The AI Revolution Is Here - Are Businesses Ready for It?

The way people interact with the world is changing in the era of widely accessible AI tools, such as the latest trending chatGPT language model. AI adoption in certain business areas globally has more than doubled since 2017.

McKinsey's research over the past five years states that AI adoption and investment are steadily increasing, and this is predicted to increase at a similar rate over the next three years.

AI leaders are engaging in practices that help execute AI successfully, where AI is driving revenue rather than reducing costs. However, AI tools also present notable risks to organizations.

Are businesses ready for the AI revolution? Here’s what AI leaders do differently:

Factors that determine successful AI implementation

Organizations that receive the highest returns from AI are linking AI strategy to business outcomes. The success of these efforts also depends on how well they manage AI risks. These could include ensuring AI and data governance, standardizing processes and protocols, automating processes to remove errors introduced through manual work, testing the validity of models, and monitoring them over time for potential issues.

Some other factors include:

●?????Practices are well aligned with AI integration: AI leaders will have data architecture and automated date-related processes that are modular enough to accommodate new AI applications rapidly.

●?????More expenditure: AI high performers are over five times more likely to spend more than 20 percent of their enterprise-wide revenue on digital technologies.

●?????Bigger AI skills pool: They report sourcing AI-related talent in broader ways, and look for more ways to do skilling and upskilling of existing staff. For example, they will create capability-building programs to develop technology personnel’s AI skills or use more experiential learning to develop skills.

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Mckinsey further found that while AI use has increased, there has been no substantial increase in reported mitigation of any AI-related risks from 2019 until now, to maintain digital trust.

Tips to use AI tools at work

●?????Create an internal AI department: An in-house AI team will help you develop your AI projects, where you can allocate AI talents into specific divisions, allowing you to upskill and retain your existing employees. New AI-related teams and job descriptions will need to be created by adding scientists, data engineers, and machine learning engineers to your team.

●?????Start small and experiment: It’s important to test your artificial intelligence strategy and create a small number of artificial intelligence resources that are difficult to implement, and tailor these resources so that they give you a competitive advantage in your industry.

●?????Regulation of AI practice: If you want to implement AI projects in hypersensitive industries, where compliance and regulations are mandatory, you will need to assign who is accountable for these regulations. You will need to answer questions such as who is responsible for the AI system, are compliance controls in place, and who takes responsibility for actions when AI makes a mistake.

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Therefore, potentially, businesses might not be ready for AI from a risk management point of view.

In part 2, we’ll unpack some of these risks that businesses may encounter.

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Article written by The CURA Content Team