Digital Economy Dispatch #191 -- How to Deliver Value from AI-at-Scale

Digital Economy Dispatch #191 -- How to Deliver Value from AI-at-Scale
7th July 2024

The buzz surrounding AI adoption in large organizations across both public and private sectors is undeniable. High expectations are swirling about the speed and impact of AI, with ambitious goals set for return on investment. For example, a study from the Alan Turing Institute views AI as a key driver of massive productivity gains in the UK Government, potentially automating 84% of repetitive tasks and transforming the civil service landscape within the next 15 years.

But will these aspirations become reality?

Serious doubts are emerging. The latest comments in The Economist from 6th July 2024 asks some very challenging questions about whether AI will have the economic effects expected. Under the title “What happened to the AI revolution?”, the article points to the lack of data showing positive impact from AI and quotes a census from the US Census Bureau that only 5% of businesses in the US used AI in the past 2 weeks. In a similar study in Canada it was 6%. The article concludes that enterprise concerns such as security and privacy are stalling the rollout of AI in larger organizations. While small-scale use of AI elsewhere is not resulting in gains beyond a few narrow use cases in customer service and marketing areas.

The true challenge lies in effectively scaling AI adoption. And for this to succeed, critical questions need answers: How can we translate learnings from pilot projects into enterprise-wide transformations? What obstacles must be overcome to seamlessly integrate AI into existing workflows? How can early successes build sustainably into measurable, substantial benefits?

What is clear is that leaders and decision makers are struggling to address these questions. To move forward they need guidance that recognizes their specific issues and challenges of AI adoption, they need a set of concepts that help they understand the risks and opportunities they face in using AI technologies, they want to see examples of AI success, and they must acquire knowledge of the core capabilities of AI to help them ask better questions of what’s available, what’s just around the corner, and what‘s still a long way off being deployable.

Surviving and Thriving in the Age of AI: Your Guide to Success

This is the focus for my new book, “Surviving and Thriving in the Age of AI”. It is a handbook aimed at leaders and decision makers who want to understand more about successfully delivering AI-at-Scale. A key focus of the book is how to deliver value from AI-at-Scale.

Deploying AI across complex organizations presents significant challenges. Any substantial change inherently faces resistance, and for large established organizations (LEOs) pursuing disruptive digital transformations, these barriers can be particularly formidable.

Where can we turn for guidance? Here's where learnings from previous large-scale digital shifts, especially those involving agile methodologies, can prove invaluable.

Agile methodologies, known for their iterative development and rapid feedback loops, have become a cornerstone of modern software development. While core principles are well-established, achieving "Agile at Scale" presents its own set of hurdles. Scaling agile goes beyond simply adopting practices; it requires optimizing processes for collaboration across diverse stakeholders and integrating new ways of working into complex, legacy environments.

Initial agile adoption often starts with enthusiastic developer teams, but scaling these successes across the organization requires overcoming several key hurdles, offering valuable insights for AI-at-Scale:

  • Resistance to Change: Traditional, plan-driven mindsets can clash with agile's emphasis on dynamic planning and rapid iterations, creating a divide. Misaligned incentive models and project metrics can further exacerbate tensions.

  • Misaligned Support Teams: Resource managers, financial teams, and other supporting functions may struggle to adapt to agile's less rigid planning and progress tracking, perceiving it as disruptive to established workflows.

  • Middle Management Challenges: Project managers, analysts, and other mid-level roles may feel threatened by the potential loss of control associated with empowered agile teams. A lack of understanding of agile team dynamics and progress can lead to mistrust across different levels.

Lessons Learned from Agile at Scale for Successful AI Adoption

Understanding these broad characteristics of “agile-at-scale” is important for anyone looking to successfully deploy AI. But to have effect, we must look deeper. What should be the key principles guiding leaders and decision makers as they look to scale their AI efforts?

The experience of scaling agile offers valuable insights for organizations navigating large-scale AI deployment. Both initiatives require more than just implementing new technologies or practices; they necessitate a cultural shift that embraces structural reforms, leadership adaptation, and substantial collaboration across departments.

Empowerment Over Disruption

A key lesson is to focus scaling efforts on empowerment rather than disruption. Similar to concerns around agile replacing traditional project management roles, fears of AI displacing human workers can hinder adoption. By framing AI as a tool to enhance existing functions and empowering stakeholders to understand its contribution, organizations foster a more positive and collaborative environment for AI to flourish.

Phased Implementation

Another crucial lesson is the importance of a phased implementation approach when introducing substantive change. Just as agile adoption often starts with successful pilot projects, AI initiatives should begin with targeted use cases that showcase the technology's value proposition. This allows for incremental scaling, continuous improvement, and adaptation of existing workflows to ensure a smooth integration of AI.

Building Bridges

Building bridges between technical teams and supporting functions is critical. Similar to the challenges faced in delivering agile at scale, large-scale AI adoption faces hurdles in fostering open communication and ensuring a clear understanding of how AI tools interact with existing processes and roles.

Moving Forward with AI

Make no mistake, despite the hype, getting value from substantial investments in AI will not be quick nor easy. However, by applying these lessons learned from agile at scale, organizations can navigate the complexities of large-scale AI adoption more effectively. A focus on collaboration, empowerment, and a phased approach are important starting points to unlock AI's full potential while minimizing disruption and maximizing value.

These lessons form a useful core for moving forward. Yet, much more is required from leaders and decision makers to be effective in delivering AI-at-Scale. They must also take on the challenge of delivering AI responsibly, effectively, and sustainably in the context of rapid change we all face today. It not a technological update that is required: It is a business, cultural, and structural shift that requires new ways of thinking and working with substantial implications for every part of the organization.

It is for this reason that my new book, "Surviving and Thriving in the Age of AI: A Handbook for Digital Leaders" focuses on these key lessons and describes how to reframe digital strategy for the age of AI. If you want to learn more about how to navigate the path towards AI at Scale, unlock AI's true potential, and enable your organization to leverage this transformative technology responsibly and effectively, please take a look!