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For mid-sized organisations, key decisions to launch a new product or implement new technology usually begin with a well-considered strategy. Without one, investment decisions are harder to prioritise and returns are more difficult to realise.
Adoption of artificial intelligence (AI), however, has not followed a traditional linear path. In most organisations, AI has gradually crept into everyday operations without a formal strategy or plan behind it.
Where AI tools continue to be developed in an ad-hoc way, they risk overlooking the full value AI can create as well as the risks it can present. Failing to move beyond piecemeal adoption can leave organisations at a disadvantage compared to those that take a deliberate approach to identifying where AI can create the most value and how it can be applied.
Current AI trends and insights
Deploying AI for scalable, strategic impact is fast becoming a business necessity, with several trends emerging among organisations that are achieving success from AI adoption:
- Businesses are moving beyond individual experimentation to embed AI into core processes delivering measurable value.
- Data quality and readiness have emerged as critical AI enablers, with the effectiveness of AI dependent on the quality of information and processes that support it.
- Workforce capability is a key priority, where AI training and disciplined change management are playing a vital role in building the competence and confidence to use it intelligently.
- Responsibility for AI is becoming more formalised through dedicated leadership roles, cross-division working groups, and executive accountability as leaders recognise the need for ownership and governance.
A recent survey of 155 medium to large organisations, published in the RSM Australia 2026 Cyber security report, found mid-sized organisations are leading the way in practices surrounding AI governance. Smaller organisations are lagging behind – perhaps due to limited resources – and larger organisations have room for improvement, especially in workforce capability. Less than half of the respondents from large organisations reported having training programs in place to support the responsible development and use of AI.
Identifying AI use cases
As organisations focus on improving governance, workforce capability and data readiness for successful AI adoption, the next step is to identify where AI can have the most impact and prioritise business initiatives that align with strategic goals and existing capabilities.
One way to identify meaningful use cases is to consider the different ways AI creates value in an organisation.
For example:
- Perception AI draws on sensory inputs such as images, video and audio. It can be used to identify product defects, process handwritten information, or convert speech to text.
- Predictive AI uses historical data to identify patterns and forecast future outcomes. It is commonly used for demand planning, risk assessment, workforce planning, trend forecasting, and so on.
- Generative AI creates new content based on spoken or written prompts. Generative AI is being used across industries and has gained widespread popularity for its ability to draft or edit communications, summarise large quantities of information, generate marketing content, and more.
- Agentic AI goes beyond responding to basic prompts and is able to reason and take action. These systems can complete multi-step tasks, coordinate workflows, and escalate matters for human oversight where required.
Leaders can evaluate these different AI models within the context of their organisation, and the three categories AI use typically falls into:
Personal productivity
Using tools such as Microsoft Copilot to summarise emails, take meeting notes, and assist with drafting content. While simple, these tools offer significant time savings when employees know how to use them effectively.
Business productivity
AI can often help remove friction and bottlenecks from cross-functional processes, reducing manual work and improving data quality. In finance teams, this might include automating invoice processing, assisting with account reconciliations, generating management reports, or identifying anomalies for further investigation. By reducing time spent on repetitive administrative tasks, employees can focus on higher value activities such as analysis and supporting business performance, regardless of whether they are in junior roles or the C-Suite.
Enterprise intelligence
With strong data foundations in place, AI is capable of uncovering deeper insights through forecasting and predictive analysis to help leaders anticipate change and make astute decisions. Business leaders are likely to find opportunities in each of these areas and can build momentum through quick wins before pursuing more sophisticated applications over time.
In addition to understanding what AI can do and where it can create value, leaders also need to be clear about why they are pursuing it. Identifying use cases and maximising AI's impact is most effective when guided by a defined process:
- Business outcomes - clearly articulate the strategic objectives and challenges you wish to address.
- Framework - establish consistent processes to gather, assess and prioritise feedback.
- Ideation - run workshops to explore potential challenges and identify suitable use cases.
- Cross-functional collaboration - engage teams from different parts of the organisation to align on shared priorities and needs.
- Trends and benchmarking - monitor competitor activity and new developments in data and automation.
- Leverage existing resources - review existing data to uncover patterns, opportunities and inefficiencies.
Selecting the right use cases for AI in your organisation
A business, experience and technology framework will help organisations prioritise the use cases most likely to succeed.
Factors such as impact, feasibility and fit enable leaders to distinguish the highest value opportunities and avoid applying AI in situations where simpler solutions would make more sense.
