The adoption of Artificial Intelligence (AI) has created a lucrative opportunity for techies around the world. Development teams working on AI share some common habits. McKinsey conducted a global AI survey to understand how companies are embedding AI in their businesses. Here are the top 5 things that successful AI teams perform.
1. Clear strategies
Companies that perform complex tasks in AI have clear strategies and plans to scale AI. These companies are likely to address key issues in business alignment and data. Nearly 72% of survey respondents from top-performing AI companies said that their company's AI strategy aligns with the corporate strategy. Over 65% of high performing AI companies report having a clear data strategy.
2. Multi-disciplinary approach
Successful AI programs require that companies create working teams with representation from multiple disciplines. Vodafone tried to build AI looking at cognitive engineers. The mix of required skills varies based on the flavour of AI.
3. Wide business problems
Companies don't look at implementing AI to solve the limited set of problem that they face. Most of these companies cast a wide net of use cases to determine how far the new AI-enabled solution can go.
4. Proof of value
When implementing AI, successful AI teams work with the key business stakeholders to get a clear idea about what KPIs the AI-solution will impact. Smart AI teams validate any quantifiable business outcome with a trusted financial executive or functionalists. For those projects where the work is more fundamental, the focus should be on verifying the link between initial investment and eventual ROI-generating applications.
5. User adoption and experience
The end goal of developing anything is to extract insights in real-time. It is important to find ways to make the solution easy and available. Most of the AI success lies in ensuring that users trust the system. Companies focus on user adoption and experience while working on an AI solution.
1. Clear strategies
Companies that perform complex tasks in AI have clear strategies and plans to scale AI. These companies are likely to address key issues in business alignment and data. Nearly 72% of survey respondents from top-performing AI companies said that their company's AI strategy aligns with the corporate strategy. Over 65% of high performing AI companies report having a clear data strategy.
2. Multi-disciplinary approach
Successful AI programs require that companies create working teams with representation from multiple disciplines. Vodafone tried to build AI looking at cognitive engineers. The mix of required skills varies based on the flavour of AI.
3. Wide business problems
Companies don't look at implementing AI to solve the limited set of problem that they face. Most of these companies cast a wide net of use cases to determine how far the new AI-enabled solution can go.
4. Proof of value
When implementing AI, successful AI teams work with the key business stakeholders to get a clear idea about what KPIs the AI-solution will impact. Smart AI teams validate any quantifiable business outcome with a trusted financial executive or functionalists. For those projects where the work is more fundamental, the focus should be on verifying the link between initial investment and eventual ROI-generating applications.
5. User adoption and experience
The end goal of developing anything is to extract insights in real-time. It is important to find ways to make the solution easy and available. Most of the AI success lies in ensuring that users trust the system. Companies focus on user adoption and experience while working on an AI solution.