Up until now, investment in Artificial Intelligence (AI) has been the preserve of the very largest of organizations. But, as research shows, that now looks to change. Forrester predicts that 70 percent of enterprises will implement AI over the next 12 months, up from 40 percent in 2016 and 51 percent in 2017.
What's driving this change, and how can cloud service providers (CSPs) seize this opportunity to grow their business?
Drivers for Change
For the first time, data and compute are coming together to create the optimum conditions for mainstream AI to flourish, supported by real-world evidence of successful AI applications:
1. An exponential increase in the number of Internet-connected devices means there is more data available in the cloud than ever before.
2. Advances in processing power and modern computer models, such as cloud computing, have made it possible to analyze these vast quantities of data at high speed.
3. Evidence of AI's transformative power is emerging all around, from medical use cases through to virtual assistants to autonomous driving.
And with enterprises convinced of the benefits of AI, and with the necessary data and processing power in place, everyone can roll out AI tomorrow, right?
Seventy percent of enterprises will implement artificial intelligence over the next 12 months, up from 40 percent in 2016 and 51 percent in 2017.
Opportunities for CSPs
AI still requires hard work, as well as significant technological and cultural changes, and this is where CSPs can add value:
1. Helping customers overcome the top barriers to AI adoption: underdevelopment of IT infrastructure, talent shortages, and not enough investment.
2. Supporting customers with cleansing and organizing the data for use in their machine learning and deep learning algorithms.
3. Aggregating customers' edge device and sensor data, blending it with other data sources and applying big data analytics to deliver insights back to the customer.
4. Storing the data that customers use to train their AI, so they are still able to develop their AI algorithms without having to store vast amounts of data themselves.
5. Working with customers to create algorithms so they can still take advantage of AI despite not having the capacity or expertise to build their own AI capabilities.
6. Offering continual AI as-a-Service based on their customers' most pressing needs – for example, an AI test environment, an image classification solution, or an AI speech recognition system.
Building an Offering
Once CSPs have identified the right AI solution to grow their business, the next step is to ensure that their infrastructure is capable of providing a solid foundation for building a compelling AI proposition. Broadly speaking, CSPs should focus on:
1. Improving the scalability of their infrastructure.
2. Selecting the right frameworks for their AI projects.
3. Ensuring their storage systems are architected correctly to support ML and DL workloads.
4. Improving the performance and speed of the platforms that will be running ML or DL algorithms.
5. Continually re-evaluating the AI technology landscape to ensure they are deploying the best possible mix of infrastructure, tools, and processes for their circumstances.
For more detail on the steps needed to put in place the infrastructure to support AI workloads, as well as the Intel® technologies that support this, download our latest eGuide: Artificial Intelligence: A Guide for Cloud Service Providers.