How to Leverage AI for Business Growth

By Caroline Kennedy | Published July 3, 2026

Business growth has always depended on a company’s ability to adapt. What has changed is the speed at which adaptation now needs to happen.

AI gives organisations a way to move faster without simply asking people to work harder. It can streamline operations, accelerate analysis, personalise customer experiences, reduce administrative drag, and surface insights that would take teams significantly longer to uncover manually. Used well, it improves both efficiency and decision quality.

But I think there’s an important distinction to make here: AI matters not because it is impressive technology, but because it can compound the effectiveness of people, systems, and decisions across the business. That is where the growth impact comes from.

Organisations that treat AI as part of how work gets done, rather than as a novelty layered on top of existing processes, are seeing measurable results. According to Accenture, companies with AI-led processes achieve 2.5 times higher revenue growth, 2.4 times greater productivity, and are 3.3 times more successful at scaling generative AI initiatives than their peers.

Those numbers are significant, but they also reinforce a point I often make to clients: the value of AI is rarely in the tool itself. The value sits in how effectively the business redesigns work around it.

For leaders, then, the conversation is no longer whether AI has potential. The real question is how to leverage AI for business growth in a way that supports strategy, strengthens execution, and creates durable advantage rather than short-term experimentation.

 

Where AI Creates The Greatest Business Value

Artificial intelligence creates the most value when it is tied to a specific business function or commercial problem. That sounds obvious, but many businesses still approach AI the other way around: they start with the tool and then look for somewhere to use it.

Strategically, I think that is backwards.

The better approach is to start by asking where the business is losing time, margin, consistency, or decision quality. In most organisations, the biggest AI opportunities are not hidden. They sit inside customer service bottlenecks, reporting delays, repetitive admin, hiring inefficiencies, weak forecasting, or underused customer data.

Here are seven practical areas where businesses are already leveraging AI effectively.

1. Improve Customer Service & Customer Relationships

Delivering exceptional customer experiences remains one of the fastest ways to build loyalty and drive growth. AI helps businesses respond to enquiries faster, personalise interactions, and provide support around the clock through chatbots, virtual assistants, and intelligent CRM systems.

Australian organisations such as Telstra have integrated AI-powered virtual assistants to improve customer support and reduce response times. Platforms like Salesforce Einstein, Zendesk AI, and Intercom Fin are also helping businesses automate common customer interactions without sacrificing quality.

If I were prioritising AI in a growth-focused business, customer service would often be near the top of the list because improvements here tend to have a double effect: they reduce operational drag while also protecting revenue through better customer retention.

2. Make Better Decisions with Data & Analytics

Better decisions begin with better visibility.

AI can process large volumes of business data, identify patterns, forecast demand, and surface opportunities that would otherwise take significant time to analyse manually. That allows leaders to respond more quickly to changing market conditions, customer behaviour, and operational risks.

But there is a more strategic reason this matters: in many organisations, growth does not stall because leaders lack data. It stalls because useful insight arrives too slowly, sits in too many systems, or never gets translated into a decision.

That is where AI can be especially powerful. It shortens the distance between information and action.

Retailers such as Woolworths Group use AI to improve demand forecasting and inventory planning, helping ensure products are available where and when customers need them. Businesses of all sizes can pursue similar gains using tools such as Microsoft Power BI Copilot, Google Vertex AI, or Microsoft Copilot to analyse data and surface meaningful insights.

My view is that analytics is one of the most underappreciated AI opportunities in business. Content generation gets more attention because it is visible and easy to trial. But from a strategic standpoint, faster, better decisions around pricing, inventory, customer behaviour, and resource allocation often create much larger long-term returns.

3. Optimise Business Processes

Every business has repetitive tasks that quietly drain time, attention, and margin. AI can automate administrative work, streamline approvals, reduce manual errors, and improve workflows across finance, operations, customer service, and administration.

This is one of the least glamorous uses of AI, but often one of the most commercially valuable.

Companies including BHP have adopted AI to optimise operations and improve predictive maintenance across complex assets. Meanwhile, platforms such as Microsoft Power Automate and UiPath enable organisations to automate routine workflows across a wide range of business functions.

If a leadership team asked me where to start with AI, I would usually look for process bottlenecks before I looked for “innovative” use cases. Fixing invisible operational friction is rarely exciting, but it often creates the cleanest ROI because it frees up capacity across the entire business.

4. Strengthen Market Research & Business Intelligence

Understanding your customers, competitors, and market shifts is essential for making informed decisions. AI can accelerate market research by monitoring industry trends, analysing customer sentiment, evaluating competitor activity, and identifying emerging opportunities.

The obvious benefit is speed. The more important benefit is decision confidence.

Australian technology company Canva uses AI to support product development and better understand user behaviour across its global customer base. Businesses can also use tools such as Perplexity, Brandwatch, and Meltwater to strengthen market research and stay informed about changes in their industry.

That said, I would be cautious about treating AI-generated market intelligence as complete or authoritative. It is useful for accelerating pattern recognition and early analysis, but leaders still need to pressure-test the findings. AI can help you see the landscape faster; it should not be the only lens through which you interpret it.

5. Increase Workplace Productivity

Many professionals spend hours each week writing emails, preparing meeting notes, summarising documents, drafting first versions of content, and searching for information. AI can automate or accelerate much of this work, allowing employees to spend more time solving problems, collaborating, and delivering value to customers.

