Turning AI ambition into practical, measurable business value
This is the second article in a series on what it takes to adopt AI in a way that is practical, secure and genuinely valuable. Over the coming posts, we will explore the strategic, technical and organisational foundations that help organisations move from isolated experimentation to lasting business value. If you missed the first article, you can find it here: AI Adoption Is Not Just Tool Deployment – It Is a Transformation.
Many organisations and technology leaders are asking themselves: How can we harness the power of AI and get the maximum value from its potential?
That is a very important question in today’s world. But first, I want to highlight one important point: if you think your organisation is not already adopting or using AI, that is probably not the case.
Tools like ChatGPT, Microsoft Copilot and others are already being used by many people. Even Google Search now has an AI mode that gives users direct answers and invites them to continue chatting, rather than only searching through websites and references.
But the real value of AI comes when it has a direct impact on the organisation’s profit, which can happen in three main ways: improving customer value and increasing customers’ willingness to pay, reducing costs, or increasing volume by attracting more customers.
Profit = (Willingness to Pay – Cost) × Volume
Any AI initiative should improve at least one of these three factors. Otherwise, it may be interesting, but it may not create real business value. So do not start with AI for the sake of AI. Start with the value you want to create, and do not begin by randomly looking for AI use cases.
This blog post offers a methodical approach to addressing this dilemma. It doesn’t cover how to execute AI strategic initiatives; it focuses on how to define them.
The Two Dimensions of AI Initiatives
Let’s start by clarifying that AI has two main dimensions:
- The first dimension is augmentation. This means using AI to support humans and help them do more. Examples include document summarisation, better search, pattern recognition, predictions, decision-making support, coaching, and many other use cases.
- The second dimension is automation. This is where AI works on its own to automate certain functions, tasks, processes, or even parts of decision-making, with or without a human in the loop.
These two dimensions provide a lens for generating and selecting your initiatives. Every AI initiative you identify will sit somewhere on the spectrum between augmentation and automation. Once you understand this, as a leader in the organisation, you need to clarify the objectives of your AI strategy, which I believe will fall into one or more of the following three objectives:
Objective 1: Enable AI Across the Organisation
One objective, which I am sure most organisations will have, is to enable everyone in the organisation to use AI safely and effectively, particularly through the augmentation lens. This is because if you do not enable people to use AI, they will likely use it anyway. So it is better to do it properly.
This objective involves having the right rules, guidelines, policies, and approved tools. It also means procuring tools that allow people to use company data safely and confidently and providing the training they need.
With this objective, the technology leader’s responsibility is to enable and coordinate so AI can be used across the organisation, across all business units, and by different people in ways that make sense for their roles.
The hidden challenge within this objective is what happens when personal tools become enterprise dependencies. Do you need a centralised release process? A formal testing process? A shared platform? A standard user experience? These are important questions to consider early, before small solutions become business-critical systems.
For example, you may give your finance manager a Claude subscription. The finance manager may then start building tools and workflows using AI to perform financial analysis and produce reports for management and the board. That may be useful, but it also creates risk. If that person leaves, the logic behind those reports may only be known to them. The new finance manager may then need to work out how the previous reports were built, what assumptions were used, and whether the outputs can still be trusted.
Another example is when a team member builds a reporting agent to produce a specific report with specific metrics, and that report starts being used across the organisation. If the data source is not accurate, or if the metrics have not been agreed across the organisation, then the effort may create confusion rather than value.
Always remember to differentiate between something built for personal use and something built for enterprise use. Once a solution is used across the organisation, it needs the right governance, testing, documentation, and ownership.
In profit terms, this objective rarely increases willingness to pay or volume on its own. Its value is mostly on the cost side through productivity gains across the organisation and in avoiding the hidden costs of ungoverned AI: rework, compliance exposure, and decisions built on tools nobody owns.
Objective 2: Use AI to Support Business Strategy
The second objective is to ask: How can we use AI to support our business?
