AI adoption is no longer a question of if, but how fast and how well. Most organisations are exploring AI in some form, but they’re moving at very different speeds.
The ones seeing the most value share a few traits: cross-functional collaboration, strong leadership sponsorship, and tight alignment between business and tech. That’s how they sharpen focus, deploy critical skills where it matters, and accelerate from idea to outcome.
But the gap between ambition and execution is real. As one executive put it, “We’ve seen digital natives do in 24 hours what takes our industry six months.” The risks of getting it wrong are just as real; think of Zillow’s USD 500M loss from overreliance on flawed AI models.
When done right, AI benefits every part of the organisation; not just data teams.
“Our AI-powered screening for insurance agents fast-tracks candidate selection by analysing resumes and applications to pinpoint top talent.” – HR Leader
“Conversational AI delivers 24/7 customer engagement, instantly resolving queries, easing team workload, and boosting CX.” – CX Leader
“AI transforms work by streamlining workflows and optimising transport routes, making operations faster and smarter.” – Operations Leader
“Using AI to streamline our sales pipeline has cut down the time it takes to qualify leads, enabling our team to focus on closing more deals with greater precision.” – Sales Leader
“We’re unlocking data value: AI agents personalise customer support at scale, while AI-driven network optimisation ensures seamless IT operations.” – Data Science Leader
In the short term, most businesses are focusing on operational efficiency, but the real wins will be in longer-term innovation and financial value.
For tech teams, this means delivering robust, scalable AI systems while supporting responsible experimentation by business teams – all in a fast-moving, high-stakes environment.
However, that’s not easy.
• High Costs. AI requires substantial upfront and operational spend. Without measurable outcomes, it’s hard to justify scaling.
• Security & Governance Risks. AI heightens exposure to bias, misuse, and compliance gaps. Most organisations lack mature guardrails to manage this.
• Regulatory Uncertainty. Shifting global policies make it difficult to design AI systems that are both future-proof and compliant.
• Skills Shortage. There’s a growing gap in AI and data expertise. Without the right talent, even promising use cases falter.
• Data Challenges. AI needs vast, high-quality data, but many organisations struggle with silos, poor lineage, and inconsistent standards.
Yet the toughest obstacles aren’t technical.
• Limited AI Fluency at the Top. Many leaders lack a practical understanding of AI’s capabilities and constraints, slowing decisions and making cross-functional alignment difficult.
• No Clear Ownership or Strategy. Without clear ownership, AI efforts remain scattered across IT, innovation, and business teams, leading to fragmentation, misalignment, and stalled progress.
• Unclear ROI and Benefits. AI’s value isn’t always immediate or financial. Without clear metrics for success, it’s hard to prioritise initiatives or secure sustained investment.
• Short-Term Pressure. The push for quick wins and fast ROI often comes at the expense of long-term thinking and foundational investments in AI capabilities.
• Rigid Business Models. AI demands adaptability in processes, structures, and mindsets. But legacy workflows, technical debt, and organisational silos frequently stand in the way.
• Change Management is an Afterthought. Many AI efforts are tech-first, people-later. Without early engagement and capability building, adoption struggles to gain traction.
Bridging the Innovation-AI Gap: The Power of Ecosystems
Bridging this gap between AI ambitions and success requires more than technology; it needs a coordinated ecosystem of vendors, enterprises, startups, investors, and regulators working together to turn innovation into real-world impact.
Public-private partnerships are key. In Singapore, initiatives like IMDA’s Spark and Accreditation programmes tackle this head-on by spotting high-potential startups, rigorously validating solutions, and opening doors to enterprise and government procurement. This approach de-risks adoption and speeds impact.
• For Enterprises. It means quicker access to trusted, local solutions that meet strict performance and compliance standards.
• For Startups. It unlocks scale, credibility, and funding.
• For the Economy. It creates a future-ready digital ecosystem where innovation moves beyond the lab to drive national competitiveness and growth.
