Artificial intelligence (AI) is the toast of every tech-powered business enterprise. Business establishments are taking AI to integrate a multitude of transformative opportunities to leverage the value chain. However business friendly it may sound, the path to adopting AI has not been a smooth ride. As per the latest reports from Deloitte’s 2017 State of Cognitive survey, only 6% of business enterprises are having a smooth ride with AI. This leaves a staggering 94% that face challenges in implementing an AI solution to its enterprise. Here are the top challenges that enterprises face while implementing an AI solution to its business process.
Lack of Technical Know How
To identify the deployment of AI applications, business enterprises require specialists who have a deep understanding of the current AI technologies, its limitations and the current advancements. AI being a niche domain, the lack of AI know-how in management is hindering its adoption in most cases.
Another roadblock for emerging technology adoption is hyper-optimism, which makes business teams work without an ROI tracking of AI adoption, towards goals that are far to achieve. The lack of skilled human resources who could implement AI/ML solutions to business is another cause of concern.
The Cost Factor
AI technologies are an expensive deal to an organization. While big names like FAMGA (Facebook, Apple, Microsoft, Google, Amazon) have separate budget allocations for AI implementation, it is the small and mid-size enterprises that struggle to implement AI solutions to their business processes. Comparing the multi-trillion $ AI opportunity analyzed by consulting majors it is obvious that AI talent is a costly expense.
Data Acquisition and Storage
Data acquisition and storage is a real challenge in AI implementation. Industrial AI systems depend on sensor data as its input. The humongous amount of sensor data collected for AI validation may present noisy datasets that are difficult to store and analyze, thus causing an obstruction.
Expensive Human Resource
AI needs a human resource that is trained specifically to its adoption. Data scientists, data engineers and subject matter experts in today’s market are rare and expensive. The organizations which have tighter budgets are not in a position to hire the talent according to their project requirements. Hence that causes an impediment.
The popularity of AI has raised a number of challenges that arise out of ethics and morality that are yet to be addressed. AI bots are increasingly mimicking human conversations to perfection. The humanoids like Sophia, Junko Chihira, Nadine have perfected human emotions and caricature that is eerily true to accept. It is becoming increasingly difficult to ascertain whether the customer service rep we are chatting with is a human or a machine. This possesses an ethical and moral challenge, which makes the AI solution a tough technology to implement.
Lack of Computation Speed
AI and the promising machine learning and deep learning solutions demanded by the market, require a huge number of calculations to be computed at hypersonic speed. This requires processors that have much advanced processing power much higher than what is in general adoption today. As a short-term solution, cloud computing and massively-parallel processing systems have catered to business requirements. The problem arises, as data volumes continue to grow, and deep learning brings more complex algorithms into existence. The answer is hidden in the development of next-generation computing infrastructure solutions, like quantum computing that is based on quantum superposition concept to perform operations on data far more quickly than today’s computers.
Though AI is an expensive retreat requiring a human resource that is hard to find and requires supercomputer processing speeds for a successful adoption, one cannot let go the revolutionary changes it is bringing to an organization. Rather stay back weighing the negatives, organizations should focus on how they can responsibly reduce the ill effects of this path-breaking technology. The key lies in minimizing the challenges and maximizing the benefits through the creation of an extensive technology adoption roadmap that understands the core capabilities of artificial intelligence.