A Plateau Plan to Become AI-Native

Author(s): Bram Nauts Originally published on Towards AI. AI will not transform because it’s deployed – it will transform because the way of operating is redesigned. The tricky part? Transformations rarely fail at the start, they fail in the middle – when organisations try to scale. In a previous article I defined the concept of the AI-native bank. A bank where decisions, processes and customer interactions are continuously driven by AI. Since publishing that article, one question came up repeatedly: “How do we actually get there?” Before exploring that question, it is important to acknowledge something. The idea of AI-native organisations is still largely a promise. The potential of AI is enormous, but the long-term economics and risk profile of AI-driven companies are still emerging. Some initiatives will deliver extraordinary value. Others will fail to scale. But despite this uncertainty, one thing is becoming increasingly clear. The opportunity is too large to ignore. Across industries, AI is beginning to reshape how companies operate. Technology firms are embedding AI into decision-making. Digital platforms are automating complex processes. New entrants are building organizations designed around AI from day one. If this shift continues – and all signs suggest it will – the competitive landscape will change dramatically. Companies that operationalize AI effectively will unlock: faster decision cycles, lower operational costs, more personalized customer experiences and changing business models. Companies that move too late risk something more dangerous than short-term inefficiency. They risk becoming structurally slower than their competitors. The real question therefore is not whether they should experiment with AI. They already are. The real question is: “How do organisations move deliberately toward becoming AI-native?”. Therefore in this article I will outline the best practices I apply: 1) Create a plateau plan to guide and lay out the journey, and; 2) Apply transformational best practices to navigate change. The Plateau Journey Toward an AI-Native Organisation Transformation in general don’t progress smoothly. Organisations move through plateaus. At each plateau the organization evolves: adoption, foundation and value creation. Understanding these plateaus helps leaders identify where they are – and what must happen next. Plateau 1 – Exploration and Foundation Most organizations begin their AI journey through experimentation. Teams explore use cases such as document processing and internal productivity copilots. The goal is learning. Scope of this plateau at a minimum A set target of use cases A top-down cost reduction target to start embedding value AI Governance aimed at AI Act compliance, key internal and external risks and increasingly literacy Testing scalability of the Data & AI platforms and other foundations The kill list Stop any use case without business owner and contribution to the value case – ensure strategic alignment Stop any shadow AI – get in control What success looks like pilots deliver measurable improvements teams gain confidence in AI employees begin using AI leadership recognises strategic potential Foundation capabilities are tested and their improvements areas are clear Typical bottlenecks Experimentation quickly exposes structural issues: fragmented data leading to AI using ungoverned data unclear roles & responsibilities delaying decisions and misaligning priorities limited AI literacy hampering true adoption of the AI use cases KPIs to set and track # high-impact use cases in production Time to production % of total population related to the use case using the AI use case #AI risks identified, incidents & ethical #Lessons learned implemented Leadership question to be answered: Where are we seeing real value from AI – and which experiments should become strategic priorities? Plateau 2 – Strategic Verticalisation The second plateau begins when organisations stop asking: “Where else can we experiment with AI?” and start asking: “Where can AI fundamentally transform our business?”. Investment concentrates on a few high-impact areas. In banking these often include: • customer servicing • financial crime and KYC • credit and investments • operations Scope of this plateau 3–5 Holistic value areas to deploy AI end-to-end across disciplines Modernised data & AI platform focussed AI-ready-data (e.g. investing in knowledge graphs and vector databases) Explicit governance with guardrails focussing on accelerating and controlling what matters The kill list Stop batch decisioning (overnight risk & fraud) and manual case handling – move to realtime to harness the benefit of AI and dare to stop the ‘old way of working’ Freeze all unrelated use cases – move top down on the values areas – and focus your talents and experts where it matters Stop an experimentation setting and demand AI systems to be monitored and promote sharing learnings What success looks like • entire journeys related to the value areas redesigned around AI • faster decision cycles • improved customer experience • meaningful cost reduction Typical bottlenecks • Central AI and data platform constraints hampering fast deployments • Unclear AI governance, clear on paper but not harnessed in practice • Lack of alignment between business and technology priorities hampering speed Leadership question to be answered: Which few domains should we transform with AI – and are we focusing our investments strongly enough there? Plateau 3 – Enterprise-Scale AI Acceleration Once AI becomes critical across multiple areas, the organisation must evolve its ability to deliver AI at scale. AI becomes a repeatable enterprise capability. Equally important is solidifying the foundation, and with that put focus on transforming leadership and the workforce. Employees must learn how to work with AI systems and leaders need to become extremely bold to push change. What success looks like • AI solutions move rapidly from development to production • AI systems are continuously monitored and improved • AI-ready-data products are reused across teams • AI becomes part of daily operations The kill list Process-and-system based operating model – organise around AI Product-and-discipline centric silo’s – organise around AI Typical bottlenecks • resistance from the workforce • unclear and not formalised risk appetite • uncontrolled “citizen AI” experimentation leading to more risks Leadership question to be answered: Can our organization reliably and responsibly deliver and scale AI solutions? Plateau 4 – AI-Native Operations At the final plateau AI […]