Australia is making meaningful progress on its digital journey, driven by a vibrant tech sector, widespread technology adoption, and rising momentum in AI. But realising its full potential as a leading digital economy will depend on bridging the skills gap, moving beyond surface-level AI applications, accelerating SME digital transformation, and navigating ongoing economic uncertainty. For many enterprises, the focus is shifting from experimentation to execution, using technology to drive efficiency, resilience, and measurable outcomes.
Increasingly, leaders are asking not just how fast Australia can innovate, but how wisely. Strategic choices made now will shape a digital future grounded in national values where technology fuels both economic growth and public good.
These five key realities capture the current state of Australia’s technology landscape, based on insights from Ecosystm’s industry conversations and research.
1. Responsible by Design: Australia’s Path to Trusted AI
AI in Australia is progressing with a strong focus on ethics and public trust. Regulators like ASIC and the OAIC (Office of the Australian Information Commissioner) have made it clear that AI systems, especially in banking, insurance, and healthcare, must be transparent and fair. Banks like ANZ and Commonwealth Bank, have developed responsible AI frameworks to ensure their algorithms don’t unintentionally discriminate or mislead customers.
Yet a clear gap remains between ambition and readiness. Ecosystm research shows nearly 77% of Australian organisations acknowledge progress in piloting real-world use cases but worry they’re falling behind due to weak governance and poor-quality data.
The conversation around AI in Australia is evolving beyond productivity to include building trust. Success is now measured by the confidence regulators, customers, and communities have in AI systems. The path forward is clear: AI must drive innovation while upholding principles of fairness, transparency, and accountability.
2. The New AI Skillset: Where Data Science Meets Compliance and Context
Australia is on track to face a shortfall of 250,000 skilled workers in tech and business by 2030, according to the Future Skills Organisation. But the gap isn’t just in coders or engineers; it’s in hybrid talent: professionals who can connect AI development with regulatory, ethical, and commercial understanding.
In sectors like finance, AI adoption has stalled not due to lack of tools, but due to a lack of people who can interpret financial regulations and translate them into data science requirements. The same challenge affects healthcare, where digital transformation projects often slow down because technical teams lack domain-specific compliance and risk expertise.
While skilled migration has rebounded post-pandemic, the domestic pipeline remains limited. In response, organisations like Microsoft and Commonwealth Bank are investing in cross-skilling employees in AI, cloud, and risk management. Government initiatives such as CSIRO’s Responsible AI program and UNSW’s AI education efforts are also working to build talent fluent in both technology and ethics.
Despite these efforts, Australia’s shortage of hybrid talent remains a critical bottleneck, shaping not just how fast AI is adopted, but how responsibly and effectively it is deployed.
3. Beyond Coverage: Closing the Digital Gap for Regional Australia
Australia’s vast geography creates a uniquely local digital divide. Despite the National Broadband Network (NBN) rollout, many regional areas still face slow speeds and outages. The 2023 Regional Telecommunications Review found that over 2.8 million Australians remain without reliable internet access. Industries suffer tangible impacts. GrainCorp, a major agribusiness, uses AI to communicate with workers during the harvest season, but regional connectivity gaps hinder real-time monitoring and analytics. In healthcare, the Royal Flying Doctor Service reports that poor internet reliability in remote areas undermines telehealth consultations, particularly crucial for Indigenous communities.
Efforts to address these gaps are underway. Telstra launched satellite services through partnerships with Starlink and OneWeb to cover remote zones. However, these solutions often come with prohibitive costs, particularly for smaller businesses, farms, and community organisations that cannot afford private network infrastructure.
The implications are clear: without reliable and affordable internet, regional enterprises will struggle to adopt AI, cloud-based systems, and digital tools that drive efficiency and equity. The next step must be a coordinated approach involving government, telecom providers, and industry, focused not just on coverage, but on quality, affordability, and support for local innovation. Bridging this digital divide is not simply about infrastructure, it’s about ensuring inclusive access to the tools that power modern business and essential services.
