{"id":64741,"date":"2026-04-13T08:32:03","date_gmt":"2026-04-13T08:32:03","guid":{"rendered":"https:\/\/www.elixirr.com\/en-gb\/?p=64741"},"modified":"2026-04-13T08:46:03","modified_gmt":"2026-04-13T08:46:03","slug":"how-to-set-your-organisation-up-to-successfully-embrace-the-power-of-ai","status":"publish","type":"post","link":"https:\/\/www.elixirr.com\/en-gb\/how-to-set-your-organisation-up-to-successfully-embrace-the-power-of-ai\/","title":{"rendered":"How to set your\u00a0organisation\u00a0up to successfully embrace the power of AI\u00a0"},"content":{"rendered":"\n
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This is the second in a two-part series on how\u00a0organisations\u00a0can successfully embrace AI.\u00a0In the first piece, we explored why many\u00a0organisations\u00a0remain\u00a0stuck in fragmented AI activity without achieving scale. Here, we turn to the more important question: what does it take to structure an\u00a0organisation\u00a0to capture AI value in practice?<\/span>\u00a0<\/span><\/p>\n

Once AI is understood in those terms, the structural question moves to the\u00a0centre\u00a0of the discussion.\u00a0If AI is to be delivered safely and repeatedly across the enterprise, what structure is actually needed?\u00a0Where should\u00a0prioritisation\u00a0sit? Who owns trade-offs across business value, reuse, speed,\u00a0control\u00a0and\u00a0risk? Who decides which initiatives\u00a0move\u00a0forward, which are exceptions, and\u00a0which need to be stopped? Who is accountable when an AI solution moves into\u00a0production\u00a0and something goes wrong?<\/span>\u00a0<\/span><\/p>\n

These are not secondary governance questions. They sit at the heart of whether AI\u00a0remains\u00a0a fragmented set of experiments or becomes a repeatable\u00a0organisational\u00a0capability. The structure an\u00a0organisation\u00a0chooses is therefore not a cosmetic choice. It is one of the main determinants of whether AI becomes embedded into the way the enterprise\u00a0operates.<\/span>\u00a0<\/span><\/p>\n

That is also why the debate about whether to appoint a Chief Data and AI Officer, create a time-bound AI executive role, or\u00a0establish\u00a0an\u00a0AI Control Tower matters. But it matters in context. Those are not the starting\u00a0point\u00a0of the discussion. They are\u00a0possible answers\u00a0to the broader\u00a0operating-model\u00a0question. The danger is to jump too quickly to titles. A new executive role can sound like decisive action while leaving the underlying operating model untouched. But the opposite mistake is just as risky: assuming that AI can simply be absorbed into existing structures without changing how the enterprise coordinates,\u00a0governs\u00a0and delivers it.<\/span>\u00a0<\/span><\/p>\n

Why a standalone AI leader is not always the best answer<\/h2>\n

For some\u00a0organisations, appointing a senior executive such as a CDAIO or CAIO may be the right move. That is most likely where the enterprise genuinely lacks a single point of authority across strategy,\u00a0prioritisation, governance,\u00a0adoption\u00a0and\u00a0production accountability. In those circumstances, a senior role can create focus, remove\u00a0ambiguity\u00a0and force alignment across functions that do not naturally move together.<\/span>\u00a0<\/span><\/p>\n

But it is important to be precise about what\u00a0that role\u00a0is solving. A technology-led AI leader, sitting primarily as an extension of the tech stack, does not automatically resolve the harder business questions around workflow redesign, process ownership, role evolution, value\u00a0realisation\u00a0and\u00a0frontline adoption. If the problem is enterprise transformation, then even a senior AI leader will struggle unless the remit is explicitly broader than technology.<\/span>\u00a0<\/span><\/p>\n

The wider market evidence points in the same direction. Gartner reported in 2025 that 70% of chief data and analytics officers have primary responsibility for building their\u00a0organisation\u2019s\u00a0AI strategy and operating model. That suggests\u00a0organisations\u00a0do want identifiable leadership for AI, but many are solving the problem by evolving existing roles rather than defaulting to a standalone Chief AI Officer.<\/span>\u00a0<\/span><\/p>\n

That is why a standalone AI leader is not always the best answer. The risk is not only duplication with existing technology or data roles,\u00a0although\u00a0that is real. The bigger risk is that the\u00a0organisation\u00a0treats AI as if it belongs to a specialist office rather than to the enterprise. Once that happens, business leaders can distance themselves from the redesign\u00a0challenge,\u00a0operational teams can assume the AI team owns delivery,\u00a0and technology becomes the default lens through which the transformation is run. That is precisely the wrong outcome for a shift that is fundamentally about business\u00a0model, workflow,\u00a0control\u00a0and human performance.<\/span>\u00a0<\/span><\/p>\n

