CIOs seeking big wins in high business-impacting areas where there’s significant room to improve performance should review their data science, machine learning (ML), and AI projects.
A recent IDC report on AI projects in India[1] reported that 30-49% of AI projects failed for about one-third of organizations, and another study from Deloitte casts 50% of respondents’ organizational performance in AI as starters or underachievers.
That same study found 94% of respondents say AI is critical to success over the next five years. Executives see the AI opportunity for competitive differentiation and are looking for leaders to deliver successful outcomes.
ML and AI are still relatively new practice areas, and leaders should expect ongoing learning and an improving maturity curve. But CIOs, CDOs, and chief scientists can take an active role in improving how many AI projects go from pilot to production.
Are data science teams set up for success?
A developing playbook of best practices for data science teams covers the development process and technologies for building and testing machine learning models. Developing models isn’t trivial, and data scientists certainly have challenges cleansing and tagging data, selecting algorithms, configuring models, setting up infrastructure, and validating results.
Leaders who want to improve AI delivery performance should address this first question: are data scientists set up for success? Are they working on problems that can yield meaningful business outcomes? Do they have the machine learning platforms (such as NVIDIA AI Enterprise),infrastructure access, and ongoing training time to improve their data science practices?
Continue reading: https://www.cio.com/article/411198/how-to-launch-your-ai-projects-from-pilot-to-production-and-ensure-success.html
A recent IDC report on AI projects in India[1] reported that 30-49% of AI projects failed for about one-third of organizations, and another study from Deloitte casts 50% of respondents’ organizational performance in AI as starters or underachievers.
That same study found 94% of respondents say AI is critical to success over the next five years. Executives see the AI opportunity for competitive differentiation and are looking for leaders to deliver successful outcomes.
ML and AI are still relatively new practice areas, and leaders should expect ongoing learning and an improving maturity curve. But CIOs, CDOs, and chief scientists can take an active role in improving how many AI projects go from pilot to production.
Are data science teams set up for success?
A developing playbook of best practices for data science teams covers the development process and technologies for building and testing machine learning models. Developing models isn’t trivial, and data scientists certainly have challenges cleansing and tagging data, selecting algorithms, configuring models, setting up infrastructure, and validating results.
Leaders who want to improve AI delivery performance should address this first question: are data scientists set up for success? Are they working on problems that can yield meaningful business outcomes? Do they have the machine learning platforms (such as NVIDIA AI Enterprise),infrastructure access, and ongoing training time to improve their data science practices?
Continue reading: https://www.cio.com/article/411198/how-to-launch-your-ai-projects-from-pilot-to-production-and-ensure-success.html
