Artificial Intelligence (AI) and Generative AI (GenAI) are rapidly reshaping business landscapes, offering transformative abilities across industries, and changing the ways organizations operate, innovate, and engage with stakeholders. While AI offers immense potential for organizations ready to embrace its capabilities, achieving successful AI transformation requires thoughtful approaches to adoption, integration, and governance processes.
This executive brief discusses insights and strategies from industry leaders on leveraging AI for organizational change and transformation, addressing common hurdles, and measuring its impact effectively to ensure successful adoption.
This roundtable was held on September 26th, 2024.
Artificial Intelligence & Its Influence on Organizational Roadmaps
Leveraging Artificial Intelligence (AI) capabilities within your organization can open the door to countless new possibilities that can help a business grow from both a short-term and long-term perspective. However, since AI can be used in so many different ways, it is important for companies to identify what kinds of approaches would work best for their business needs.
“Each organization has unique use cases. It is critical to pinpoint the business problem and build something specific to solve it.”
Stacey Taylor - VP, Implementation & Change | Visa
Three Levels of AI Adoptions
Sundeep Thusoo, Director of Business Transformation at Philips, outlined three distinct scales of AI adoption, each representing a progressive level of integration and impact within the organization. It shows how AI can evolve from streamlining daily tasks to reshaping the very foundation of a business.
Examples of Possible Adoptions
✔ Data-Driven Decision-Making
AI supports decision-making by analyzing data and generating actionable insights to improve business processes such as deal optimization, reducing supply chain costs, or optimizing logistical processes. Its ability to process and auto-label data on the fly eliminates the need for a singular data model, enabling more flexible and faster judgment.
“We are now able to utilize GenAI in code development, to help optimize codes and test smaller models to solve specific tasks before scaling to enterprise-level applications. For employee productivity, instead of investing in a heavy-duty Enterprise Data Warehouse (EDW) platform, we are now able to gather all the data together before manipulating it to enable certain capabilities at the grassroots level and automate day-to-day work.”
Akshaya Murthy - Director, AI Transformation | Zendesk
✔ Churn Rate & Automations
Consider how different AI applications from machine learning (ML) to GenAI can improve the churn rate and how to include them in your organization’s transformation roadmaps to shorten the timeframe or reduce possible issues while trying to complete a specific model. For example, AI implementation to finance operations can help clean up records and automate tasks such as name matching, duplicate record cleanup, and reconciliation. This way, the organization can improve operations while keeping an eye on governance.
✔ Supply Chain Optimization
AI agents can optimize supply chains by reducing costs, streamlining logistical processes, and automating low-level decisions such as assigning fulfillment centers or managing shipping processes. Machine learning also helps improve inventory management to ensure a continuous flow of supply in each store. This leads to more optimized operations and reduced human intervention.
✔ Enhance Employee Assistance Tools
AI-powered tools support employees by automating routine tasks such as drafting emails, assisting with workflows, and managing data, subsequently improving productivity and reducing burnout by acting as on-demand assistants.
“Your in-house chatbot can work as an assistant to help your store associates with low-level tasks such as finding specific information or getting solution suggestions to solve common problems, similar to ChatGPT’s functionality.”
Arkesh Mishra - Head of People | Walmart U.S. Tech
✔ Enhanced Customer Support
It is also possible to implement AI to transform customer support by providing faster, more accurate, and round-the-clock assistance. With tools such as AI-driven ticket systems, organizations can address customer inquiries efficiently, personalize their experiences, and anticipate needs based on historical data. This will help free up human agents to focus on more complex issues, proactively solve problems, enhance customer experience, and improve overall response time.
Embedding AI Capabilities Into Your Organization: Overcoming Common Challenges
1. Ensuring Organization-Wide Buy-In
✔ Securing Buy-In and Sponsorship
AI can have a significant impact on operations, demonstrating its ability to substantially increase ROI. However, the estimated figures can be so impressive and almost unbelievable that they may cause bottlenecks in convincing people of their legitimacy.
“Leadership buy-in could take months to be achieved. Leaders really need to quickly start seeing that the cost of AI is worth the investment as the ROI is so high, so that the organization is able to catch up with changes.”