4. Resilience Over Defence: Australia’s Evolving Cybersecurity Focus
Australia’s cyber landscape has shifted sharply following major breaches like Optus, Medibank, and Latitude Financial, which pushed cybersecurity to the top of national agendas. In response, regulators and organisations have adopted a more urgent, coordinated stance. Under the Security of Critical Infrastructure (SOCI) Act, critical sectors must now report serious incidents within hours, enabling faster, government-led responses and stronger collective resilience.
Organisations across sectors are stepping up their defences, moving from reactive measures to proactive preparedness. NAB confirmed that it spends over USD 150M annually on cybersecurity, focusing on real-time threat hunting, simulation exercises, and red teaming. Telstra continues to run annual “cyber war games” involving IT, legal, and crisis communications teams to prepare for worst-case scenarios.
This collective focus signals a broader shift across Australian industries: cybersecurity maturity is no longer judged by perimeter defence alone. Instead, resilience – an organisation’s ability to detect, respond, and recover swiftly – is now the benchmark for protecting critical assets in an increasingly complex threat landscape.
5. Designing for the Long Term: Sustainability as a Core Capability
Organisations across Australia are under growing pressure – not only from regulators, but also from investors, customers, and communities – to demonstrate that their digital strategies are delivering real environmental and social outcomes. The bar has shifted from ESG disclosure to ESG performance. Technology is no longer just an efficiency lever; it’s expected to be a catalyst for sustainability transformation.
This expectation is especially acute in Australia’s core industries, where environmental impact is both material and highly scrutinised. In mining, for example, Rio Tinto’s 20-year renewable energy deal with Edify Energy aims to cut emissions by up to 70% at its Queensland aluminium operations by 2028. But the focus on transition is not limited to high-emission sectors. In financial services, institutions are actively supporting the shift to a low-carbon economy, from setting long-term net-zero targets to aligning lending practices with climate goals, including phasing out support for high-emission assets.
Yet for many, the path forward is still fragmented. ESG data often sits in silos, legacy systems constrain visibility, and ownership of sustainability metrics is scattered. Digital transformation efforts that treat ESG as an add-on, rather than embedding it into the foundations of data, governance, and decision-making, risk missing the mark. Australia’s next digital frontier will be measured not just by innovation, but by how effectively it enables a low-carbon, inclusive, and resilient economy.
Shaping Australia’s Digital Future
Australia’s technology journey is accelerating, but significant challenges must be addressed to unlock its full potential. Moving beyond basic digitalisation, the country is embracing advanced technologies as essential drivers of economic growth and productivity. Strong government initiatives and investments are creating a foundation for innovation and building a highly skilled digital workforce. However, overcoming barriers such as talent shortages, infrastructure gaps, and governance complexities is critical. Only by tackling these obstacles head-on and embedding technology deeply across organisations of all sizes can Australia transform automation into true data-driven autonomy and new business models, securing its position as a global digital leader.
The latest shift in AI takes us beyond data analysis and content generation. With agentic AI we are now seeing systems that can plan, reason, act autonomously, and adapt based on outcomes. This shift marks a practical turning point for operational execution and strategic agility.
Smart On-Ramps for Agentic AI
Technology providers are rapidly maturing their agentic AI offerings, increasingly packaging them as pre-built agents designed for quick deployment. These often require minimal customisation and target common enterprise needs – onboarding assistants, IT helpdesk agents, internal knowledge copilots, or policy compliance checkers – integrated with existing platforms like Microsoft 365, Salesforce, or ServiceNow.
For example, a bank might deploy a templated underwriting agent to pre-screen loan applications, while a university could roll out a student support bot that flags at-risk learners and nudges them toward action. These plug-and-play pilots let organisations move fast, with lower risk and clearer ROI.