The case for an AI Control Tower<\/h2>\n

This is where the notion of an AI Control Tower becomes particularly useful. Properly understood, it is not just another forum, a\u00a0centre\u00a0of excellence with no teeth, or a rebranding of existing committees. It is an orchestration mechanism for enterprise AI. It exists to provide the connective tissue the\u00a0organisation\u00a0is otherwise missing: one portfolio view, one\u00a0prioritisation\u00a0logic, one place for exception decisions, one reusable blueprint\u00a0library\u00a0and\u00a0clear accountability for what is deployed into production.<\/span>\u00a0<\/span><\/p>\n

In many institutions, the core ingredients are already present. Data and technology\u00a0functions\u00a0own platforms,\u00a0infrastructure\u00a0and engineering reliability. Business and transformation functions own adoption, process\u00a0redesign\u00a0and\u00a0value\u00a0realisation. What is often missing is a single spine that can sequence priorities, arbitrate trade-offs, manage\u00a0exceptions\u00a0and assign production accountability across the\u00a0whole system. The Control Tower is designed to provide exactly that.<\/span>\u00a0<\/span><\/p>\n

But it only works if it carries real authority. Without that, an AI Control Tower quickly becomes a talk shop: a place where standards are discussed, priorities are\u00a0debated\u00a0and\u00a0risks are noted, but where no one is compelled to follow the decisions made. If it is to work, it must have decision rights that are visible and binding. It must be able to shape sequencing, force initiatives into a common portfolio,\u00a0adjudicate\u00a0exceptions\u00a0and assign accountability across the\u00a0organisation. In effect, it needs to be\u00a0an operating mechanism, not an advisory body. Authority is what prevents coherence from collapsing back into fragmentation.<\/span>\u00a0<\/span><\/p>\n

The harder question: can the core move fast enough?\u00a0<\/span><\/h2>\n

Most leadership teams will understandably want to capture AI value from within the core business, and\u00a0in most cases that should\u00a0remain\u00a0the ambition. The core holds the customers, the operational assets, the regulatory\u00a0infrastructure\u00a0and\u00a0the institutional advantages that matter. The first\u00a0objective\u00a0should therefore be to redesign the existing\u00a0organisation\u00a0so it can move with enough speed,\u00a0discipline\u00a0and clarity to capture the value at stake.<\/span>\u00a0<\/span><\/p>\n

But leadership teams should also be honest about how difficult this transformation is. In some\u00a0organisations, the real barrier is not strategy or technology, but the weight of the incumbent model itself: the incentives, governance routines, approval layers, cultural\u00a0habits\u00a0and legacy ways of working that make reinvention too slow. The point is not that every enterprise should create a separate vehicle or a newco. Most will not, and in many cases should not. The point is that the mere existence of that\u00a0option\u00a0should concentrate the mind. If the core cannot move with enough speed and conviction, the market will not wait for it to catch up.<\/span>\u00a0<\/span><\/p>\n

This matters because the value from AI is unlikely to be\u00a0evenly distributed. BCG\u2019s 2025 research found that only 5% of companies are achieving substantial AI value at scale, and that this small group materially outperforms laggards on revenue growth, shareholder\u00a0returns\u00a0and EBIT margin. The implication is clear:\u00a0organisations\u00a0that combine strategic clarity with speed, scale and\u00a0operating-model redesign are likely to capture a disproportionate share of the value, while those that hesitate may find the market concentrating around faster-moving competitors.<\/span>\u00a0<\/span><\/p>\n

No structural\u00a0option\u00a0works if AI is treated as a tech problem\u00a0<\/span><\/h2>\n

Whether an\u00a0organisation\u00a0appoints a CDAIO,\u00a0establishes\u00a0a time-bound AI executive mandate, or creates a Control Tower, the same underlying truth\u00a0remains:\u00a0this is not simply a technology problem.<\/span>\u00a0<\/span><\/p>\n

A CDAIO will fail if the role is treated as an extension of the tech function rather than a lever for business\u00a0redesign. A temporary AI executive will fail if the mandate is limited to experimentation and does not reshape how value is governed and delivered. A Control Tower will fail if it lacks authority or if it is disconnected from the business\u00a0processes\u00a0it is meant to influence. The success condition in all three cases is the same: the structure must be designed to change how the\u00a0organisation\u00a0operates,\u00a0not just how it experiments with AI.<\/span>\u00a0<\/span><\/p>\n

That is the point many\u00a0organisations\u00a0still underestimate. AI is not merely about new tooling. It is about redesigning the conditions under which work gets done. It changes how decisions are made, how\u00a0expertise\u00a0is distributed, how controls are\u00a0embedded\u00a0and how humans and machines collaborate. If the\u00a0organisation\u00a0treats it as a specialist technology agenda, it will\u00a0almost certainly\u00a0under-deliver.<\/span>\u00a0<\/span><\/p>\n

Regulation matters, but it does not change the core question<\/h2>\n

The regulatory environment is relevant, especially in financial services, but it should remain in proportion. The\u00a0important point\u00a0is not that one specific rulebook\u00a0determines\u00a0the correct structural model. It is that AI is increasingly being drawn into an existing web of obligations around privacy, fairness, model risk, conduct,\u00a0governance\u00a0and operational resilience. That raises the importance of disciplined operating-model choices, but it does not turn the question into a purely compliance-driven exercise.\u00a0Regulation reinforces the need for clarity,\u00a0control\u00a0and accountability. It does not substitute for them.<\/span>\u00a0<\/span><\/p>\n

