Four practices your organization may need to lead the AI transformation
While the pandemic has accelerated transformation in many aspects of business, artificial intelligence (AI) has progressed remarkably fast over the past two years.
As more leaders recognize and rely on AI’s utility in uncovering and scaling data-driven insights and freeing up their workforce to creatively solve problems, they increasingly see AI technologies and processes create value for employees, partners and customers.
While we are still in the early days of AI transformation, organizations are rapidly evolving and scaling their capabilities. A survey of 2,875 executives for Deloitte’s most recent State of AI in the Enterprise report found that market-leading AI-driven organizations primarily focus on leading practices in four key areas:
Setting up a strong AI strategy; Integration of AI-oriented operations; Promoting a data-driven culture; and Building ecosystems that enhance and protect competitive differentiation.
Regardless of an organization’s commitment to AI adoption or its success in deploying and scaling AI for strong results, each area needs to be scrutinized.
Strategy
Organizations with an enterprise AI strategy and leaders who communicate a bold vision were almost twice as likely to achieve high results among those surveyed than those without.
A common key to success is maintaining a clear link between AI efforts and the company’s core strategy, the research data shows. Data scientists and information technology (IT) leaders should help senior executives determine which use cases offer the best opportunities for AI to fulfill and expand the company mission.
The implementation of AI must start with a clear, coordinated, real-time enterprise-wide strategy that uses AI to gain a competitive advantage and communicate that strategy to staff, partners and customers.
Activities
In order to introduce AI technology and integrate it effectively, operational and governance processes must be reimagined and updated. Leading organizations in the study were more than three times more likely to create new roles and change operations, and were also three times more likely to document and enforce machine learning operations (MLOps) procedures. However, two-thirds of the organizations surveyed using AI have not yet adopted such operational AI-leading practices, such as adhering to a well-calibrated MLOps framework, documenting AI lifecycle publishing strategies, and updating workflows and roles across their organization. .
Without these shifts, AI may not be able to deliver on its potential. Organizations must embed AI into all core processes and activities, and the C-suite must ensure that AI is woven into the business decision-making fabric of the organization.
Making change requires a thoughtful redesign of how work is done and how the organization prepares to take advantage of new business models and opportunities as AI capabilities mature.
Culture and Change Management
According to Deloitte’s State of AI in the Enterprise report, organizations that invest in change management are 1.5 times more likely to achieve their goals than those that don’t.
AI can empower a human workforce and free people from automated processes so they can focus on ideas that add value. However, organizations must support their workforce in upgrading skills and capabilities through tailored change management activities that address needs at all levels and functions.
Communicating the benefits of AI goes beyond touting the workforce. Changing your mind generally requires training, motivation and support. To adopt AI through change management, the goals of AI transformation often need to be clear, relevant, and achievable for everyone in the organization.
For organizations that manage to find value in AI, a key difference between those surveyed was fostering a supportive AI-ready culture across the enterprise, giving employees confidence that AI will benefit their work, and data literacy across the board. levels in the company. and apply agile processes that allow for more (and faster) experimentation.
ecosystems
The organizations that get the best results from applying AI to their strategies, operations and cultures are not making this progress in a vacuum. They tend to build broad and diverse partnerships to support transformative visions for AI and strategies that span their enterprises to make AI a true differentiator that adds value.
And while this approach may feel counterintuitive, building diverse and complex ecosystems may be a safer strategy for an ecosystem-building organization than limiting partnerships to a small, streamlined network that involves fewer relationships to manage. Organizations that build complex networks with the right partners to help strategize and optimize their use of hardware, software and AI applications can more easily adapt plans as needed to achieve their goals in the future.
The role of AI in transformation
Evolving economic conditions increasingly illustrate the potential of AI to transform an organization in a way that frees up its workforce to apply its innovation to create value, increases operational speed and efficiency, widely satisfies the customer expectations – and gain an advantage over competitors who are slower to embrace AI’s capabilities. At every stage of its AI transformation journey, an AI-driven organization must build AI strategy, operations, culture and ecosystems to make the most of its capabilities for their workforce and their customers.
Read Deloitte’s latest State of AI in the Enterprise report “Becoming an AI-Fueled Organization”.
About Deloitte: See www.deloitte.com/us/about for a detailed description of our legal structure.
This post Four practices your organization may need to lead the AI transformation
was original published at “https://hbr.org/sponsored/2022/04/four-practices-your-organization-may-need-to-lead-its-ai-transformation”