Templated agents won’t suit every context, particularly where rules are complex or data is fragmented. But for many, they offer a smart on-ramp: a focused, contained pilot that delivers value, builds momentum, and lays the groundwork to scale Agentic AI more broadly.
Here are 10 such opportunities – five cross-industry and five sector-specific – ideal for launching agentic AI in your organisation. Each addresses a real-world pain point, with measurable impact and momentum for broader change.
Horizontal Use Cases
1. Employee Onboarding & Integration Assistant
An AI agent that guides new hires through their critical first weeks and months by answering FAQs about company policies, automating paperwork, scheduling introductory meetings, and sending personalised reminders to complete mandatory training, all integrated with HRIS, LMS, and calendaring systems. This can help reduce the administrative load on HR teams by handling repetitive onboarding tasks, potentially freeing up significant time, while also improving new hire satisfaction and accelerating time-to-productivity by providing employees with better support and engagement from day one.
Consideration. Begin with a specific department or a targeted hiring wave. Prioritise roles with high turnover or complex onboarding needs. Ensure HR data is clean and accessible, and policy documents are up to date.
2. Automated Meeting Follow-ups & Action Tracking
With permission, AI agents can listen to virtual meetings, identify key discussion points, summarise decisions, extract and assign action items with deadlines, and proactively follow up via email or collaboration platforms like Slack or Teams to help ensure tasks are completed. By integrating with meeting platforms, project management tools, and email, this can reduce the burden of manual note-taking and follow-up, potentially saving team members 1-2 hours per week, while also improving execution rates and accountability to make meetings more action-focused.
Consideration. Deploy with a small, cross-functional team that has frequent meetings. Clearly communicate the agent’s role and data privacy protocols to ensure user comfort and compliance.
3. Intelligent Procurement Assistant
An agent that interprets internal requests, initiates purchase orders, compares vendor options against predefined criteria, flags potential compliance issues based on policies and spending limits, and manages approval workflows, integrating with ERP systems, vendor databases, and internal policy documents. This can help accelerate procurement cycles, reduce manual errors, and lower the risk of non-compliant spending, potentially freeing procurement specialists to focus more on strategic sourcing rather than transactional tasks.
Consideration. Begin with a specific category of low-to-medium value purchases (e.g., office supplies, standard software licenses). Define clear, rule-based policies for the agent to follow.
4. Enhanced Sales/Outreach Research Agent
Given a target account, citizen segment, or potential beneficiary profile, this agent autonomously gathers and synthesises insights from CRM data, public financial records, social media, news feeds, and industry reports. It then generates tailored talking points, personalised outreach messages, and intelligent discovery questions for human operators. This can provide representatives with deeper insights, potentially improving their preparation and boosting early-stage conversion rates, while reducing manual research time significantly and allowing teams to focus more on building relationships.
Consideration. Train the agent on a specific sales vertical or a targeted public outreach campaign. Ensure robust data privacy compliance when accessing and synthesising public information.
5. Proactive Internal IT Helpdesk Agent
This agent enables employees to describe technical issues in natural language through familiar platforms like Slack, Teams, or internal portals. It can intelligently troubleshoot problems, guide users through self-service solutions from a knowledge base, or escalate more complex issues to the appropriate IT specialist, often pre-filling support tickets with relevant diagnostic information. This approach can lead to faster issue resolution, reduce the number of common support tickets, and improve employee satisfaction with IT services, while freeing IT staff to focus on more complex problems and strategic initiatives.
Consideration. Start with a well-documented set of frequently asked questions (FAQs) or common Tier 1 IT issues (e.g., password resets, VPN connection problems). Ensure a clear escalation path to human support.
Industry-Specific Use Cases
6. Intelligent Insurance Claims Triage (Insurance)
This agent reviews incoming insurance claims by processing unstructured data such as claim descriptions, photos, and documents. It automatically cross-references policy coverage, identifies missing information, and assigns priority or flags potential fraud based on predefined rules and learned patterns. This can speed up initial claims processing, reduce the manual workload for claims adjusters, and improve the early detection of suspicious claims, helping to lower fraud risk and deliver a faster, more efficient customer experience during a critical time.
