Moving AI From Experiment to Enterprise Strategy

Enterprise AI is the ability to embed AI methodology into the very core of an organization's data strategy, including every process and aspect of business. This blog post summarizes some strategies from IDC around Enterprise AI and steps organizations can take to get there.

While the key strategies outlined here specifically tie back to the Asia/Pacific region, they can be applied more broadly to organizations jumpstarting or progressing forward on their journey to Enterprise AI. The application of AI should be viewed as a strategic move for organizations, grounded in developing their core competencies and growth needs while also understanding their challenges, in the hopes of implementing a plan to mitigate or resolve them completely. Some of the more practical use cases for AI include:
• Improving business agility: The ability to adapt to changes and deal with uncertainties, especially in IT and operations.
• Driving risk management: The ability to avoid and mitigate the impact of financial risks, especially in financial services organizations.
• Sparking product innovation: The ability to commercially launch new products and services, particularly in high-end manufacturing.
• Enhancing customer satisfaction: The ability to meet and exceed customers’ expectations, such as in retail and hospitality.
• Increasing productivity: The ability to produce more with less people, and faster, such as in manufacturing or telecommunications.

Making Enterprise AI an Organizational Practice
According to the IDC, there are five key steps to an AI maturity model. Once again, while the steps tie back to industry observations specifically in the Asia/Pacific region, they can certainly be applied more generally for organizations beginning to build out their AI investment and gradually adopt an Enterprise AI strategy that is scalable and will sustain the business in the future. At Dataiku, our belief aligns — we believe that Enterprise AI involves employing robust data methodology at all levels of the company, down to every business process.

1. Experimentation
Organizations investing in AI can start by experimenting with simple use cases and focused AI projects that are aligned with business goals within a few select business functions.

2. Expansion
At this stage, organizations may have a few AI initiatives, but they have yet to be coordinated as part of a broader plan for AI implementation throughout the organization. Here, use cases should be prioritized in order to identify internal advocates, upskill team members, avoid the complications associated with integration, and keep line of business stakeholders excited. For a complete framework on how to identify the right use cases for success, check out our detailed white paper on the topic.

3. Execution
In order to keep new opportunities in the organization’s AI pipeline, expand the scope. Once teams have received buy in from senior executives, they can execute on projects and track business results. Once they are able to repeatedly prove tangible business results, the onus is on them to communicate the ROI across the organization which, in turn, will generate new opportunities, intrigue from teams interested in participating, and expectations for data-driven processes moving forward.

4. Alignment of People, Process, and Technology
In order for Enterprise AI to become truly scalable and enact holistic organizational change, IDC laid out the need for alignment across people, process, and technology — something that we also believe at Dataiku — but is not a turnkey undertaking. We elaborate on these key facilitators to move 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. At Dataiku, we’re firm believers in the notion that Enterprise AI requires horizontal (team-wide) and vertical (cross-team) collaboration.
• Process: Teams need to be able to synthesize and consume information in a timely fashion to extract insights from overwhelming volumes of data. This needs to be done while meeting any unique privacy, ethics, and compliance requirements.
• 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.

5. Continue the Disruption
The journey to Enterprise AI is ongoing as data and technology continues to evolve and improve. AI projects that truly achieve real business impact and value will support and strengthen existing business operations and propel a positive chain reaction to customers, suppliers, and partners. A successful Enterprise AI system will also include regular monitoring and feedback integration to ensure ongoing improvements.

Looking Ahead
Building an Enterprise AI strategy and platform tailored to organizations’ unique needs and pain points is not an overnight initiative, but rather one that needs to be woven into every aspect of the operation. While trends and maturity in Enterprise AI differ by geographic region, one common thread applies — the ability to successfully scale and employ Enterprise AI is an organizational asset pivotal to the success of the businesses of the future.

Catie Grasso is Marketing Content Manager at Dataiku.