Artificial intelligence has great potential, but only if your organization adopts it successfully. Organizational change management and enterprise portfolio and program management practices will help you drive adoption to achieve AI’s full benefits.
Successful implementation and adoption of AI extend well beyond sophisticated technological capabilities. They demand an effective AI change management strategy that includes organizational change management (OCM,) enterprise portfolio and program management (PPM) and a strong emphasis on the human aspects of change.
This blog is the first in a series of posts focused on the intricacies, challenges and benefits of AI adoption. It addresses why you need AI change management to achieve two strategic drivers of AI – cost control and growth.
Future articles will address the various types of AI strategies organizations are adopting to ensure strategic alignment and operational effectiveness, the challenges and opportunities you may face when implementing AI, and ways you can use AI to improve technology adoption and achieve transformational change.
But before diving into AI change management, let’s look briefly at just two benefits AI adoption offers: saving costs and enabling growth.
Cost Savings and Growth Opportunities: Two Benefits of Artificial Intelligence
AI systems streamline operations and increase efficiency, leading to significant productivity, quality improvements and cost savings. Simultaneously, AI can drive growth by enabling your organization to offer innovative products, services and customer experiences.
When fully adopted, AI reduces the need for manual work and automates routine tasks, freeing employees to focus on more engaging, higher-value activities. It not only lowers operational costs but also creates enriched job experiences and higher employee satisfaction. At the same time, AI’s data analysis capabilities can identify inefficiencies, redundancies and bottlenecks, enabling organizations to optimize their processes, improve quality and further control costs.
AI also supports growth by helping organizations understand their customers, markets and competition better. This informs more effective business strategies and better-aligned business models. AI-powered analytics uncover hidden patterns, trends and insights in vast amounts of data, enabling organizations to make data-driven decisions that enable growth. Furthermore, AI enables organizations to create personalized customer experiences, fostering customer loyalty and opening new revenue streams.
However, achieving any of these benefits requires the artful application of an AI change management strategy that includes OCM and PPM.
What to Do First
Your first step is determining your AI vision, strategy and governance models. You need these elements regardless of your chosen AI technology. The AI vision determines what level of AI adoption is possible and outlines the end-point destination for performance improvement and impact.
The strategy puts flesh on the bones of the vision with more detailed goals and timelines related to people, process, technology and analytics. The governance model establishes the policy and structure for AI tool usage, including ethical considerations, security, data privacy and compliance.
In addition to the above, you should conduct a readiness assessment with AI experts and establish an AI center of excellence. A workforce and change plan will help you address the many people-related challenges AI adoption creates. This is where your organizational change management and portfolio and program management teams come into play.
OCM: The People Side of AI Change Management
A people-centric approach is critical to successful AI adoption. AI change management programs, when effective, carefully consider the human dimensions of AI integration. These programs include reconfiguring job roles and the ethical implications and fostering an organizational culture that embraces change.
Inviting employees to actively participate in the AI implementation process can significantly demystify the technology, showcasing its role as a tool for enhancing their work rather than threatening job security. This approach paves the way for heightened levels of employee engagement and acceptance of AI, the requirements of any successful business transformation.
By its nature, AI brings about significant changes in how an organization operates that affect processes, job roles and even business models. If not managed properly, this level of change can disrupt your organization, resulting in AI adoption challenges such as resistance to change, low use rates and failure to realize the full benefits of AI.
The Role of Leadership
Leadership plays a crucial role in supporting the adoption of AI-driven change. Leaders must champion change, demonstrate commitment and lead by example. The messaging leaders share is critical. They should use words like “augment” rather than “substitute” when communicating with employees.
Exemplary leadership during AI transformation involves providing comprehensive resources and active support for the AI change management process. This includes not only ensuring the right technical tools but also prioritizing developing necessary training and support structures. Building coalitions of support while creating and celebrating incremental wins helps leaders demonstrate their support.
Managing Resistance to Change
Resistance to change is one of the biggest challenges organizations face during AI adoption. People often fear that AI will render their jobs obsolete or that they will not have the necessary skills to operate in an AI-driven environment. Applying OCM to an AI adoption framework can help address these fears by providing a clear vision of what the change entails, why it is necessary and how it will benefit the organization and its employees.
Communication and Training
Two key elements of OCM are communication and training. Clear, consistent and transparent communication helps to manage expectations, alleviate fears and build trust. Communication involves explaining the reasons for AI adoption, its benefits and how it will impact different stakeholders. Leaders should encourage questions and feedback, creating an environment of trust and openness.