Consideration. Focus on a specific, high-volume, and relatively standardized claim type (e.g., minor motor vehicle damage, simple property claims). Ensure robust data integration with policy management and fraud detection systems.
7. Automated Credit Underwriting Assistant (Banking)
An AI agent that pre-screens loan applications by gathering and analysing data from internal banking systems, external credit bureaus, and public records. It identifies key risk factors, generates preliminary credit scores, and prepares initial decision recommendations for human loan officers to review and approve. This can significantly shorten loan processing times, improve consistency in risk assessments, and allow human underwriters to concentrate on more complex cases and customer interactions.
Consideration. Apply this agent to a specific, well-defined loan product (e.g., unsecured personal loans, small business loans) with clear underwriting criteria. Strict human-in-the-loop oversight for final decisions is paramount.
8. Clinical Trial Workflow Coordinator (Healthcare)
This agent monitors clinical trial timelines, tracks participant progress, flags potential non-compliance or protocol deviations, and coordinates tasks and communication between research teams, labs, and regulatory bodies. Integrated with Electronic Health Records (EHRs), trial management systems, and regulatory databases, it helps reduce delays in complex clinical workflows, improves adherence to strict protocols and regulations, and enhances data quality, potentially speeding up drug development and patient access to new treatments.
Consideration. Focus on a single phase of a trial or specific documentation compliance checkpoints within an ongoing study. Ensure secure and compliant access to sensitive patient and trial data.
9. Predictive Maintenance Scheduler (Manufacturing)
By continuously analysing real-time IoT sensor data from machinery, this agent uses predictive analytics to anticipate potential equipment failures. It then schedules maintenance at optimal times, taking into account production schedules, spare part availability, and technician workloads, and automatically assigns tasks. This approach can significantly boost machine uptime and overall equipment effectiveness by reducing unplanned downtime, optimize technician efficiency, and extend asset lifespan, resulting in notable cost savings.
Consideration. Implement for a critical, high-value machine or a specific production line where downtime is extremely costly. Requires reliable and high-fidelity IoT sensor data.
10. Personalised Student Success Advisor (Higher Education)
This agent analyses student performance data such as grades, attendance, and LMS activity to identify those at risk of struggling or dropping out. It then proactively nudges students about upcoming deadlines, recommends personalised learning resources, and connects them with tutoring services or academic advisors. This support can improve retention rates, contribute to better academic outcomes, and enhance the overall student experience by providing timely, tailored assistance.
Consideration. Start with a specific cohort (e.g., first-year students, transfer students) or focus on a particular set of foundational courses. Ensure ethical data usage and transparent communication with students about the agent’s role.
Pilot Success Framework: Getting Started Today
As we have seen in the considerations above, starting with a high-impact, relatively low-risk use case is the recommended approach for beginning an agentic AI journey. This focuses on strategic, measured steps rather than a massive initial overhaul. When selecting a first pilot, organisations should identify projects with clear boundaries – specific data sources, explicit goals, and well-defined actions – avoiding overly ambitious or ambiguous initiatives.
A good pilot tackles a specific pain point and delivers measurable benefits, whether through time savings, fewer errors, or improved user satisfaction. Choosing scenarios with limited stakeholder risk and minimal disruption allows for learning and iteration without significant operational impact.
Executing a pilot effectively under these guidelines can generate momentum, earn stakeholder support, and lay the groundwork for scaling AI-driven transformation throughout the organisation. The future of autonomous operations begins with such focused pilots.
AI is not just reshaping how businesses operate — it’s redefining the CFO’s role at the centre of value creation, risk management, and operational leadership.
