Why planning for AI disruption should be a top priority

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According to Gartner, AI Augmentation will create $2.9 trillion of business value and 6.2 billion hours of worker productivity globally with a forecast that AI could add $15.7 trillion to the global economy by 2030.

Every indication points to AI disrupting almost every industry, by 2030 or perhaps earlier. AI will spread not just to every industry but throughout entire organisations and just like those who were lethargic in their adoption of the internet, those who are not planning for the coming AI disruption will inevitably suffer.

A recent report by Accenture has shown that currently only 16% of companies have AI working for them at scale, with many companies stuck in dead end pilots, that is not from a lack of trying however. That number is sure to increase with trial and error but in the race to gain competitive advantage through AI their will undoubtedly be winners an losers.

According to McKinsey and Company, AI companies who fully absorb AI tools across their enterprises will see a substantial performance gap between them, non -adapters and partial adapters.

With that said here are 6 key considerations to becoming an AI winner.

Keys to becoming an AI winner

1. Integrate AI strategy with business strategy

Tying AI with business strategy ensures that AI initiatives get the right focus across the organization. Linking the two helps companies zero in on initiatives that bring or facilitate the most important outcomes. That makes for a savvier, much more effective allocation of AI talent and resources.

2. AI education throughout the entire organisation

This should include a deep dive into what makes for a successful A.I. use cases versus those that are unsuccessful, how to overcome obstacles to success, and how to speed the path from concept to value.

3. Align the production of AI with the consumption of AI

Getting the most out of AI requires a team effort. A good rule of thumb is to consider AI to be 10% about algorithms, 20% about technology, and 70% about business process transformation. Companies that focus solely on the production of AI—leveraging data, technology, and tools to build solutions—are less likely to derive value than companies that enable the consumption, or usage, of AI.

4. Trustworthy AI

A.I. needs to be human-friendly, intuitive and easily understandable. Companies must protect themselves from inadvertent bias or errors and ensure that A.I. is working as it's expected to. Enabling technology must produce A.I. that everyone in your organization can explain and defend, and that is consistent with your ethics and values.

5. Invest in AI talent.

How do AI leaders tackle the AI talent shortage? The best approach is a combination of hiring new talent, cultivating AI skills in the existing workforce, and looking to outside experts. If your organization already has data scientists, try to make them even more productive by automating as many of the manual, repetitive tasks as possible so they can focus on delivering the highest value.

6. Monitoring and Governance

Because machine learning models drift over time and data changes quickly, a centralized machine learning operations (MLOps) and governance center as a complement to traditional DevOps is becoming more critical to helping get a greater number models into deployment.

Become an AI front runner

Identify and execute on as many opportunities that are justified by the business value and practicality, but remember that AI is not all about technology. It’s much more about people, processes, culture, and business strategy. Companies that bring all the pieces together don’t just build AI—they build the right AI.

Alldus International is a specialist Data Science & AI Recruitment Company with offices in Ireland, Germany and the US. Whether you are looking for your next position or hiring talent, Alldus has you covered.

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