Entrepreneurs and experts at the front lines of the AI revolution recognize there are issues like a company’s culture or lack of trust from a customer base, which cannot be solved by technology alone. These are fostered by principles that shape the everyday inner and outer workings of a company.

 

Copyright: enterprisetalk.com – “How to Make Enterprise Comfortable With Artificial Intelligence”


AI is a powerful mechanism for amplifying human knowledge, skills, and efficiency. But can AI proponents employ AI to fix a moribund or toxic corporate culture? That’s probably the most vexing challenge with AI rollouts.

One of the challenges that Artificial Intelligence models are based on historical data, which means they are prone to biases that are ingrained into the algorithm while their learning phase is on. So basically, humans pass on their biases to AI applications. Sometimes, an automated process doesn’t take into account the people it works with.

The challenge, then, is to put people first in any AI project. AI practitioners make the following recommendations for building a people-centric, but AI-driven culture:

Also Read: Organizations to Adopt a Multi-lingual Approach to Knowledge Management Capabilities

Expand AI ownership and commitment beyond the IT department

AI adoption in an enterprise should be a comprehensive business plan, with all parties involved. Successful distribution and production of AI is an effort for a variety of activities away from data science. Extended teams need to move from the technical side, involving IT and cloud operations with data security and control, to the business side, which includes change management, educational training, acquisition, best practice.

AI does not attempt to replace human beings but empowers human-like dialogue with the power of automation and ingenuity a machine can have.

Identify AI in areas where it has the greatest impact

The enterprise components of the promotion and delivery of AI are very different across industries, Wu said. But the common theme is that organizations should have a reliable source of clean and rich data as a product from normal business operations. Companies with large support centers often keep a good record of events and decisions. Activity data for sales organizations is usually as accurate as needed for good accounting practices. This data will continue to add fuel to their AI / ML as it reads. On the other hand, although marketing organizations also have a lot of data, they are often noisy and often need to be cleaned up before they can be used in AI and ML production. […]

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