As stewards of capital, CFOs must cut through the hype and ensure AI investments deliver measurable business returns. As guardians of risk and compliance, they must shield their organisations from new threats — from algorithmic bias to data privacy breaches with heavy financial and reputational costs. And as leaders of their function, CFOs now have a generational opportunity to modernise finance, champion AI adoption, and build teams ready for an AI-powered future.
LEAD WITH RIGOUR. SAFEGUARD WITH VIGILANCE. CHAMPION WITH VISION.
That’s the CFO playbook for AI success.
Click here to download “AI Stakeholders: The Finance Perspective” as a PDF.
1. Investor & ROI Gatekeeper: Ensuring AI Delivers Value
CFOs must scrutinise AI investments with the same discipline as any major capital allocation.
- Demand Clear Business Cases. Every AI initiative should articulate the problem solved, expected gains (cost, efficiency, accuracy), and specific KPIs.
- Prioritise Tangible ROI. Focus on AI projects that show measurable impact. Start with high-return, lower-risk use cases before scaling.
- Assess Total Cost of Ownership (TCO). Go beyond upfront costs – factor in integration, maintenance, training, and ongoing AI model management.
Only 37% of Asia Pacific organisations invest in FinOps to cut costs, boost efficiency, and strengthen financial governance over tech spend.
2. Risk & Compliance Steward: Navigating AI’s New Risk Landscape
AI brings significant regulatory, compliance, and reputational risks that CFOs must manage – in partnership with peers across the business.
- Champion Data Quality & Governance. Enforce rigorous data standards and collaborate with IT, risk, and business teams to ensure accuracy, integrity, and compliance across the enterprise.
- Ensure Data Accessibility. Break down silos with CIOs and CDOs and invest in shared infrastructure that AI initiatives depend on – from data lakes to robust APIs.
- Address Bias & Safeguard Privacy. Monitor AI models to detect bias, especially in sensitive processes, while ensuring compliance.
- Protect Security & Prevent Breaches. Strengthen defences around financial and personal data to avoid costly security incidents and regulatory penalties.
3. AI Champion & Business Leader: Driving Adoption in Finance
Beyond gatekeeping, CFOs must actively champion AI to transform finance operations and build future-ready teams.
- Identify High-Impact Use Cases. Work with teams to apply AI where it solves real pain points – from automating accounts payable to improving forecasting and fraud detection.
- Build AI Literacy. Help finance teams see AI as an augmentation tool, not a threat. Invest in upskilling while identifying gaps – from data management to AI model oversight.
- Set AI Governance Frameworks. Define accountability, roles, and control mechanisms to ensure responsible AI use across finance.
- Stay Ahead of the Curve. Monitor emerging tech that can streamline finance and bring in expert partners to fast-track AI adoption and results.
CFOs: From Gatekeepers to Growth Drivers
AI is not just a tech shift – it’s a CFO mandate. To lead, CFOs must embrace three roles: Investor, ensuring every AI bet delivers real ROI; Risk Guardian, protecting data integrity and compliance in a world of new risks; and AI Champion, embedding AI into finance teams to boost speed, accuracy, and insight.
This is how finance moves from record-keeping to value creation. With focused leadership and smart collaboration, CFOs can turn AI from buzzword to business impact.
Large organisations in the banking and financial services industry have come a long way over the past two decades in cutting costs, restructuring IT systems and redefining customer relationship management. And, as if that was not enough, they now face the challenge of having to adapt to ongoing global technological shifts or the challenge of having to “do something with AI” without being AI-ready in terms of strategy, skills and culture.
Most organisations in the industry have started approaching AI implementation in a conventional way, based on how they have historically managed IT initiatives. Their first attempts at experimenting with AI have led to rapid conclusions forming seven common myths. However, as experience with AI grows, these myths are gradually being debunked. Let us put these myths to a reality check.
