Implementing Enterprise Agility for Artificial Intelligence

How agile ways of working are essential for enabling AI that scales

by Erik Lenhard , Jonathan Licis , and Diego Villanueva

It’s easy to see that the majority of large organizations are now competing in a rollercoaster race to unlock the potential of Artificial Intelligence (AI). They’re acutely aware of the need to steal a march on their competitors wherever possible, while also desperate to scale any successful AI implementations across their whole organization as fast and responsible as possible. It’s understandable, given the ultra-rapid pace of technological change.

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What’s less clear is who will gain advantage, and how they’ll do it. Implementing AI beyond initial trials and pilots is challenging, but we believe the most agile organizations will be the ones best positioned to unlock real AI value, and successfully scale it.

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Our research and experience indicates that what really matters is creating alignment and focus, and using agile approaches across teams and empowered individuals. These are the factors that will enable AI-driven success — at scale, and for the long term.?Above all we need to ask: how can a company align people and AI resources to its purpose and objectives, scale this alignment across the whole organization, and then build the ability to reprioritize initiatives quickly enough to become an AI-driven firm?

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The AI explosion context

Organizations are already using AI to improve decision making, enhance customer experience and create novel content. Examples abound, including:

• Microsoft Teams Premium using ChatGPT to automatically generate notes, recommended tasks, and personalized highlights after a meeting.
• OpenAI’s AI-powered DALL-E 2 system is being used by more than 1.5M users, actively creating over two million images a day.
• A multi-institutional team led by Harvard Medical School researchers launched a platform that aims to optimize AI-driven drug discovery by developing more realistic data sets and higher-fidelity algorithms.

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Agile is the way to master the complexity of AI

Human beings empowered by AI will produce unimaginable value — but only to organizations that are able to align it to their overall purpose and strategy. In addition to the infrastructure development, there is a crucial people issue: capturing AI value beyond initial pilots is heavily influenced by the success of an organization’s ability to adapt and align its people’s focus and processes. To unlock the potential of AI, organizations need to redesign cross-functional teams with AI capabilities integrated into them. These teams, which focus on value-driven outcomes rather than plan-driven processes, will not only use AI to produce work but also to learn from it and improve with every iteration.

Despite a narrative that automation might make employees feel redundant or subservient, our research indicates that working with AI often affords individuals more autonomy rather than less. Extensive BCG research conducted in partnership with the MIT Sloan Management Review shows that AI tends to benefit employees within organizations that implement it: for example, we found a 3.4x increased likelihood of job satisfaction among workers who are gaining value from AI. Across industries, we find employees that use AI are feeling more competent in their roles, more autonomous in their actions, and more connected to their work, colleagues, partners, and customers.

We are seeing AI tools emerge that enhance individual autonomy in several ways: by helping individuals learn from past actions, by projecting the outcomes of current actions, by providing salient information about relevant past situations, and by offering feedback on the consequences of past actions that suggest ways to improve performance. In fact, AI often gives individuals greater autonomy by providing guidance and empowering employees to make their own decisions based on recommendations without the direct support of a manager.

Of course, these patterns of increased autonomy will be familiar to anyone who has taken the teachings of agile to heart. However, much of this work has been done at pilot level; the greater challenge is still successful scaling, as we have seen.

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Agile ways of working lead naturally to scalable AI success

Organizations must rise to the challenge of scaling. Tests, trials and pilots are all crucial, but the wider challenge is to scale up AI options that show promise, and to discover which ones they are, as early as possible. For this, agile approaches are essential.

Agile ways of working are innately suited for use in any rapidly evolving environment — and few have ever moved as fast as the AI landscape is currently. And the capabilities that agile organizations have nurtured and grown — such as continuous learning — can be dovetailed into emerging ways of using a technology that is advancing so rapidly. Capturing AI value depends on success in the area of people and processes?— which, of course, is where true agility resides. So it’s natural that agile and AI can be seen at this early stage as already being ‘a match made in heaven.’

When getting underway, small cross functional teams can rapidly ideate and test early hypothesis on product features. AI delivery teams can then focus each sprint on a key goal that validates the approach and direction, rapidly placing value into the hands of customers. After the AI product has been launched, agile operations teams can continuously monitor the AI in the production, enhancing the AI to changes in data and customer behavior.

Agile thrives on complexity by applying frameworks, processes, and principles explicitly designed to drive results and experience in these situations, while ensuring focus and reducing waste. Enterprise Agility for AI brings the best of agile to the complex task of AI delivery, helping ensure success and value, upon which future results can be delivered and scaled.

Those managers who can effectively harness Enterprise Agility for AI will be among the first to extract real, large value for their organisations from this rapidly emerging field, while bringing benefits to their people and process.

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