Technology is always an enabler of the corporate strategy, its initiatives, and the strategies of each business unit. It is very important to identify how AI can support those strategies to define the right AI strategic initiatives.
To illustrate this objective, let’s take a hypothetical manufacturing organisation. One of its strategic objectives is to build three new factories in three different locations. The questions is “How can technology, and specifically AI, support this strategic initiative?”.
The starting point is to understand the objective in more detail. Why are these factories being built? What business problem are they solving? What outcomes does the organisation expect? Is the goal to increase production capacity, reduce supply chain risk, enter new markets, improve delivery speed, or reduce operating costs?
Once you understand the objective, you can start identifying where AI may help. The output could include ideas such as:
- AI can support simulation and optimisation of factory layouts to improve material flow, reduce waste, and increase operational efficiency.
- AI can help model supplier options, shipping routes, inventory levels, and potential disruptions. This can reduce risk before the factories are operational.
- AI can analyse project plans, budgets, supplier dependencies, and previous project data to identify risks early and support better decision-making.
The key point is that you are not starting with AI use cases in isolation. You are starting with a strategic business objective, then asking how AI can help achieve it faster, cheaper, better, or with less risk.
Depending on the strategy it supports, this objective can pull any of the three profit levers and identify initiatives in both augmentation and automation lenses. In the factory example, AI-driven layout optimisation reduces cost, supply chain modelling protects volume, and faster delivery can lift what customers are willing to pay. The lever is determined by the business objective, not by the technology.
Objective 3: Use AI to Redefine the Business Model
The third objective to consider is: How can we use AI to completely update, change, or redefine our business?
This is where you need to look at your business model. You may use tools such as SWOT analysis and business model canvas to identify where AI can create new opportunities or change how the business operates.
This cannot be done by the technology team alone. It needs to be done with other business leaders.
This third objective is the most complex and innovative one to achieve, but it is the one that, done correctly, returns the maximum value.
A real-world example is Stitch Fix. Instead of operating like a traditional online fashion retailer where customers browse and select products themselves, Stitch Fix built a model where AI and human stylists work together to curate personalised clothing recommendations. This changed the value proposition from “buy clothes online” to “receive personalised styling advice and curated products.” AI supports customer personalisation, demand prediction, inventory decisions, and stylist productivity. In this case, AI did not just improve an internal process; it helped shape a different business model. Though the model faced execution challenges, it illustrates the structural shift.
This is the objective that focuses on willingness to pay. This one changes what the customer is buying and that is where the equation moves the most.
Conclusion
Whichever objective you focus on, whether it is enabling everyone to use AI, supporting your corporate and business unit strategies, or redefining your business model; remember that none of them is just a tool installation project. Each is a transformation that requires alignment between people, process, and technology, a topic I cover in the first article in this series: https://skillfield.com.au/blog/ai-adoption-treat-it-like-transformation-not-tool-deployment/
In practice, most organisations need all three at once, just with different weights. Objective 1 is the foundation almost everyone should start building now. Objective 2 should drive the bulk of your AI investment, because it is anchored to strategy to which you have already committed. Objective 3 deserves a smaller, deliberate bet as it carries the most risk, but it is where the maximum value sits.
The organisations that will benefit most from AI are not necessarily the ones that experiment the most. They are the ones that connect AI to strategy, manage it as a transformation, and focus on measurable business value. They are the ones asking themselves, “What value do we need to create from AI?” instead of “Where can we use AI?”.
Author: Mouaz Alnouri
Author Bio
Mouaz Alnouri is the CEO of Skillfield, an Australian IT consultancy specialising in AI, cyber security and data services. With a background spanning technology leadership, complex delivery and organisational transformation, Mouaz writes about the practical realities of adopting emerging technologies in business. His focus is on helping leaders cut through hype, make better decisions and build the foundations needed for technology to create lasting value.
Further Reading:
https://skillfield.com.au/blog/ai-adoption-treat-it-like-transformation-not-tool-deployment/
https://skillfield.com.au/blog/why-every-business-needs-an-ai-strategy/