Training, on the other hand, equips employees with the necessary skills to operate in an AI-driven environment. It helps to bridge the gap between current capabilities and those required in the future, reducing anxiety and resistance. Successful AI training programs champion a culture of continuous learning and development. They recognize that as AI evolves, so must the skills of the workforce. Hence, these programs often incorporate self-paced learning modules, mentorship opportunities and channels for feedback and improvement.
By fostering an environment that encourages lifelong learning, organizations ensure their workforce remains agile and prepared for future technological advancements.
Here’s an example of how General Electric used organizational change management to successfully adopt AI into their workflows.
General Electric’s AI-Driven Predictive Maintenance
General Electric (GE) successfully adopted AI for predictive maintenance in its aviation and energy businesses. The AI system analyzes machine data to predict potential failures, reducing maintenance costs and minimizing downtime.
GE’s approached its AI implementation with clear strategic alignment, strong project management and a focus on the people side of AI and change management. GE involved employees in the implementation process, equipped them with the necessary skills through training and maintained transparent communication throughout, effectively managing resistance and driving acceptance of the new technology.
PPM: The Backbone of AI Change Management
Effective portfolio and program management plays a crucial role in successful AI adoption. It serves as the backbone that supports AI initiatives, ensuring alignment with business goals, efficient resource allocation and successful execution.
In the context of AI adoption, portfolio management includes overseeing all AI-related initiatives within the organization to ensure they align with the company’s strategic objectives. Effective portfolio management helps to prioritize AI initiatives based on their potential return on investment, feasibility and alignment with business goals. It also enables organizations to balance their AI portfolio, ensuring a mix of short- and long-term initiatives and a balance between low- and high-risk projects.
Moreover, portfolio management involves regularly reviewing the AI portfolio, assessing the performance of AI initiatives against predefined metrics and making necessary adjustments. By doing so, organizations can ensure their AI initiatives stay on track, deliver value and contribute to the strategic objectives.
While portfolio management provides a high-level view of all AI initiatives, program management focuses on coordinating related projects to achieve benefits companies could not obtain if they managed projects individually. In the context of AI adoption, program management is essential to ensure the successful delivery and execution of individual AI projects contributing to the overall AI strategy.
Effective program management involves defining clear roles and responsibilities, establishing robust governance structures or systems, and designing effective communication and reporting mechanisms. It also involves coordinating efforts across different projects while managing interdependencies and complexities to mitigate risk.
PPM’s Role in Successful AI Adoption
By providing a structured approach to managing AI initiatives, portfolio and program management enable organizations to navigate the complexities of AI adoption effectively. The PPM team provides the necessary oversight, coordination and control to ensure the company efficiently executes the AI initiatives, delivers the expected benefits and contributes to the strategic objectives.
Moreover, PPM fosters a disciplined approach to AI adoption. The PPM team ensures your company pursues AI initiatives as part of a coherent, strategic approach to leveraging AI rather than in isolation. This process helps to avoid wasted resources, misalignment and failure to realize AI’s potential benefits.
Here’s an example of how JPMorgan used portfolio and project management during an AI implementation.
JPMorgan Chase’s COiN
JPMorgan Chase’s Contract Intelligence (COiN) platform is an example of successful AI adoption, underpinned by effective portfolio and program management. COiN uses machine learning to review legal documents and extract important data points and clauses, which would take thousands of hours if done manually.
The decision to develop COiN was part of JPMorgan’s broader portfolio of AI initiatives aimed at automating routine tasks and reducing operational costs. This strategic alignment ensured that COiN received the necessary resources and support from the top management.
Program management was vital during the development and implementation of COiN. Given the complexity of the project and the need to integrate with existing systems, they required rigorous planning, organization and risk management. The program management team ensured that the company effectively coordinated and executed different aspects of the project, from data collection and model training to system integration and user training.
AI offers organizations a powerful tool to control costs and drive growth simultaneously. However, to fully realize these benefits, organizations must take a people-centric approach to build their AI adoption framework and leverage strategic portfolio and program management processes.
By doing so, organizations will ensure their AI initiatives deliver maximum value and drive long-term, sustainable success. In the next post of this series, we’ll discuss how business architecture, business process improvement and performance measurement contribute to successful AI implementation.
Authors note: The development of this article was supported using Open AI’s Chat GPT-4.