Akshaya Murthy - Director, AI Transformation | Zendesk
Employees need to be introduced to AI through value proposition and WIIFM messaging while C-levels will need to see the ROI equation to prove the value of these expensive tools and how they are indeed vital to business optimization. However, with important monetary investments needed, it will most likely add pressure to reduce other operational costs such as decreasing headcount.
“Growth companies may reduce headcount pressure by decreasing new hires instead of impacting the current workforce. It might be trickier though for companies with lower profit margins to adopt AI at scale since they probably will need to have that difficult cost-cutting conversation beforehand.”
Stacey Taylor - VP, Implementation & Change | Visa
✔ Unifying Alignment and Strategies
There are different conflicting opinions on how to best leverage GenAI which are usually based on different priorities, strategies, and visions on how it should be implemented.
“These days, everyone sees themselves as an AI expert which makes implementation across the organization more difficult. It is important to create organization-wide alignment around AI positioning before diving in.”
Alessandro Prieto - Senior Director, HR North America Lead | Analog Devices
2. Reducing Workforce Resistance
✔ Encourage Mindset Shifts
Employees are usually resistant to AI adoption due to common worries like how it will impact them and their job security. Meanwhile, clients may refuse to pay the same amount for services if certain tasks are delegated to AI instead of to the human labor they are paying for. A mindset shift is critical where people need to stop viewing AI as a replacement for human efficiency, and instead, emphasize the complementary value both bring to the table.
“Imagine AI as the minions in ‘Despicable Me’. They make human tasks easier and faster, but they can never replace humans. A similar shift in perspective is required and people should view AI’s role as assisting people. Humans are still needed for judgment calls and decision-making.”
Sergio Tortora - Senior Transformation Leader | AIG
✔ Reframe Your Language
It’s important to reframe language used around AI and how it helps elevate people’s capabilities. For instance, emphasize how GenAI has helped in performing various organizational tasks including ‘creating’, ‘goal-setting’, and ‘brainstorming’. By helping to improve conversations around business performance goals, strategic goals, and even performance metrics with engaging prompts, it encourages people to see what they can achieve with this technology.
“Imagine AI as an exoskeleton that lets humans go far and beyond. It’s not that the robots are taking over, but almost like you have suddenly become Iron Man. Don’t forget to find those who are able to reframe their thinking earlier and see AI as a way to improve their skills, and have these people act as your change champions to help move along the adoption faster.”
Laurie Ditch - Sr. Director, Organizational Strategy and Operations | iMerit
✔ Change in Identities
GenAI may also cause changes in employees’ careers and their identities at work. It is important for the corporation to address this issue and help employee change their career projection or goals without feeling lost or replaced. However, this may present a new challenge as certain people will not be interested in upskilling or being involved in more strategic positions.
“Taking the pharmaceutical industry as an example, GenAI has significantly reduced the time required for medical writing tasks, with some companies reporting up to a 30% reduction in time spent on clinical documentation and regulatory submissions. This development accelerates the timeline for health authorities to review research and study data, ultimately benefiting patients who are eagerly waiting for medications to reach the market. However, this shift poses challenges for traditional medical writers who may be reluctant to upskill or explore alternative career paths as their roles evolve with advancing technology.”
Nadine Hammer - Sr. Organizational Change Lead - Product Development, Portfolio, Strategy and Delivery | Genentech
“If AI can now code and perform quality assurance (QA) tests on its own, developers and QA’s role will begin to become obsolete in the long run. It is important for these people to rethink their skills and how they can provide value to their organization.”
Stacey Taylor - VP, Implementation & Change | Visa
The AI Impact: Measuring Results and ROIs
The next questions to answer are: how certain are you that the results with AI are better or equal to what humans can do? How do you supercharge your people so that they can do more in a shorter amount of time? First, it is crucial to remember that measuring the ROI and productivity impact of GenAI solutions in your organization will highly depend on your use cases.
“Set specific goals, benchmarks, and timeframes for certain achievements rather than trying to swallow the whole elephant in one bite.”
Jeffrey Barth - Strategic Advisor | The Rube Goldberg Institute for Innovation & Creativity
“Come up with a methodological plan for every unique case and define productivity before identifying if and how GenAI can improve it.”
