Cloud innovation: Becoming an AI-fueled entrepreneur

A blog post by Diana Kearns-Manolatos, senior manager, Center for Integrated Research, Deloitte Services LP

In Deloitte’s State of AI in the Enterprise, 3rd Edition, 83% of respondents said artificial intelligence (AI) will be very or critically important to their organization’s success in the next two years. Taking an AI-everything strategy that spans internal operations and external customer experiences requires a high level of data maturity and machine learning (ML) capability across organizations, and many are looking to cloud ML infrastructures to support these types of programs.

To give an example of how organizations might use the cloud to enable AI customer and operational processes simultaneously, let us look at the auto industry. A vehicle’s dashboard sends an alert to the owner that the car’s digital maintenance system has identified a part that needs to be replaced. The system provides the owner with the nearest service station, which is a short distance away, and secures an available appointment. The owner accepts the appointment and follows the GPS to the service station, which has preapproved his insurance claim for the required part. The system then automatically adjusts the company’s inventory management system to account for the part being purchased. The cloud allows the organization to bring AI everywhere across its user experience and back-end operations.

The cloud has an important role to play in helping the chief data officer and chief data scientist bring AI to all corners of the organization to become an AI-fueled entrepreneur.

Business and technology strategies for the AI-fueled entrepreneur

The AI-fueled entrepreneur’s cloud innovation strategy focuses on four key business outcomes:

• Business operations and continuity toward remote workforce management focus on everything from the virtual workforce (such as bots that automate work) to more advanced conversational AI with solutions that become increasingly mature over time.
• Data intelligence or data at the intelligent edge is a staple for an organization that requires a highly mature data strategy to power ML solutions across the business ecosystem.
• Personalized virtual experiences can be achieved with historical customer data and predictive analytics focused on optimizing and personalizing the user journey, while at the same time making sure that microsegmentation doesn’t get in the way of a more holistic perspective.
• A digital ecosystem that brings together customers, workforce, partners, and even regulators with shared technologies can generate valuable data as an asset for the AI program and support stakeholder capitalism.

Given these business requirements, the AI-fueled entrepreneur’s cloud innovation program would benefit from being:

• Globally distributed to bring together data from different silos across the organization;
• Standards-aligned to speed time to market and allow for greater data interoperability across ecosystems while supporting AI explainability;
• Evolutionary infrastructure–focused so that AI solutions can quickly adapt to rapid advancements in algorithms, hardware, and cloud ML strategies, independent of any single vendor or model; and
• Cloud-captive so that all-cloud execution strategies allow for greater consistency and reliability needed with an AI-everything strategy.

To learn more about how cloud innovation can support a range of different business scenarios, read our full blog series discussing four cloud innovation scenarios, reactive responders, experience innovators, and proactive data defenders, and check out our Deloitte Insights article, “A new framing for cloud innovation.”