Major Industry Trends Driving AI and Graph

This is an abbreviated version of a presentation by Stela Solar, Global Head of Artificial Intelligence Solution Sales at Microsoft during the Fall 2021 Graph + AI Fall Summit conference.

Stela Solar, Global Head of Artificial Intelligence Solution Sales | Microsoft

Watch the full session from Graph + AI Summit Fall

In this blog post, we’ll explore how to kick into the next gear of AI value and innovation that you can get within your own organization.

The COVID-19 pandemic has had some turbulent effects on AI adoption over the last year and a half or so. The rate of adoption of machine learning has accelerated to 80% of organizations implementing some kind of machine learning in a production environment, which in 2019 was just 55%. Additionally, 47% of organizations that McKinsey surveyed felt that the pandemic has accelerated their own AI projects, as they’ve been trying to work out to navigate the world around them. 

The very first trend organizations are implementing is knowledge mining. For example, how do organizations enable all of their knowledge workers to do their best to have access to structured data, and unstructured data such as audio, images, video, and much more? 

The second pattern is around conversational AI. By 2030, it’s estimated that 30% of all eCommerce will be through voice. So a trend that we’re seeing is moving from chatbots into voice bots and now into neural voice and thinking about customizing that voice and bot interaction that is unique to a brand of that organization. 

So a third pattern that we’ve seen taken off as well is document process automation. This fundamentally is driven by a huge pressure to drive cost savings and drive greater efficiency through processes. By some estimates, up to 46% of IT costs could be saved by automating some of the processes. 

One other trend we’re seeing is about 80% of organizations are now adopting ML in production, yet the opportunity is much bigger. I want to focus on that today – the trend that we’re seeing when many organizations want to leverage the benefits of AI, the new experiences, the intelligence, the automation, and the amazing applications that can be built with AI. 

Increasing AI’s Value

Let’s dive deeper into four areas that I want to propose how you can kick into the next gear of AI value and innovation in your organization. The first one is AI at scale. Many organizations are implementing AI for a particular use case, but how to do it at scale is the competitive frontier right now for many organizations in the market. The second area is data capabilities and culture – where there is AI, there’s always going to be data as well. The third one is developer velocity, putting the tools or all of these technology enhancements into the hands of developers and then ensuring that you’re empowering the developers to be creative and innovate rapidly. That is a core component kicking into the next gear about AI. Underlying all of this is how to do this in a super responsible way. That is upholding the value of your organization and contributing as a responsible corporate citizen in the global environment. 

There are certain organizational factors of getting AI into scale production. One really key dependency is thinking about the data capability and culture because where there is AI, there’s always going to be data. If we look at data-driven transformation, particularly over the last year and a half to two years, we have seen that the organizations who have brought together their data and harnessed it have fared better over the past year and a half; and so 62% in customer satisfaction for organizations who are harnessing that data-driven opportunity. Also, 54% increase in profits, and 44% faster time to market. These are the kinds of benefits of bringing that data power together, which is a foundation to enabling AI value over the top. 

However, it’s not so easy, right? One of the questions that comes up is how to break through the silos. This is where graph can be so helpful – because graph connects data in a much more nuanced, informative way, and get to the insights faster. All of these questions have led us into two key areas thinking about data capabilities and data culture. 

We see data capabilities in a few core areas: 

• The data state and ensuring that it is modernized so that you can connect all of that data and harness the insights that come from there
• Cloud-native applications 
• Analytics and insights
• Governance. 

The second element, data culture, has us thinking about three core areas:

• How do we expect data-driven decision-making at all levels from all roles? 
• Democratizing access to the data – how do we allow every single individual to have access to the data that they need to make the best decisions they can within their contexts and priorities? 
• How do we keep focusing on building skills in our people? 

Developer Velocity and Responsibility

The next topic crucial for organizations and AI is developer velocity. There are so many new capabilities for developers with AI. Creating the right environment and allowing them to have access to drive business performance is vital for AI adoption and effectiveness. Organizations with a high developer velocity index have higher revenue growth (4-5%), and higher innovation (55%). 

And to wrap up that fourth and final overarching pillar is responsibility. 

The first step that Microsoft took with responsibility was we implemented a set of principles: fairness, reliability, inclusivity, privacy, transparency, and accountability. Every single employee at Microsoft needs to be trained in these, whether they are a customer-facing role, like a seller or a support engineer or cloud architect, whether they’re in marketing, whether they’re in operations, or whether they’re in engineering, these principles are at the core of every single behavior that a person takes at Microsoft. Also, we established an Ether Committee, which is AI ethics in Engineering and Research Committee, which brings together our research engineers that are continually evolving the guidelines and practices in this space. We also created an enormous climate investment fund ($1 billion) where we’re investing our technology to support the current climate crisis and trying to reverse it. 

We’ve covered quite a lot, AI at scale, data capabilities and culture, and developer velocity, all of which is underpinned by responsible use and setting those principles, practices, and the tools that are the foundation across the board. Hopefully you got a glimpse into the Microsoft perspective of how to kick into the next gear of AI success. Thank you again for all of your time today. 

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