A common goal for all business organizations have been valuing addition and increased revenue. However, these come from a more efficient workplace and heightened customer experience. At the core of enterprises, today is technology and more specifically AI (Artificial Intelligence) that seeks to meet the ends. A recent study by Teradata reveals that 80% of modern businesses have implemented AI in some form to their business process – be it to streamline the business process or use analytics for better customer experience. The opportunities with AI are many. However, at the same time as many as 90% of these companies face barriers to AI implementation. For some, it about lack of infrastructure and for others, it’s the lack of talent and understanding of how AI works.
Enterprise AI Adoption – 10 Challenges Hindering our Way
Lack of IT infrastructure
The lack of proper infrastructure is basic but is also something that will likely be solved with time. AI specifically processes like deep learning and machine learning need a state of the art infrastructure. Processing power is one of the first of areas to invest into. AI is only answered by massively parallel processing systems through cloud computing and this is not something that every other organization would be able to integrate into their process. According to Forbes, “we are at least five, more likely 10, years from that”.
Lack of access to talent
Data focused companies have been deficient when it comes to the right brainpower. In one of such surveys by PwC, only about 20% companies have the necessary skills to handle the opportunities with AI. In most cases, the machine learning talent is restricted to a few departments but with increasing demand, this is also a problem that time would solve.
Lack of Understanding
AI comes with the potential of taking over a large part of the manual job, which is currently prone to errors and bound by time and efficiency. It’s true that a lot of AI applications today are highly specialized. The ability of AI is, however, dependant on the resources it draws from. This reason why AI needs to be taught to reach areas that are further beyond the lines of theories. The lack of specialized talents does add to this problem.
Impact on Customer Expectations
AI is provocative and not all businesses are ready with the right kind of demographics to cater to. Along with the skills of the organizations, the market as a whole to needs to come into sync with the powers of AI. AI does impact customer experiences a lot but “taking people by surprise” is not the right way to build trust.
AI Technology is still building and unproven
For many, Ai is still science fiction. This is mainly because AI has not yet reached the mark where it can affect and impact the life of the common man. While there is potential, there is still some time when it becomes a part of parcel of our lives.
Lack of Data
The only fuel to AI is data. The power of AI is largely dependent on the amount of data it receives and can analyze from. Though the world is on the way to become interconnected, there is still a limited contribution from the end users.
Lack of Adequate Budget
Barring the technology giants and big brands like Amazon and Apple, Ai proves to be too expensive to be implemented. Without an adequate budget, there’s a limitation to the infrastructure and thus the opportunities with AI.
Complications around Policies, Regulations, and Rights
In some way, this is highly true and relevant. AI is set to take over the jobs of their human counterparts. Building a world where a robot can do a job is not something that our nations are ready for, especially when many are still trying hard to increase employment rates.
Impact on Employee Morale
Extending to the previous challenge, AI does challenge the morale of its human counterpart. It’s complicated when we are building robots and scripts to take over our jobs!
Long Implementation Time
The fruits of AI implementation don’t show up immediately. Time is a factor but is not something that businesses would readily invest for.
While there won’t be a Skynet in the future, we are still confused about what AI can do and how it does it. Until ideas move beyond just theories, the challenges to AI would be persistent and test our patience.