The word ‘Artificial Intelligence’ evokes excitement and paints a picture of super-intelligent robots and self-driving cars. Even the statistics are bullish about the impact of AI – By 2020, 85% of customer interactions will be managed without a human. (Gartner); By the end of 2018, ‘customer digital assistants’ will recognize customers by face and voice across channels and partners. (Gartner). AI has widespread uses in the field of artificial agents, internet of things, marketing, financial trading, healthcare. But, are businesses able to capitalize the growing power of AI? Why adopting AI is a distant dream for small and medium businesses.



At a basic level, Artificial Intelligence can be categorized into vertical and horizontal. Vertical AI is adopted by services which perform focused jobs such as automating repetitive work, scheduling meetings, e-commerce chatbots etc. Some notable examples are Kayak, Skyscanner, X.ai. Services powered by horizontal AI can handle multiple tasks but won’t go deep into specific tasks. Cortana, Siri and Alexa are some examples of horizontal AI. They can handle many questions but can’t complete the work. For example, if you ask Siri to plan a holiday, it can perform some individual tasks like providing destination options. Whereas a chatbot like Kayak can search and plan a holiday according to your budget and date. Looking at the present scenario horizontal AIs as an enabler can integrate individual focused vertical AIs to complete a big task from start to end.

But adopting these AI services can be a tedious and expensive affair for an organization especially when it is driven by big data and complex technologies. Let’s take a look at some of the factors affecting the adoption of AI –

1. Lack of AI talent



According to Charles Green, Director of Thought Leadership at Belatrix Software “It’s a huge challenge to find data scientists, people with AI experience, or people with the skills to analyze and use the data, as well as those who can create the algorithms required for machine learning”. A.T. Kearney surveyed 430 senior executives of companies possessing advanced analytics capabilities. Two-thirds of companies answered that they can’t hire enough people who can generate insights from corporate data. This represents a dearth of talent and relevant skills. AI requires people with a firm understanding of advanced programming (Python, Java, C/C++), business strategy and digital technology. The companies often observe that candidates possess right technical acumen but lack the ability to associate it with business strategy.

2. Tangible return on investments

It’s difficult for a business to measure and predict the returns on investment in Artificial Intelligence and Machine Learning technologies. AI is generally linked with the improvement in quality and efficiency of the business in the long run. For example, an e-commerce website invests heavily in AI technology to recommend better products to the users. But the results for improved customer satisfaction and revenue would not be visible immediately.

3. Affordability of organizations

Developing and maintaining an AI solution from scratch will require data engineers, a project manager and expensive infrastructure. According to O’Reilly, it was found that the average base salary of a data scientist in the United States in the year 2014 was $105,000. Including all the other expenses, benefits, bonuses the figure can rise up to $144,000. In essence, a full data science team for AI project isn’t something newer companies or start-ups can afford.

4. Problem of Big Data

The big data conundrum is laced with two problems – abundance and lack of data. Abundance of data forces an organization to rely on sophisticated data analytics solutions and get insights for decision making. Whereas small set of data is insufficient to implement AI correctly. Applications such as speech and image recognition require deep learning and are successful when the machines are fed with huge amount of data. Here, big firms such as Google and Facebook are at an advantage. Other companies which might have excellent ideas but implementing them would be a mammoth task due to lack of data. Problems are more prominent in sectors where data is tough to gather such as healthcare. Computer vision used to predict cancer cells in the body requires more information than humans to understand concepts or recognize features.



Thus a shallow but expensive talent pool, unclear return on investments and lack of data are the major challenges faced by businesses in adopting AI. Companies looking to strengthen their business with AI should start with building a unique digital brand and establish partnerships with universities to bridge the expectation gaps. AI can be made affordable by partnering with technology firms having knowledge and infrastructure required to build robust AI models. Companies facing data hurdle should develop efficient machine learning systems which are capable of working with less data. Use of advanced data modeling techniques such as Bayesian program synthesis can make recognition of concepts in images or text much more efficiently.

