Challenges for Startups in Adopting AI and Data Analytics


The influence and impact of Artificial Intelligence (AI) on our lives over the last decade has been astonishing. Based on various forecasts, Artificial Intelligence will add close to $15.7 trillion to the world economy by the year 2030.

However, great implementing AI may sound, the path to deploying AI is not as smooth. While larger organizations find it easier to implement AI, the reality is different for start-ups. Read on to understand how and why and start-ups find it tough to implement AI and data analytics.

Challenges Start-Ups Face while Implementing AI

Let us understand the challenges that organizations face while trying to implement AI.

Lack of Business Ailment

Although most organizations use Machine Learning (ML), that does not make them an AI organization. To be an AI company, businesses are should have a system of self-learning algorithms. These algorithms should be able to make their own decisions. Startups require a complete understanding of current AI technologies, the restrictions and the right usage in the business. For an AI system to show positive results, it needs the right blend of NLP, deep learning and related tech, which most startups do not have. Such startups could lose their relevancy and perspective overtime, thus never get the chance to scale up to the mark. Experts believe that in most cases the lack of knowledge can significantly hinder the adoption of AI in startups.

Lack Of Right Talent and Resources

To be able to succeed in a startup business, organizations require a blend of different talents. Similarly, an AI startup requires resources and expertise that is more science-focused. Some of the main favorable skills are robotics, physics, cognitive and computer science with a clear focus on ML. Since the right set people are essential for driving growth, a lack of availability of right resources may prevent AI deployment in your startup.

Lack Of Trust and Patience

AI is relatively a new technology and is somewhat complex. It takes a considerable amount of time to create an AI system. Also, it is quite normal to wait up to a minimum of two years before the system starts generating revenue. This timeline is a huge challenge for startups who wish to see some profits from the first day of their business. Unlike bigger companies, start-ups cannot wait for this long to see any ROI from their investment.

Inefficient Computing

To use AI, along with ML and deep learning solutions, require huge pieces of machinery and advanced computers. And to create such a high speed of workflow, businesses require advanced processors. This becomes a huge problem for startups due to their budget constraints.

Insufficient Funds and Poor IT Infrastructure

An AI technology processes lots of data and therefore requires a high-performing hardware. To drive a successful AI-based marketing strategy, startups need a robust IT infrastructure along with advanced computer systems. Such infrastructure can be very expensive to install and run. These systems also require frequent updates and maintenance to ensure a smooth workflow. This can be a significant stumbling block for startups and smaller companies with more modest IT budgets.

Data Scarcity

Businesses have more access to data in the present times than ever before. However, the datasets that are applied to an AI application to learn are not common. Although the most powerful AI machines use supervised learning, this training usually needs labelled data — which has a limit. For a startup, this becomes a huge challenge, as there is a huge scarcity of relevant data available for them.


It is unavoidable for businesses to adopt a high-value data analytics system. There is no reason that that startups cannot be a part of the data transformation that is impacting almost every industry sector today. All it takes is an initial small-scale investment in a reliable data storage platform.