Successful AI initiatives also need a clear link to business value. Demonstrating tangible outcomes helps build stakeholder confidence and supports sustainable adoption and scale. Generally, AI initiatives create value in these three ways:
- Run the organisation - Improve efficiency and productivity through workflow enhancements and personal productivity tools to reduce costs and free up employee time.
- Protect the organisation - Build resilience through stronger controls and deeper trust in the information used to manage risk and meet regulatory obligations.
- Grow the organisation - Use AI-driven insights to accelerate growth by identifying opportunities and informing strategic priorities.
Currently there are three primary models, with each suited to different use cases and offering varying degrees of flexibility, complexity, control and data considerations:
Custom AI solutions are built specifically for an organisation using its own data and enterprise AI development platforms. They offer the highest level of flexibility and control – allowing organisations to tailor prompts, permissions, workflows and governance settings to their requirements. While these solutions can support sophisticated use cases and larger datasets, they usually require strong technical expertise and take longer to develop.
Example: IT helpdesk assistant
Integrated into tools such as Microsoft Teams, Slack, internal websites or third party applications, an AI-powered IT assistant can provide step-by-step guidance using the organisation's support knowledge base. It can help employees:
- Troubleshoot issues without leaving the application.
- Perform approved actions such as multifactor authentication resets.
- Escalate unresolved issues by creating support tickets with conversation summaries.
Hybrid AI combines the capabilities of existing platforms with customised functionality to address specific business needs. By connecting multiple systems and data sources, organisations can enhance the tools they already use without building entirely bespoke solutions. This approach offers a balance between flexibility and complexity, and requires a moderate to high level of technical expertise.
Example: Customer insights
Using tools such as Microsoft Fabric and Copilot, organisations can uncover patterns within their data and anticipate future outcomes, such as identifying customers who may be at risk of leaving. Analysts can build models and take action such as sending personalised communications from a single interface.
Example: Transaction automation
By integrating NetSuite with Copilot, employees can complete tasks using natural language prompts. For example, they might create purchase orders, update records or approve transactions through chat commands, where all actions are recorded in NetSuite and supported by a clear audit trail.
Embedded AI refers to the built-in capabilities available within existing business systems – such as enterprise resource planning (ERP), customer relationship management and automation platforms. These solutions are usually faster to deploy and require little to no custom development, though functionality and access to data does vary between vendors.
Increasingly, cloud-based ERPs and complementary solutions are launching embedded AI to assist to automate elements of the month-end close process by preparing journal entries, gathering supporting information from connected systems, and transferring transactions into the ERP. Employees stay involved through review and approval processes before entries are submitted.
When deciding whether to buy or build, organisations should consider the expected business value, feasibility, return on investment and associated risks. Custom solutions offer greater flexibility and control but require significant testing, governance, security and compliance processes. Embedded solutions can often be deployed faster, with many controls managed by the vendor, though organisations still retain responsibility for oversight and use.
AI models and solution design
Once use cases are identified, the next step is to design solutions that deliver reliable outcomes. This starts with trusted enterprise data and a clear understanding of how the technology should operate within the business. In practice, this design process involves four steps:
Trusted information:
AI is only as effective as the information it has to work with. Clean and accurate data is essential for reliable outputs.
Business context:
AI performs best when it understands your organisation's terminology, processes and ways of working so it can generate results that reflect how your business operates.
Appropriate autonomy:
AI can support routine tasks and streamline workflows while people remain responsible for oversight, judgement and exceptions.
Connected processes: More value can be achieved
More value can be achieved when AI initiatives work together to support end-to-end processes rather than individual tasks.
These elements naturally evolve over time through feedback and continual improvement, ultimately leading to greater value realisation from AI.
Partnering for success
Successful AI deployment starts with a clear understanding of the problems an organisation is trying to solve and the outcomes it wants to achieve. Aligning technology choices with business priorities enables organisations to turn what has been ongoing experimentation into intentional and scalable results.
The variety of AI tools and technologies can seem daunting, but business leaders don’t need to navigate the journey alone. Working with experienced technology providers and trusted advisers can help build capability and establish the foundations for long term value.
RSM’s CFO Advisory team supports CFOs and their teams to:
- understand existing AI use within the business
- identify gaps in capability
- develop use cases
- assess the viability of different models
- build out a practical implementation plan
Our goal is to help CFOs achieve greater clarity, confidence, capability and momentum across their AI initiatives so they can realise the full potential of this valuable new technology.
To learn how our CFO Advisory Services team can assist your organisation with strategic AI deployment, please get in touch by filling out the form below.
