Bottlenecks in deploying AI for Enterprises
It is important to note that Artificial Intelligence is not a buzzword anymore. The COVID-19 pandemic has accelerated the adoption of Artificial Intelligence and related digital technologies across the globe. And Indian enterprises have not missed out, as according to a report by PwC, India has witnessed the highest increase in AI adoption amidst the pandemic compared to major economies such as the US, UK and Japan.
With this rapid adoption, things are changing very fast in the Indian enterprise ecosystem. More enterprises are open to embracing digital transformation and lean on Artificial Intelligence, RPA and NLP technologies to combat the challenges brought on by COVID-19. And given the uncertainty in business operations due to the pandemic, AI is now an integral part of an enterprise ecosystem enabling front & back-office transformation in terms of enhanced customer experience, automation of data-intensive processes, reduced operational cost and much more.
With so many benefits, the potential of AI is limitless. However, many enterprises are still holding back to embracing AI as implementing and scaling AI has its own set of challenges. These include the absence of a clear strategy, measuring AI’s ROI, lack of data, skills shortage, and identifying areas to implement AI. In addition, while there is a clear indication that businesses are moving to deploy AI at scale across their infrastructures, only a few have their strategy in place to scale and enable AI at work.
Here are some of the most common concerns enterprises face while deploying AI:
- Vendor selection- Selecting a suitable vendor for your digital transformation is a cumbersome job. No matter what vendor you choose, it is important to understand the pain points within your organization across different departments. Once that is done, compare the needs of your enterprise with the AI vendor offerings to make a decision that will drive results and ROI.
- Company culture & goals — Change can be scary and unsupportive culture plays an essential role in AI adoption. Before rolling out an AI process, it is necessary to include the team in the process and explain the results they may achieve after AI adoption. For example, requesting a demo of a solution from the vendor for all the departments involved in the process, running a pilot program and then scheduling a feedback session with each department.
- Skill shortage- Another key concern that enterprises face while deploying AI is the shortage of the right skill set and technical staff to deploy and operate AI solutions effectively. Industry research has also suggested that experienced ML modellers and Data Scientists are in short supply.
- Lack of data — AI and ML tools rely big time on data, and lack of clean, meaningful data can affect the success of AI initiatives. But most of the enterprise data is outdated, noisy and unstructured, while many don’t have enough volume or quality of data. Therefore, for an AI initiative to be successful, data quality, management and governance is of utmost importance.