“If you want to go fast, go alone. If you want to go far, go together.”
I love this proverb and apply it a lot in my work at CloudFactory. But the truth is that to realize digital transformation from machine learning (ML), organizations have to do both: go alone and go together.
With AI and ML applications growing across all sectors, every day is a race to secure a sustainable competitive advantage from computer vision (CV) and natural language processing (NLP) applications. That means individual team members must go quickly to identify important use cases and procure usable data.
At the same time, if companies want to succeed, they need to foster a culture for these innovative technologies to thrive.
That means data scientists, senior executives, and functional leaders at all levels must go together. Why? Because if people feel threatened by new technology the culture will reject it, and the race to competitive advantage will be lost. Or, if no one can agree on where to focus, resources will be spread too thin and applications will not perform as well as they could.
In fact, in a 2018 McKinsey survey, the top two barriers to AI adoption respondents cited were lack of a clear strategy (43%) and lack of talent with appropriate skills (42%). We caught up with Kirk Borne, Principal Data Scientist and Executive Advisor at Booz Allen Hamilton at the Open Data Science Conference (ODSC) in Boston and asked him about the challenges facing the consulting firm’s clients who are struggling with AI adoption. He told us it requires clear roles and responsibilities. “That cultural transformation - that digital transformation - requires that everyone figures out where they fit,” he said.
CloudFactory’s Maggie Miller interviews Kirk Borne, Principal Data Scientist and Executive Advisor at Booz Allen Hamilton, at ODSC East in Boston.
We’ve shared about the people in the AI tech stack and the importance of fielding the best team for AI. Humans are more important to the AI data loop than ever. In a 2019 report on AI design and development, Gartner urges CIOs to “grasp the strategic and design requirements of running a human and algorithmic business.” Understanding those requirements includes considering all people in the process - from the data labelers who prepare datasets to the data scientists and engineers who test and validate ML models.
After a decade providing human-in-the-loop workforce solutions for hundreds of happy clients and working with millions of datasets, we’ve learned the most important first step before beginning an AI project is getting internal buy-in on what you plan to do and how you want to do it. Garnering internal adoption of your plan, while challenging, is a most critical first step in beginning an AI project. It’s even better if you secure an executive sponsor to guide your efforts in sharing the project across an entire organization.
At CloudFactory, we’re here to help with the race to usable data for AI. We recently announced new additions to our WorkStream offerings, designed to help our clients get massive amounts of usable data in a way that integrates seamlessly with all of the people, processes, and tools they already use to bring new applications to market.
Our train.CV and train.NLP solutions combine skilled data labelers and a proven methodology for delivering high quality data for data labeling tasks such as tagging images, semantic analysis of text, and the normalization of data among siloed sources.
Our WorkStreams are also designed to work the way our customers work, using virtually any tool on the planet. We keep our clients closely connected to their data processes, regardless of the scale of the project. This provides the best of both worlds - we do the heavy lifting on the data labeling so our clients can focus on strategy, innovation, and creating the culture to support strategic AI adoption.
Want more information? Read how CloudFactory is working with AI innovator Heretik to drive digital transformation in the legal industry. Learn how we work hard to deliver quality training data at scale for our clients every day.
Data Science Workforce Strategy Computer Vision AI & Machine Learning