Mary Fairchild - VP of Culture and Talent Development | F5
“Utilizing AI+Human for critical thinking tasks. This way, one of the ROIs of AI is helping to upskill people’s decision-making skills which in return steers the business in the right direction.”
Laurie Ditch - Sr. Director, Organizational Strategy and Operations | iMerit
Step 1: Identifying the Baseline
Establish a clear baseline of current productivity and output without GenAI. Identify this data through quantifiable metrics such as response time, resolution rate, or throughput for specific workflows. Additional metrics could include the average time employees spend searching for information daily or the volume of tickets resolved without any errors.
Step 2: Setting Benchmarks
Once baseline data has been established, set achievable benchmarks and goals. For instance, the organization can aim to improve ticket resolution speeds by 50-60 times or reduce the time spent searching for information by a significant percentage.
“It is crucial to bucket and simplify processes. Break the goals down into smaller, measurable outcomes to make the process tangible and to create sustainable habits for using AI effectively.”
Melissa Wood - VP Change Management and Organizational Development | New York Life Insurance Company
Step 3: Analyzing Results
Finally, analyze the results through a combination of quantitative and qualitative metrics to measure the success of the implementation. Quantitative metrics may include a reduction in time spent on solving a specific issue, cost savings, and a reduction in error rates. Qualitative insights might be employee sentiment and adoption rate. Tie both metrics back to the baseline and your anticipated benchmark to provide a clear picture of how AI solutions have improved the organization’s operations and ROI.
For example, you might focus on these three measurable steps shared by Melissa Wood, to track the effectiveness of your AI adoption:
By following these three steps, your organization can not only measure success but also refine its AI strategies for continuous improvement.
Data Management & Privacy: Approaches to AI Governance & Regulations
1. Ensuring Data Privacy
✔ Early Policy Setups
Establishing AI governance policies early in the implementation process helps ensure alignment with the organizational principles and compliance requirements. Policies need to address key issues that are often discussed such as data privacy, AI usage boundaries, and ethical concerns related to this technology.
This includes forming governance councils with diverse stakeholders to help oversee implementation and adherence. Proactive policy development prevents misuse of sensitive data and ensures AI systems operate within ethical and legal boundaries from the very beginning.
✔ Identifying Boundaries
Clear usage boundaries around data usage and interaction with AI are essential in maintaining trust and compliance. The organization needs to define what types of data are permissible for AI interaction, especially when dealing with sensitive or client-related information.
“Know who is accessing what data, with access control. Strict access control and monitoring is important to ensure data use aligns with legal standards.”
Akshaya Murthy - Director, AI Transformation | Zendesk
2. Regulations & Policies
✔ Identify Restrictions or Limitations
Regulatory compliance plays a central role in AI governance, especially for highly regulated industries such as healthcare, media, and finance. Stay informed with the latest AI-related laws which are increasingly becoming stringent, especially when concerning data privacy and ethical usage.
“For entertainment companies, ownership of content IP is core to the business model. Therefore, integrating AI into the workplace should be done in even more thoughtful ways.”
Andranik Ziyalyan - Executive Director, Finance Transformation - Organizational Enablement | Sony Pictures Entertainment
Conclusion
Leading AI transformations requires a balanced approach that considers technological potential, organizational readiness, and ethical responsibilities. Actively embedding AI into your organization's operations can help the business unlock immense value by redefining its capabilities and positioning itself as a pioneer in their industry.
“People that don’t embrace emails no longer exist in this age, so a similar outcome will probably happen with AI adoption in the long run. What’s important is for each organization to determine how and where AI fits within their business model and begin to scale adoption, one step at a time.”
Stacey Taylor - VP, Implementation & Change | Visa
The Executive Council for Leading Change
The Executive Council for Leading Change (ECLC) is a global organization that brings executives together to redefine the landscape of organizational change and transformation. Our council aims to advance strategic leadership expertise in the realm of corporate change by connecting visionary leaders. It's a place where leaders responsible for significant change initiatives can collaborate, plan, and create practical solutions for intricate challenges in leading large organizations through major shifts.
In a world where change is constant, we recognize its crucial role in driving business success. ECLC’s mission is to create a community where leaders can excel in guiding their organizations through these dynamic times.
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