AI Risk Assessments: What Every Business Needs Before Automating

Artificial intelligence (AI) tools are transforming the way businesses operate. Automating repetitive tasks, streamlining complex workflows, and enabling more informed decisions across sales, HR, customer service, and operations. But while the promise of AI automation is exciting, moving too fast without proper oversight can lead to costly missteps.

From compliance issues to security gaps and data bias, implementing AI systems without a structured risk assessment is like launching a new product without QA. At TechElevate, we work with businesses across Australia as fractional CIOs, helping them integrate AI technologies responsibly while balancing innovation with risk management.

In our latest guide, we’ll explore what an AI risk assessment actually involves, how to identify and mitigate potential risks before automating, and how your business can build a smarter, safer AI future, without compromising on compliance, performance, or public trust.

Business Process Automation and How AI Is Changing the Game

What is business process automation? 

In its traditional form, business process automation (BPA) involves using technology to streamline repetitive tasks and workflows that once required human effort. Think invoice processing, employee onboarding, or customer support ticketing.

But AI is revolutionising the landscape. AI automation tools, particularly those powered by machine learning algorithms, natural language processing, and generative AI, are enabling businesses to move beyond static scripts and rigid rules.

Today’s AI-powered automation is dynamic, adaptive, and intelligent. Whether you’re managing customer queries with AI chatbots, optimising human resources through smart resume screening, or using sentiment analysis to triage customer feedback, AI automation tools are now core to how modern business operations function.

These tools can:

• Create automated workflows across departments
• Enhance decision making by analysing vast data sets
• Boost productivity by reducing the need for manual follow-ups
• Empower teams to focus on strategic, high-value work

As more businesses integrate AI systems into their processes, the ability to automate tasks, improve efficiency, and scale operations has become a competitive necessity.

Risk Assessments: What Are They and Why Do They Matter?

What are AI risk assessments?

An AI risk assessment is a structured process for identifying and evaluating potential threats, vulnerabilities, and failures that could arise during the implementation or use of AI technologies.

While automation promises cost savings, boosting efficiency, and even instant answers, these benefits can unravel, fast, without a clear understanding of the possible risks.

Risks can include:

• Inaccurate outputs from flawed AI models
• Biased decision-making from incomplete training data
• Security threats such as unauthorised access or data leakage
• Compliance violations due to poor data handling or explainability gaps

Even more concerning are edge cases: unpredictable outcomes that occur in unusual circumstances, especially within complex workflows or when AI is applied to sensitive tasks like healthcare, hiring, or finance.

It shouldn’t just be a box checking task, performing a well thought out AI risk assessment allows businesses to make more informed decisions, reduce potential financial losses, and maintain smooth operations.

What is the AI model risk management?

AI Model Risk Management, or AI RMF, is a structured set of practices and policies used to identify, manage, and minimise the risks associated with AI projects.

Developed by the National Institute of Standards and Technology (NIST), the AI RMF provides guidance for:

• Evaluating the trustworthiness of AI systems
• Managing risk across the AI lifecycle (development, deployment, maintenance)
• Ensuring regulatory requirements and industry standards are met
• Improving transparency and oversight in AI decision-making

Globally, frameworks like the EU AI Act and ISO/IEC standards are gaining traction. These guidelines emphasise the importance of:

• Explainability
• Accountability
• Bias mitigation
• Risk documentation and audits

At TechElevate, we support businesses by applying these frameworks in practice, ensuring that every AI deployment is not only effective but also compliant and secure.

Case Study: What Happened When Australia’s Biggest Bank Got AI Wrong

In mid-2025, Commonwealth Bank made headlines for rolling out AI-driven job automation across its operations, only to walk it back weeks later.

Initial reports revealed that the bank used AI technologies to justify dozens of job cuts. The bank later reversed those cuts, calling the decision an “error,” stating they “did not adequately consider all relevant business considerations and admit the original redundancy decision lacked sufficient rigour; “We should have been more thorough in our assessment of the roles required.”

The backlash was swift. Staff morale plummeted, media scrutiny intensified, and customers raised concerns about the bank’s over-reliance on AI agents to manage critical business processes.

Eventually, CBA admitted that a more cautious, consultative approach was needed.

A TechElevate CIO would have:

• Flagged early-stage compliance issues and workforce impacts
• Created a cross-department risk plan with oversight from executives
• Applied AI RMF practices to ensure ethical, secure deployment
• Advised on HITL (Human-in-the-Loop) options to ensure a full review of critical decisions

This public example shows how high-profile companies can risk brand trust, internal stability, and operational continuity when they fail to align AI strategy with proper governance.

How to identify an Automated Business Process and When Should You Assess It for AI Risks

Consider a returns management system in retail: an AI agent might analyse customer emails, apply natural language processing, and approve refunds based on a set of rules and past behaviour.

However, without assessing the risk of fraud, data misuse, or inappropriate approvals, the automation can backfire.

Risk assessments should happen:

• Before development begins (to define requirements and constraints)
• During deployment (to monitor for real-world issues)
• After launch (ongoing updates to address emerging risks)

What Makes AI Risk Management Different from Traditional IT Risk?

AI doesn’t behave like traditional software. It learns, adapts, and often functions as a “black box” where machine learning models make decisions that are hard to trace.

Key differences:

• AI outputs are probabilistic, not deterministic
• Input data constantly changes (e.g., real-time customer queries)
• Models can be influenced by natural language prompts, tone, and phrasing
• Risk may emerge from the system’s own evolution (e.g., generative AI generating unexpected outputs)

This means organisations need:

• Stronger access controls
• Version-controlled AI models
• Audits for bias, fairness, and transparency
• Enhanced training for staff and developers

Choosing the Right Tools: AI Automation Solutions That Address Risk First

Safe and scalable AI automation tools should prioritise the following:

• Explainability: Can you trace the AI’s decision?
• Role-based Access: Is data only seen by authorised users?
• Transparent Data Use: Are inputs and sources clearly disclosed?
• Compatibility: Can it integrate with your existing systems?
• Human-in-the-Loop (HITL): Can a person review or override critical decisions?

At TechElevate, our CIOs help businesses choose tools that not only improve efficiency but reduce risk. We run vendor evaluations, stress-test solutions, and ensure alignment with enterprise goals.

AI Risk Audits: Why You Need a CIO or vCIO on Board

AI implementation isn’t just plug and play. It’s a strategic, operational, and reputational concern. When it comes to implementing and assessing new IT infrastructure like this a TechElevate CIO helps your team:

• Conduct AI readiness reviews
• Guide responsible AI deployment (addressing things like privacy concerns and security)
• Oversee compliance with industry standards
• Manage third-party risks through vendor oversight
• Stress test your solution against frameworks tailored to your specific business

As experts in enterprise transformation, our CIOs give you the structure and support needed to deploy AI with confidence.

Use AI with Confidence, Not Blind Faith

AI systems and business process automation offer undeniable benefits: from productivity boosts to unlocking savings while allowing your teams to focus on more meaningful work.

But these outcomes only happen when businesses take the time to understand and manage the risks.

Whether you’re creating your first AI solution or scaling existing systems, TechElevate offers the leadership and expertise to ensure you work with AI responsibly.

Reach out to our team of experienced CIOs today to discuss how we can support your next AI-powered automation initiative.