In July 2019, Catalytic joined other executives from the largest artificial intelligence companies in the ecosystem, like Microsoft, Google, Facebook, and Amazon, at Venture Beat’s VB Transform 2019 conference. After two exciting days of conversations with the country’s top thought leaders on AI innovation, we offer our key takeaways for what’s necessary to move into a more effective digital future and toward more successful AI.
1. Intentional inclusivity
Last year at VenureBeat’s Transform 2018 conference, only 5% of attendees were women. This year, that number rose to 30%. While still shy of parity, it was inspiring to see more diversity in our tech leader representation.
Catalytic attended the first event of the conference: the Women in AI Breakfast, which hosted more than 200 women founders, inventors, researchers, and activists in the AI sector. Attendees ranged from representatives of IBM and Uber to startup founders who are building out a global network of women investing in women.
To sum it up, Julie Choi, VP of the Artificial Intelligence Products Group at Intel, said it best on stage:
“If you want to build the best products that are really differentiated, you’re going to need diversity, and that is not going to happen automatically,” Choi said. “And it doesn’t just get built by our HR teams — it’s every single person in this room, having the intention to be inclusive and to be diverse in the way you think and open minded.”
2. Better data strategies
Amazon Web Services AI VP Swami Sivasubramanian and Facebook AI VP Jérôme Pesenti lead some of the largest AI operations in the world. At VB Transform 2019, they both agreed that if you want to scale your AI programs or become an AI-first company, you need to get your data in order first.
Data is what teaches machine learning. So in order to be an AI-led business, you need to focus on being data-driven.
As Sivasubramanian said:
“The number one thing I can say there is you want to get your data strategy right, because if you don’t, when you end up hiring a machine learning scientist and you expect them to come and invent amazing new algorithms, the reality is they spend a large percent of their time dealing with data cleanup and data quality setup and so forth.”
However, reassuringly, AgShift stated that even if your organization has no data, creating data sets for AI is still possible for traditional industries like agriculture or manufacturing. The action item? Start now and adapt quickly.
3. Digital-minded leadership
No matter where you are in implementing AI initiatives, there’s a trend toward advocating for company-wide support—starting with leadership from the top.
Hypergiant Chief Strategy Officer John Fremont said if you “don’t have top-down support and understanding for bottom-up initiatives, you’re going to fail miserably because there’s going to be … a longer timeline for ROI.”
“[The most successful companies implementing AI] have a mandate from the CEO — they have real dollars behind it and functional business owners who have decision-making abilities to buy the technology directly, without a ton of bureaucracy,” he said. “Anybody at any organization who’s discrediting what’s happening [with AI], or not acknowledging that it’s actually an industrial revolution, [are going to contribute] to failure.”
To successfully implement AI and add to your organization’s digital transformation, all stakeholders and employees must be engaged and forward-thinking, making scaling just as much about change management and leadership skills as it is about the technology.
4. More education initiatives
Recognizing the need for better understanding and advocacy for AI, Landing.ai’s Andrew Ng talked about how he and companies like Microsoft have recently introduced education initiatives. One example is the AI Business School, made specifically for business executives to understand the value of the technology they’re adopting, rather than just telling them to adopt it.
Hilary Mason, General Manager of Machine Learning at Cloudera, echoed this, predicting that more managers will get involved in AI planning and training. She says the future of data science should be made easy enough for product managers and business users to understand because “those are the people who are best positioned to recognize where AI should fit into [the business].”
5. Smarter automation
Today, creating bots is easy—scaling them is another matter. That was the main takeaway of a panel with Brian Bond, consumer vice president at CenturyLink, as he discussed the company’s Robotic Process Automation (RPA) efforts.
RPA is growing thanks to the use of AI tools that make it easier to streamline how they work. What many at the conference were wondering was—how to take your simple tasks completed via RPA and turn automations into a robust digital factory.
During one of Catalytic’s panels at VB Transform, we answered: “RPA is the gateway drug for AI.”
Catalytic’s Chief Customer Officer Ted Shelton went on to explain that the most successful way to continuously advance your digital capabilities is to think beyond RPA. If you set it up the right way, it’s possible to gather enough quality data to use as the training set for machine learning.
Then, if you use a tool like Catalytic to trigger the next step in the process after a bot completes its job, full end-to-end process automation becomes possible.