The questions leadership teams should be asking\u00a0<\/span><\/h2>\n

The organisations that succeed with AI are rarely the ones with the most pilots, the loudest strategy statements, or the most impressive titles. They are the ones willing to ask harder questions about how the enterprise is set up to deliver change of this magnitude.<\/span>\u00a0<\/span><\/p>\n

1. Are we driving toward specific business outcomes, or simply generating AI activity?<\/span><\/span>\u00a0<\/span><\/strong><\/h3>\n

A long list\u00a0of use cases is not a strategy. A clear view of where AI should change economics, customer experience, control, or productivity is.<\/p>\n

2. Are we deploying AI into existing workflows, or genuinely redesigning around what AI makes possible?<\/span><\/b>\u00a0<\/span><\/h3>\n

This is often the line between incremental improvement and structural advantage. If workflows,\u00a0roles\u00a0and\u00a0handoffs\u00a0remain\u00a0untouched, most of the value will remain inaccessible.<\/span>\u00a0<\/span><\/p>\n

3. Do we have the authority structure\u00a0required\u00a0to move beyond PoC purgatory?<\/span><\/b>\u00a0<\/span><\/h3>\n

Who decides what gets\u00a0prioritised? Who has the right to stop duplicative efforts? Who owns exceptions? Who is accountable\u00a0in\u00a0production? If the answers are vague, fragmented, or dependent on informal influence,\u00a0scale\u00a0will be elusive.<\/span>\u00a0<\/span><\/p>\n

4. Are our most experienced people genuinely embracing AI-augmented ways of working, or merely complying while protecting the old model?<\/span><\/b>\u00a0<\/span><\/h3>\n

Human adoption is not a side issue in AI transformation. It is one of the central determinants of whether value is ever\u00a0realised.<\/span>\u00a0<\/span><\/p>\n

5. Is our governance built for the capabilities we are\u00a0actually deploying, rather than for\u00a0a previous\u00a0generation of technology?<\/span><\/b>\u00a0<\/span><\/h3>\n

As AI systems become more autonomous, embedded\u00a0and\u00a0business-critical, the operating model around oversight, controls and accountability\u00a0has to\u00a0evolve with them.<\/span>\u00a0<\/span><\/p>\n

6.\u00a0Is\u00a0our investment and operating plan\u00a0calibrated\u00a0to where AI capability is heading, not just where it is today?<\/span><\/b>\u00a0<\/span><\/h3>\n

AI is advancing too quickly for static, multi-year planning assumptions to hold. The\u00a0organisations\u00a0that win will be the ones able to absorb change without\u00a0organisational\u00a0drift.<\/span>\u00a0<\/span><\/p>\n

7. Can we capture this opportunity at the speed\u00a0required\u00a0from within the core, or are our current structures preventing that?<\/span><\/b>\u00a0<\/span><\/h3>\n

For some\u00a0organisations, the greatest barrier to AI transformation is not the technology but the incumbent model itself. Leadership teams need to ask, honestly, whether the core can move fast enough to capture the value at stake.<\/span>\u00a0<\/span><\/p>\n

The real choice<\/span><\/b>\u00a0<\/span><\/h2>\n

The real choice facing leadership teams is not whether to do AI. That question has already been answered. The real choice is whether the\u00a0organisation\u00a0is prepared to put in place the operating model, authority and leadership discipline\u00a0required\u00a0to redesign work around what AI makes possible.<\/span>\u00a0<\/span><\/p>\n

That is what separates\u00a0organisations\u00a0using AI from\u00a0organisations\u00a0being transformed by it. The winners will not be those with the most pilots or the most elegant\u00a0organisational\u00a0charts. They will be the ones that\u00a0recognise\u00a0AI for what it is: not a software upgrade, but a business transformation challenge that demands a structural response.<\/span>\u00a0<\/span><\/p>\n

The\u00a0organisations\u00a0that succeed will be the ones that make the operating-model choices early, assign real authority, move beyond AI\u00a0theatre\u00a0and build the conditions for AI to create measurable value in production. In some cases, that will mean redesigning the core. In others, it may mean creating space outside it. But in all cases, the winners will be those that\u00a0recognise\u00a0that the value at stake is large, unevenly\u00a0distributed\u00a0and unlikely to wait for slow-moving institutions to catch up.<\/span>\u00a0<\/span><\/p>\n <\/div>\n <\/div>\n<\/section>\n\n\n\n

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\n Learn more<\/span><\/a> <\/div>\n <\/div> <\/div>\n\n <\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":31,"featured_media":64749,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[116],"tags":[478],"related_service":[45],"related_industry":[69,62],"class_list":["post-64741","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-ai-machine-learning"],"acf":[],"yoast_head":"\nHow to set your\u00a0organisation\u00a0up to successfully embrace the power of AI\u00a0<\/title>\n<meta name=\"description\" content=\"Learn how to structure your organisation for AI success, from governance to operating models, and move beyond pilots to scale real business value.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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