Businesses have always sought better ways of working, and to some extent, they’ve succeeded. Software as a service transformed the deployment of new tools. Remote work took down barriers to talent and collaboration. Agility reshaped teams into responsive, customer-focused value delivery units.
However, rarely have these factors fundamentally changed a company’s overall operating model. Most businesses still organize around how work is done rather than what the work achieves. Traditional enterprise technology may make tasks easier, but it’s still driving processes, not outcomes.
It’s not news to anyone that you can have great tech-powered processes and still fail to deliver meaningful outcomes. But the status quo was arguably workable so long as AI outputs were constrained by the availability of scarce, highly trained specialists. Then generative AI made AI-driven decisions directly accessible to anyone.
The other shoe dropped with the introduction of agentic AI, enabling systems to take on increasingly complex tasks.
As with other digital breakthroughs — think smartphones, cloud computing, the internet or the graphical user interface — AI isn’t likely to be intrinsically differentiating. You can expect competitors to have similar capabilities. This means the winning edge will go to businesses that understand the best way to deploy AI. And to enable that, businesses must revisit their operating models with an AI-first, outcome-oriented mindset.
In the rest of this Executive Insights, we’ll show you what an AI-first organization looks like, discuss the approaches that some companies have taken to being AI-first and share a few tips to consider as you evaluate your own organizational construct.
Defining the AI-first organization
Let’s start with what “AI first” is not. It’s not about building a dedicated AI unit. Nor is it an evolution of the digital-first organization, which emphasizes the use of digital platforms such as customer relationship management systems, apps and digital supply chains.
An AI-first organization recognizes that not every decision needs to be made by a human and that the decisions humans do make are often improved with AI input. In the consumer industry, for example, human creativity delivers up to 20 times more high-interest innovations when paired with AI augmentation.
At the same time, you can’t simply provide workers with access to AI capabilities and expect that alone to yield transformative results. To go from AI-enabled to AI-first, the workplace must be redesigned to get the most out of adding AI as a team member.
This means doing away with rigid structures and process-driven centralization, which are poorly suited to dynamic, real-time, unstructured intelligence and evolving beyond just agile pods. Instead, an AI-first organization deploys AI as a team member focused on outcomes connecting people and AI to deliver high-quality outcomes collaboratively (see Figure 1).
