Artificial intelligence (AI) is the crown of every tech-powered business enterprise — whether small or big. And embracing new opportunities with AI is something every business must do to stay relevant in their industry. Implementing artificial intelligence in business will provide a direct impact on the success of the companies ranging from improved decision-making to better use of the extensive data generated.
However, business-friendly it may sound; the path to implementing artificial intelligence in business is not a smooth ride. While larger businesses find themselves in a better position, the same cannot be said about startups. There are some typical challenges that startups face when it comes to implementing AI in their organisation. In this article, we will discuss six such challenges to implement AI in startups vs in larger organisations.
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 perspective 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.
Another issue with AI, like any other emerging technology, is its associated hyper optimism, which leads to business working without a clear ROI framework or tracking towards impossible goals. It’s usually the new managers and the leaders of startups who suffer from hyper optimism by reading unrealistic studies that are promoted by vendor companies and starts to believe that they are falling behind the curve. In fact, to correctly deploy AI applications, startups require specialists who have a deep understanding of its current advancements.
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.
Currently, there’s a huge AI skills gap, which can significantly impact startups wanting to dive into the industry and develop in-house AI marketing solutions. Even startups who are using readymade AI solutions will also need to ensure to have sufficiently skilled and trained employees to deploy and manage it, and to interpret the results correctly. While in some cases, this skills gap can be filled by deploying training modules for your existing employees, whereas other startups are allocating a considerable budget towards attracting AI specialists with a competitive salary package. This, in turn, puts pressure on the existing budget or will create a need to convince the higher management to invest more money into AI, which usually startup leaders are reluctant to do.
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.
Getting the right data and using the right tools can be some of the major challenges for an AI startup. It is therefore important to have a trust in the technology and strike the right amount of balance into developing AI products.
Computing Not That Advanced
To use artificial intelligence, along with machine learning and deep learning solutions, require heavy pieces of machinery and advanced computers to solve problems at hypersonic speed. And to create such a high speed of workflow, businesses require advanced processors, which becomes a huge problem for startups because of their budget constraints. However, to have a short-term solution, cloud computing and massively-parallel processing systems have catered to startups’ requirements. But, the real problem arises when the volume of the data continues to grow, and deep learning brings in more complex algorithms into existence. And to solve this issue, startups need to deploy next-gen computing infrastructure solutions, like quantum computing, which works on superposition concept to perform operations on data far more quickly than today’s computers. In fact, lack of scalable product and experienced AI founders may further raise questions on the long term sustainability of the startup.
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.
However, the industry found an alternative solution to get around this problem. While larger businesses may opt for developing and running in-house AI marketing software, startups with less impressive resources can always opt for cheap cloud-based solutions. For startups, cloud software vendors become extraordinarily beneficial, as it provides all the IT infrastructure that is required to run AI software. These cloud services are an obvious solution for businesses with insufficient IT infrastructure to build in-house systems.
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 to have 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.
Startups can collect their data on various applications with a multitude of formats such as text, audio, images, and videos; however, a wide range of platforms to collect this data adds to the challenges of artificial intelligence. In order to be successful, all this data must be integrated in a manner that the AI can understand and transform into useful results. Nonetheless, with time, startups are investing in designing methodologies and focusing on how to create AI models despite the scarcity of labelled data.
