Rapid 2-Week Program Helps Companies Identify and Prioritize Appropriate Business Cases and Remove Common Roadblocks to Implementation
Philadelphia, PA – November 8, 2018 – Anexinet Corporation, a leading provider of digital business solutions, today announced its new Machine Learning Kickstart Program. The Anexinet Team directs companies through a 2-week process to identify the best use cases for Machine Learning (ML), then build a road-map to achieve goals and overcome barriers to realizing a successful implementation for driving business decisions and processes.
According to a new update to the International Data Corporation (IDC) “Worldwide Semiannual Cognitive Artificial Intelligence Systems Spending Guide,” spending on cognitive and AI systems will reach $77.6 billion in 2022, more than three times the $24.0 billion forecast for 2018. The compound annual growth rate (CAGR) for the 2017-2022 forecast period will be 37.3%.
Despite the IDC growth projections, businesses still face many challenges when attempting to adopt machine learning. “Many organizations view ML as a distinctive advantage, but suffer a knowledge gap in their IT team’s ability to effectively implement,” said Anexinet Senior VP of Analytics and Machine Learning, Michael Golub. “To fill this gap, Anexinet enables organizations to quickly identify ML use cases and ready their IT team—with a comprehensive overview of the technical, process, and personnel decisions necessary to successfully execute ML, and a realistic action plan for measurable success.”
The Machine Learning Kickstart program removes one major roadblock businesses experience when investing in these technologies: the availability of experienced IT staff with the know-how to map machine learning into the business to ensure a successful outcome. Anexinet addresses this ML issue, with their new 2-week Kickstart approach, conducted in 4 steps:
- Direction Setting: Establish goals, align expectations with business value and drivers, and define the metrics for success.
- Ideation & Concept Generation: Identify and prioritize opportunities for machine learning and score them in a Scenario Matrix based on their alignment with business drivers, organizational readiness, and ease of implementation.
- IT Readiness Assessment: Produce a report that evaluates the IT team’s current capabilities and its ability to successfully execute on a machine learning strategy.
- Roadmap & Recommendations: Provide a readiness roadmap to fill any gaps and phases, while outlining how to prioritize IT projects and tactics based on their alignment to business needs.
For more information, please see Anexinet’s Machine Learning Kickstart at https://insights.anexinet.com/machine-learning-strategy-kickstart.
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