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Organizational Change Imperatives for Driving Gen AI Value

Organizational Change Imperatives for Driving Gen AI Value

Generative AI (Gen AI) promises immense value. However, the technology alone is not enough.

Published Mar 11, 2024
This paper was written in collaboration with Anthropic Claude on Amazon Bedrock.

Introduction

The advent of generative AI represents a watershed moment for business. By automating tasks, surfacing insights and accelerating decision velocity, Gen AI can drive major cost and time reductions, unlock new revenue opportunities and sharpen competitive advantage. According to McKinsey, it could potentially deliver over $3 Trillion in annual value. However, these outcomes are by no means guaranteed. Although the technology promises immense potential, realizing and sustaining value requires organizational change. Gen AI solutions do not operate in isolation; they get woven into business operations, augmenting processes and people. To capitalize on Gen AI's possibilities, companies must build organizational capacities to rapidly adopt Gen AI across the business and nurture a high velocity experimentation culture that spurs innovation. We see two imperatives for organizational change:
1. Build robust capabilities to accelerate Gen AI adoption: Proactively manage a complex set of people-focused changes around roles, skills, decision rights, controls and more.
2. Foster an experimentation culture: Encourage creativity, learn from failures, rapidly prototype new use cases and scale successes.
Mastering these two imperatives is essential to outcompete rivals in capturing Gen AI's potential. Laggard firms will cede advantage; proactive organizations willing to evolve will win. We expand on each imperative below, outlining the key changes to drive Gen AI value.
Imperative 1: Build Capabilities to Accelerate Adoption
Gen AI promises immense potential. But potential is worthless without realization. Leaders must build organizational capacities to rapidly adopt Gen AI across business functions, ensuring solutions integrate into operations and decision-making. This requires managing a complex set of changes: establishing Gen AI as a strategic priority, adapting processes and roles to leverage new technologies, upskilling workforces, implementing controls around data and ethics, fostering human-AI collaboration and more. Prosci research affirms this necessity, showing organizations highly capable in managing change are six times more likely to meet objectives. We see eight key changes to accelerate Gen AI adoption:
1. Signal leadership commitment: Leaders must clearly communicate Gen AI's strategic importance through words, resources and actions. This convinces workforces to embrace change.
2. Adapt processes and roles: Gen AI will transform tasks and activities. Proactively realign processes, roles and responsibilities to optimize human-AI collaboration.
3. Clarify decision authority: Provide frameworks for determining when Gen AI can drive autonomous decisions versus human oversight.
4. Close skill gaps: Assess workforce readiness and provide upskilling/reskilling to address needs around data, AI development and ethics.
5. Structure collaboration: Implement organizational structures and communication flows optimizing human-AI teaming to make decisions.
6. Manage displacement: Where Gen AI automates jobs, mitigate impacts through transition support and training.
7. Instill governance: Establish guidelines, controls and oversight to ensure ethical, responsible Gen AI use.
8. Adhere to standards: Ensure Gen AI solutions meet requirements around safety, quality, explainability and unbiased outcomes.
Change is complex. Attempting transformation without consciously addressing these people-focused dynamics risks failure. Leaders must proactively realign organizational components to ready the business.
Imperative 2: Foster a Culture of Experimentation
Beyond rapidly adopting current use cases, leaders must build a culture encouraging employees to creatively experiment with Gen AI to uncover new applications and opportunities. This requires psychological safety, creativity, and learning from failures.**** We see four steps to build this culture:
1. Incentivize experimentation: Implement formal and informal rewards for experimenting, highlighting benefits of innovation and lessons from failures.
2. Lower barriers: Provide infrastructure, tools and datasets enabling employees to easily build prototypes.
3. Share discoveries: Facilitate information flows on experiment techniques, use cases and results.
4. Scale successes: Once experiments show value, rapidly productize and disseminate across the organization.
A robust experimentation culture also requires leaders role modeling desired mindsets and behaviors, celebrating failed efforts that teach important lessons, and directing resources to the most promising ideas.
Rather than keeping Gen AI restricted to small expert teams, transform it into a capability and mindset permeating the organizational fabric. This unlocks immense potential for innovation.

Conclusion

Generative AI allows unprecedented automation, insight and decision velocity. But technology alone is not enough. Leaders must consciously build organizational capacities accelerating adoption and fostering experimentation. Proactive management of people-focused changes around processes, skills, controls and culture dictates which firms realize Gen AI's full potential. For a more holistic perspective on accelerating adoption to the new ways of working when moving from the current state to a future state of cloud transformation, please see the AWS Change Acceleration 6-Point Framework and Organizational Change Management Toolkit.
The lessons are clear - organize for change or risk defeat. The race for advantage is underway; leaders must choose whether to lead or fall behind.