6 Traits of an AI-Everyday Organization

AI is now an organization-wide imperative for success; leading companies embrace several common characteristics and actions, Deloitte research shows

AI continues to be a topic of discussion in boardrooms across industries, and since the broader commercialization of generative AI, these discussions have only accelerated. Many companies are exploring how to penetrate new markets, defend existing opportunities face down disruption, and operate more efficiently using AI. Decisions on these topics may require companies to reconsider how they interact with their customers, the speed at which they make strategic decisions, and how members of their workforce fundamentally approach their jobs.

To better succeed with AI, companies should take an enterprise-wide AI-everyday approach, which extends AI capabilities to all corners of an organization. AI is no longer a skill set to house in a siloed technology function. Nor is it a capability without a C-suite narrative, a tool without trust at the forefront, or an opportunity to pursue outside of an ecosystem of internal and external partners.

Despite this AI imperative, it can often be difficult to determine where to start or what the ideal end state looks like. Deloitte research of more than 100 AI-everyday organizations has found six common traits that can be customized to help inform companies as they start, or continue, their AI journey.

AI as a Company North Star

A clear and embedded AI focus can deliver differentiation and value, yet organizations may struggle to define, communicate, and update a clear and relevant purpose for using the technology. According to Deloitte’s most recent “State of AI in the Enterprise” survey, 94% of business leaders surveyed agree that AI is critical to success over the next five years, yet as organizations deploy more AI, outcomes appear to be lagging. Organizations can benefit from developing thoughtful AI priorities aligned with overall corporate strategy and supported by a prioritized portfolio of value-driven use cases, targeted capability building, and talent upskilling. The following factors can help:

• Clear link to value (and ROI). Anchor AI initiatives in enterprise and business strategy, objectives, and value, which may mean focusing on growth, optimization, or efficiency.
• Top-down sponsorship. Ensure buy-in from leadership for AI projects, including their prioritization.
• Focused communication. Relay AI messaging throughout all levels of the organization and consider sharing AI-related educational resources or holding a hackathon.
• Targeted interventions. Develop a bespoke education program or partner with an existing platform provider to arm employees with new capabilities and address AI skill gaps.
• On-the-job experiences. Create roles and experiential moments to help illustrate how AI is changing work—and then reinforce in the work itself.

Cross-Functional Teams and Standardized Methodologies

Close collaboration between business and technology stakeholders can help drive value across the AI lifecycle, yet organizations may have trouble realizing AI value beyond the proof-of-concept phase. Organizations will likely find more success if they leverage cross-functional talent and standardized delivery methodologies to ensure business value is front and center from ideation to execution. Consider the following:

• Integrated talent model. Adapt ways of working to create hybrid delivery teams (e.g., data science, design, business) that work together to identify technology solutions rooted in specific business needs.
• Grassroots innovation process. Create a framework and culture to identify and prioritize use cases to meet business needs.
• Standardized intake process. Standardize the intake process across business units and develop a robust prioritization framework to manage supply and demand for requests.
• Enterprise-value opportunities. Start with opportunities that have cross-enterprise value and scalability across business units to create momentum; consider pilots to effectively scale.

Holistic AI Data Strategy

A robust data management, creation, and acquisition strategy is table stakes for AI value creation, but organizations often fail to unlock the full value of their data. Organizations can benefit from establishing an end-to-end data strategy with proper governance to democratize AI across the organization. Leaders should continuously look for opportunities to use AI to help generate value from existing data, create and acquire new data, and evaluate monetization opportunities. To do so, consider establishing:

• Data governance processes. Create processes to manage data integrity, availability, and security within the organization.
• Data quality. Develop an organized system to validate data quality to stay on top of potential issues and ensure data is being used for its intended purposes.
• Consistent data labeling. Prioritize data labeling to help ensure that AI models are trained to accurately interpret and categorize incoming data and are unlikely to introduce potential bias.
• Data marketplace. Build a marketplace of data assets and available AI tools to expand the monetization of proprietary and third-party data.
• Adoption and change management. Incentivize creation, use, and reuse of data assets by technical and nontechnical stakeholders through leadership messaging, recognition, hackathons, training, and other programs.

Technology Infrastructure that Enables AI

Cloud services coupled with open-source libraries and AI tools can decrease time to insight, but organizations may struggle to adopt and sustain infrastructure scalability. Some organizations are focusing on how to maximize value from technology by leveraging existing partners, and integrating with a portfolio of other cloud and technology providers that can provide access to open-source libraries and the latest technology tools to empower their employees. In doing so, organizations can consider:

• Data product ownership. Business domains can benefit from owning data across its lifecycle, rather than having to go through a centralized technical team.
• Self-service platform. Data should be discoverable and shareable across an organization and ecosystem to accelerate development time for technical and nontechnical stakeholders.
• Governance and compliance. Data products can be built with automated, federated governance and compliance to ensure the right people have access to the right data.
• State-of-the-art platform and tools. Monitoring and integrating technology tools available in the market can help accelerate innovation, as can decommissioning tools past their prime.

Strong Ecosystem of Alliances and Partnerships

In today’s fast-paced environment, companies are increasingly collaborating outside of their organizations to meet their customers’ and markets’ expectations. This often requires taking an ecosystem mindset to growth and differentiation. An effective ecosystem and partner strategy can help an organization to scale efficiently, but aligning on how best to partner and leverage an ecosystem can be challenging. Organizations can seek to augment core AI competencies by fully leveraging the AI ecosystem to gain access to partners’ unique AI capacities, technologies, and capabilities. Doing so may require:

• Clearly defined objectives. Outline goals based on market needs.
• Market scan. Continuously monitor the market landscape including competitors, adjacent industries, and disruptors to keep a pulse on leading capabilities and potential partnership opportunities to build or go to market together.
• Capability awareness. Build a robust understanding of internal capabilities and opportunity areas.
• Frameworks. Create frameworks to systematically identify, launch, and scale build, buy, or partner opportunities within the AI space.
• Governance structure. Establish clearly defined roles, responsibilities, and collaboration practices amongst partners.
• Organizational structure. Develop an understanding of where different organizations fit within the AI ecosystem.

Trustworthy AI

Strong and evolving principles of AI ethics can help build consumer and employee trust, yet companies may be unable to effectively develop, deploy, and update ethical safeguards. Ethical AI principles abound in the market; what can drive trust is how organizations embed them in existing processes. This can help organizations to identify, monitor, and address risk, and partner with other companies and regulators to shape future standards. To make strides in this area, consider the following:

• Communication. Ensure trustworthy AI principles are known and understood by all.
• Processes. Standardize processes that bake trustworthy AI into new projects and ensure those standards are continually followed.
• Ownership. Decide who is responsible for ensuring trustworthy AI is being practiced.
• Success metrics. Define standards and measurement of success metrics against customer, employee, and company values.
• Supporting structure. Create teams to support trustworthy AI goals.

As AI is increasingly woven into enterprise IT strategy and operations, the entire organization should bear responsibility for contributing to an AI-everyday environment. By considering the challenges at hand and how to overcome them through preparation, collaboration, and ingenuity, technology leaders can help position their enterprises for future AI-fueled growth.

—by Howie Stein, managing director; Greg Spillman, senior manager; Nick Jameson, senior manager; and Udit Rastogi, manager, all with Deloitte Consulting LLP

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PUBLISHED ON: June 27, 2023 3:00 pm ET

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F. Robert Jacobs

Professor Emeritus of Operations Management

Operations and Decision Technologies

Indiana University, Bloomington

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