In order for Enterprise AI to become truly scalable and enact holistic organizational change, enterprises must achieve alignment across people, process, and technology — a task that is far from a turnkey undertaking. While this alignment is critical, the process doesn’t happen overnight and will require a significant time investment from everyone within an organization. We elaborate on some of the key facilitators to usher organizations along the path to Enterprise AI below:
People: There needs to be an institutional capacity to learn from data, collaborate internally, and govern data and machine intelligence. This takes the right skills and organizational structure in order to progressively automate business processes without losing the human-in-the-loop element. How can staff help achieve this?
- At Dataiku, we’re firm believers in the notion that Enterprise AI requires horizontal (team-wide) and vertical (cross-team) collaboration and teams should continuously search for new ways to use the collective skills of the employees.
- While hiring and retention of talent is a key part of this process, AI transformation is more than that. It means giving everyone a seat at the table and involving more than just data scientists and analysts along the way, which will allow people at all levels of the organization to harness data for rapid decision making.
- Organizations should strive to reach a point where data and analytics are deeply ingrained into the company’s culture — for example, scaling to the point of thousands of data projects, hundreds of thousands of datasets, and thousands of people participating in some stage of the end-to-end process.
Processes: Teams need to be able to synthesize and consume information in a timely fashion to extract insights from overwhelming volumes of data. They should:
- Ensure that they are doing so responsibly, meeting any unique privacy, governance, ethics, and compliance requirements.
- Establish and promote a self-service data and analytics strategy, enabling the company to effectively be able to expand data use throughout the organization. This involves connecting directly to data sources (no more back-and-forth with IT and hunting down disparate spreadsheets) and identifying ways to easily share projects with other employees or other teams for review and sometimes even reuse.
Technology: Technological capabilities need to deliver insights at enterprise scale as the foundation of automation and autonomy. The right platform will allow agile consumption of data, continuous development of applications, and operationalized deployment of AI models, helping organizations move from a disconnected enterprise, to one that is globalized, interconnected, and collaborative.
- In order to make your organization more cutting-edge and deliver AI projects that are responsible and sustainable, data technology needs to be made available to everyone, not just those that are in the weeds with data all day as part of their job function.
- To make this process more seamless, teams can go with open source options and consider implementing a collaborative data science platform that enables everyone to access and use data, despite their background or job title.
Check out this video to hear from Chris Kakkanatt, Senior Director of Data Science at Pfizer, speak with Kurt Muehmel, Chief Customer Officer at Dataiku, about the decade-long journey that they have taken to achieve a truly human-centric, AI-driven transformation that embodies the thoughtful alignment people, processes, and technology.