1. Collecting Data
Data collection has always been a headache and in 2019 companies have access to more data than ever before. With this unprecedented access to data, many companies are having trouble finding talent to sort through it all.
Every time there’s a technology innovation that sweeps across all large companies, there’s always the same problem, talent. Data has unlimited potential if you can analyze it. While some claim AI and machine learning can solve this problem, there will still be the problem of hiring those who know how to implement it effectively. These new hires must have the drive, knowledge, and skill to realize this potential.
“Predictive analytics and machine learning will transform business processes.” – Elliott, Innovation Evangelist, SAP (Digitalistmag.com)
According to the McKinsey Global Institute’s discussion paper on Skill Shift Automation and the Future of the Workforce
(www.mckinsey.com), the US will have a skill shortage of up to 250,000 data scientists in the next couple of years. Data analytics is not an easy process. Even using AI has its own problems such as access to highly qualified and well-trained talent.
CIO’s will need to overcome these issues if they want to utilize their data in a valuable way.
AI and machine learning are still the cutting edge in analytic innovation, and it requires talent. The barrier to implementation is finding talent that is bold, ambitious, and people you can trust. With the lack of new hires looming over companies, signification investments will have to be made to ensure a competitive advantage.
If you want to learn more about this lack of talent, check out our blog on Skill Shifts predicted by the latest MGI Report (https://cxonexus.com/will-ai-take-your-job-skill-shifts-and-ai-adoption/)
“AI can yield benefits today but is held back by a lack of talent, trust, and ambition.” – Nigel Duffy, Global AI Leader, EY (Digitalistmag.com)
2. Using Data
Just like collecting data, using the data you have can be a headache. Once tapped, big data can provide insight into potential profit, better customer service, less overhead, and many other benefits. CIO’s are scrambling to find the best solutions to their accumulating data.
There are still innovations to be made before AI and machine learning systems take the step from descriptive analysis and prescriptive actions. If a company can make it over this hurdle, there is unlimited potential with the data they are already collecting. Companies that focus on collecting big data can see the potential their data holds and according to MGI, they will have a compounding competitive advantage if they are first to market with these solutions.
“Big Data analytics will move from descriptive to predictive.” – Bill Schmarzo, CTO, IoT and Analytics, Hitachi Vantara (Digitalistmag.com)
One of the problems with using the data you have is time. Real-time solutions to data analytics are hard to come by. AI and machine learning platforms are the go-to solutions to this problem. These platforms allow for reliable, real-time analysis and actionable data.
Real-time data analysis is the new frontier in AI innovations. Optimizing operations, perfecting processes, and maximizing profit can all be achieved by prescriptive and real-time analysis. From mining companies improving workflow to companies like us, CXO Nexus, giving power back to companies over their vendors through CIO focused real-time vendor spend data.
“Management teams will focus on smarter data management.” – Peter Aynsley-Hartwell, CTO, Utopia Global (Digitalistmag.com)
3. Application of AI
One of the worst headaches for CIO’s and IT teams is the implementation of new systems such as AI. Everything is a trade-off when it comes to implementation. Software that delivers everything you need in a reliable, streamlined, real-time way requires a complex and lengthy process. While on the other hand, simpler solutions lack the value offered by the more complex processes.