1. We can rely solely on external tech companies
Even in a highly regulated industry like banking and financial services, internal processes and data management practices can vary significantly from one institution to another. Experience shows that while external providers – many of whom lack direct industry experience – can offer solutions tailored to the more obvious use cases and provide customisation, they fall short when it comes to identifying less apparent opportunities and driving fundamental changes in workflows. No one understands an institution’s data better than its own employees. Therefore, a key success factor in AI implementation is active internal ownership, involving employees directly rather than delegating the task entirely to external parties. While technology providers are essential partners, organisations must also cultivate their own internal understanding of AI to ensure successful implementation.
2. AI is here to be applied to single use cases
In the early stages of experimenting with AI, many financial institutions treated it as a side project, focusing on developing minimum viable products and solving isolated problems to explore what worked and what didn’t. Given their inherently risk-averse nature, organisations often approached AI cautiously, addressing one use case at a time to avoid disrupting their broader IT landscape or core business. However, with AI’s potential for deep transformation, the financial services industry has an opportunity not only to address inefficiencies caused by manual, time-consuming tasks but also to question how data is created, captured, and used from the outset. This requires an ecosystem of visionary minds in the industry who join forces and see beyond deal generation.
3. We can staff AI projects with our highly motivated junior employees and let our senior staff focus on what they do best – managing the business
Financial institutions that still view AI as a side hustle, secondary to their day-to-day operations, often assign junior employees to handle AI implementation. However, this can be a mistake. AI projects involve numerous small yet critical decisions, and team members need the authority and experience to make informed judgments that align with the organisation’s goals. Also, resistance to change often comes from those who were not involved in shaping or developing the initiative. Experience shows that project teams with a balanced mix of seniority and diversity in perspectives tend to deliver the best results, ensuring both strategic insight and operational engagement.
4. AI projects do not pay off
Compared to conventional IT projects, the business cases for AI implementation – especially when limited to solving a few specific use cases – often do not pay off over a period of two to three years. Traditional IT projects can usually be executed with minimal involvement of subject matter experts, and their costs are easier to estimate based on reference projects. In contrast, AI projects are highly experimental, requiring multiple iterations, significant involvement from experts, and often lacking comparable reference projects. When AI solutions address only small parts of a process, the benefits may not be immediately apparent. However, if AI is viewed as part of a long-term transformational journey, gradually integrating into all areas of the organisation and unlocking new business opportunities over the next five to ten years, the true value of AI becomes clear. A conventional business case model cannot fully capture this long-term payoff.
5. We are on track with AI if we have several initiatives ongoing
Many financial institutions have begun their AI journey by launching multiple, often unrelated, use case-based projects. The large number of initiatives can give top management a false sense of progress, as if they are fully engaged in AI. However, investors and project teams often ask key questions: Where are these initiatives leading? How do they contribute? What is the AI vision and strategy, and how does it align with the business strategy? If these answers remain unclear, it’s difficult to claim that the organisation is truly on track with AI. To ensure that AI initiatives are truly impactful and aligned with business objectives, organisations must have a clear AI vision and strategy – and not rely on number of initiatives to measure progress.
6. AI implementation projects always exceed their deadlines
AI solutions in the banking and financial services industry are rarely off-the-shelf products. In cases of customisation or in-house development, particularly when multiple model-building iterations and user tests are required, project delays of three to nine months can occur. This is largely because organisations want to avoid rolling out solutions that do not perform reliably. The goal is to ensure that users have a positive experience with AI and embrace the change. Over time, as an organisation becomes more familiar with AI implementation, the process will become faster.
7. We upskill our people by giving them access to AI training
Learning by doing has always been and will remain the most effective way to learn, especially with technology. Research has shown that 90% of knowledge acquired in training is forgotten after a week if it is not applied. For organisations, the best way to digitally upskill employees is to involve them in AI implementation projects, even if it’s just a few hours per week. To evaluate their AI readiness or engagement, organisations could develop new KPIs, such as the average number of hours an employee actively engages in AI implementation or the percentage of employees serving as subject matter experts in AI projects.
Which of these myths have you believed, and where do you already see changes?
