that require a lot of human
interfaces and complex decision-making process. These business problems can be
resolved by leveraging Natural Language Processing (NLP) and computer vision
to ensure impactful results.

3. Select Pilot Project

A Pilot project is nothing but experimentation and prototypes.
It is critical to select well-defined, small-scale pilot projects which are
technically feasible. Limiting the scope of the project will allow you to have
better execution and control over the results. 

Pilot projects are the key to identify and demonstrate the
possibilities and increase AI awareness and adoption in an organization. Working closely
with the stakeholders of the organization is recommended, as these demonstrations
and prototypes will allow them to understand AI and its benefits. 

4. Ensure Data Quality

AI algorithms work best on a large quantity of data with high-quality. For better execution
of pilot projects, every organization needs to organize frequently, update, and
expand its data-set. It is crucial to have accurate, complete and proper
labelled data for any AI project’s success. It is necessary to provide ample
time in data retrieval and its analysis because this will accelerate the pilot

5. Start Small

Stay selective while starting with AI technology. Begin with a small sample of your data rather than throwing
away all the data you have. 

According to Aaron Brauser, Vice President of Solutions Management at Modal , 

“Start simple, use AI incrementally to prove value, collect
feedback, and then expand accordingly”.

Starting with small and focused projects will reduce the potential risk and, more significant the impact. Initial success with smaller projects boosts confidence and pave the way for more ambitious initiatives in future.  It requires in-depth knowledge and commitment to integrate AI technology into any organization. Currently, it is the technology to take your business to the next level.

However, successful AI execution depends upon the quality and quantity of data. Leveraging this data on a small scale is the key for better outcomes in pilot projects and successful AI adoption. And organizations are aware that without satisfying these values, AI falters. 

This content was originally published here.