How Industry Experts Adjudge The Automation-AI Adoption And Challenges?

The time has finally arrived when AI and automation technologies have attained the power and are impacting real-world applications – facial recognition, intelligence in robots, language translations, to name a few. Meanwhile, companies are harnessing AI advents in their business operations. AI adoption assures considerable benefits for business and economy both through its contribution to innovative growth.

If so, then would it be right to put up a question – Do Challenges of AI and Automation Adoption Outdo Their Good Deeds?

Well, a bench of technology police is framing its opinions and expertise adjudging the current scenario of technology embracement. According to the experts, other than data quality and quantity generated per day, human workforce issue is also a challenge while implementing AI and automation enablers into a workstation.

Radical AI-Automation Adoption Challenges Leaders Face Today

To behave like a human, machines have to learn like humans through the information extracted from huge datasets. Managing the data quality is not the only problem for professionals but bringing the data together from varied systems that they are looking to learn from is the greater deal. This makes them rethink the procedure of data integration because it was not the way it had been done before.

Such revision process gives rise to the collection of challenges including bringing data together, identifying the data, cleaning the data and teaching off through it. All this preparation procedure impels the leaders to revise the whole stack.

The other significant challenge in a row is a human adaptation. Often with AI projects, instead of the bottom-up approach, while working within the layers of human tasks and their understanding, the projects tend to become a top-down approach. In such cases, the work generally stops after the implementation phase.

The Key to Prevent Cynical Judgements

The best prevention key from skeptical opposition are clear business drivers and use cases with specific goals. Certain companies and leaders try to fit in someone else's shoe thinking it would be great without even defining their own needs and goals for it. Therefore, clear business drivers and use cases with predefined goals while implementing AI help the business grow beyond challenges in real sense.

Considering Operation Efficiency, the Core of Technology Implementation

The apt reason to opt for automation and embracing new ML and AI technology tools should be for operational efficiency of a business. Operational efficiency is the key to protect one's domain, up-level employees and entice clients with projects.

Some experts do believe that as the companies have continued to encompass emerging and innovative technologies but the addition hasn't brought any enhancement to productivity. Rather it has made the workforce less productive. According to them, if a company thinks of embracing automation this hour it should bring and rebuilt the productivity of the workforce as well.

Hence the purpose of adopting new-age technologies and enablers should be to accelerate the productivity and efficiency of a business and its employees rather than just being a new cool fashion in the trend. It's high time that companies should focus on areas of gain instead of worrying about areas of pain which would use disruptive technologies for a quick fix.