by Lack of Business Ailment
Although the majority of companies nowadays use machine learning, that doesn’t make them an AI company. In order to be a real AI company, businesses need to have a system based on self-learning algorithms and should be able to make their own decisions.
For startups, to identify business use cases for artificial intelligence applications, require a deep understanding of current AI technologies, the limitations and the correct usage in the business. For an AI system to show positive results, it requires the right blend of NLP, deep learning and related tech, which most startups fail to achieve. Such startups may lose their relevancy and perspectives overtime, hence never get the chance to scale up to the mark. Experts believe, in most cases, that the lack of knowledge can majorly hinder the adoption of AI in startups.
Poor IT Infrastructure & Insufficient Funds
An AI technology processes huge amounts of data and therefore needs high-performing hardware. In order to drive a successful AI-based marketing strategy, startups require a robust IT infrastructure and advanced computer systems behind it, which can be very expensive to set up and run. These systems also likely require frequent maintenance and updating to ensure a smooth workflow. And, this can be a significant stumbling block for startups and smaller companies with more modest IT budgets. While large companies like Facebook, Apple, Microsoft, Google, Amazon have separate budget allocations for AI implementation, it becomes difficult for startups and smaller companies to implement AI solutions to their business processes.
Lack of Right Talent & Resources
To succeed in your startup business, companies need the right blend of different talents. Similarly, an AI startup demands expertise and resources that are more science-focused and who has an interest in playing with complex models with a lot of math and problem-solving skills. Some of the key favourable skills are physics, robotics, cognitive and computer science with a definite focus on machine learning. It takes immense patience and endurance to build a startup, and the relevant talent supply may still be undeveloped, which leaves most startup companies bereft of the right talent to deal with emerging technologies. Since the right people are at the core of driving growth, a lack of availability of right resources may be a deterrence in implementing AI in your startup.
Access to Data and Product Focus
For eCommerce you need execution skills. That means you want to grow fast and secure a market share. Once you can scale the business, your success depends on making fewer mistakes and securing more funding than the number 2 in the market.
For fintech and healthtech you need to build trust, seek cooperation with established players and solve the complexity of heavy regulations.
SaaS startups need to own their users with amazing usability, since they often replace excel or paper & pen.
Lack of Trust & Patience
AI is a relatively new technology and is somewhat complex. It usually takes a considerable amount of time to develop an AI system, and it is normal to wait up to at least two years before the system could actually generate its first revenue. This gap between the theory and real implementation is huge, which makes it a huge challenge for startups who are wishing to see some profits from the first day of their business. Unlike larger companies, it also gets quite frustrating for startup founders to wait forever for any ROI from their investment. Product development in AI demands an extensive interaction with potential customers to understand their problems and accordingly train models, which, in turn, is a time and capital intensive task. It sometimes gets even challenging for startups to find the right balance between research and its application.
Data Scarcity
It is a fact that business has access to more data in the present time than ever before; however, the datasets that are applied to an AI application to learn are rare. Although the most powerful AI machines are those that are trained on supervised learning, this training usually requires labelled data which has a limit. And therefore, for businesses, automated creation of increasingly difficult algorithms will only worsen the problem. For a business to successfully implement AI strategies requires having a basic set of data and needs to maintain a constant source of relevant information incoming in order to ensure that AI can be useful in their industry. For a startup, this becomes a huge hurdle, as there is a massive scarcity of relevant data available for them.