This is where a lot of businesses start with AI, and that makes sense. The barrier to entry is relatively low, the benefits are easy to observe, and teams can adopt tools quickly.

Australian software company Atlassian continues to embed AI across its products to help teams manage projects, organise knowledge, and improve collaboration. Businesses can also leverage tools like ChatGPT, Microsoft 365 Copilot, and Google Gemini to support everyday productivity across the workplace.

In my experience, this category delivers the best results when leaders set clear expectations about how AI should be used. Without that, employees often use it inconsistently: one person uses it to improve decision briefs, another uses it to draft emails, and someone else avoids it altogether. The productivity gain becomes fragmented rather than systemic.

6. Support Talent Acquisition & Employee Development

Finding, hiring, and retaining talented people remains one of the biggest challenges for growing organisations. AI can help identify qualified candidates, screen applications, match skills to job requirements, and recommend personalised learning opportunities that support employee development. From a strategic perspective, this matters because hiring mistakes are expensive in ways that are not always visible on a spreadsheet.

Australian employment marketplace SEEK continues to invest in AI to improve candidate matching and recruitment experiences. HR teams can further streamline hiring and workforce planning using platforms such as LinkedIn Recruiter AI, Workday AI, and Eightfold AI.

What I find most valuable in this category is not simply faster screening. It is AI’s ability to introduce a more structured layer of analysis into decisions that are often influenced by bias, momentum, or incomplete information. Used carefully, AI can help leaders notice what they may have overlooked not by replacing judgement, but by challenging it.

7. Enhance Security & Fraud Protection

As businesses become increasingly digital, protecting data and preventing fraud have become critical priorities. AI can monitor systems for unusual activity, detect potential cyber threats, and identify fraudulent transactions before they escalate into larger issues.

Commonwealth Bank has invested heavily in AI to enhance scam detection and fraud prevention across its banking services. Organisations can strengthen their own cyber resilience using AI-powered security platforms such as Microsoft Security Copilot, Darktrace, and CrowdStrike Charlotte AI.

If a business is increasing its digital footprint, expanding e-commerce, or handling large volumes of customer data, I would argue that AI-enabled security deserves to be part of the growth conversation rather than treated as a separate IT concern.

 

Real-World Case Studies of Leveraging AI for Business Growth

AI becomes much more valuable when it is applied to a real commercial problem rather than discussed in abstract terms. Two examples from my own work illustrate this well.

From Local Retailer to Digital Growth

One of my retail clients had built a successful brick-and-mortar business and was achieving steady year-on-year growth of around 15%. It was a healthy business, but it had reached a point where relying on traditional channels alone would make further growth progressively harder.

The opportunity was not simply to “use AI.” It was to create a more scalable operating model.

Together, we developed a digital growth strategy that placed AI at the centre of several key functions. AI was used to analyse customer purchasing behaviour, identify high-performing product categories, generate SEO-optimised product descriptions, streamline email marketing campaigns, improve customer support with automated responses, and forecast inventory demand more accurately.

What made the difference was not any one AI use case on its own. It was the combined effect of removing friction across multiple parts of the business at once. Instead of the team spending hours writing descriptions, manually segmenting customers, reacting slowly to support requests, or making inventory calls from instinct, they were able to execute faster and with better information.

Within 18 months, the business recorded more than 250% year-on-year growth. AI was not solely responsible for that outcome, and I think it would be misleading to suggest otherwise. But it did accelerate execution, improve decision-making, and allow the team to scale in ways that would have been difficult through manual processes alone.

Using AI to Eliminate Hiring Blind Spots

Another client was recruiting for a senior leadership role. After several interview rounds, the shortlist came down to two exceptionally qualified candidates. On paper, both were strong, and the preferred option seemed obvious based on experience and interview performance.

Rather than relying solely on instinct, we asked AI to review interview notes, behavioural assessments, psychometric results, role requirements, organisational values, and observations from the hiring process. The purpose was not to hand over the decision to AI. It was to test our assumptions and see whether any patterns or blind spots had been missed.

Interestingly, AI highlighted that the second candidate demonstrated stronger long-term alignment with the organisation’s leadership style, communication preferences, and future strategic direction. It also surfaced several risks associated with the initially preferred candidate that had not been fully considered during the process.

The client chose the second candidate. More than a year later, the appointment has proven highly successful.

For me, the lesson was not that AI “picked the better candidate.” The more important lesson was that AI created a more disciplined decision-making process. It forced the hiring conversation beyond charisma, interview performance, and first impressions, and back toward fit, context, and long-term alignment.

That is one of the most useful roles AI can play in leadership: not making the call for you, but making it harder for you to make a shallow one.

 

Where AI Falls Short (And Why Leadership Still Matters)

For all the value AI can create, it has real limitations. If leaders ignore those limitations, they risk overestimating what AI can do and underinvesting in the human judgement required to use it well.

AI Can’t Solve Vague Business Problems

AI performs best when it is given a clear objective. If the business problem is poorly defined, the output is usually generic, inconsistent, or strategically weak.

I see this often. A business wants AI to “improve marketing,” “fix sales,” or “grow the business.” Those are not AI use cases. They are broad business ambitions. Until the underlying problem is defined (low lead quality, poor follow-up, slow reporting, weak retention, inconsistent conversion) AI has nothing concrete to optimise.

This is why I believe the quality of the business question matters more than the sophistication of the AI tool. Clarity comes first.

AI Lacks Human Creativity

AI can generate ideas, draft content, suggest alternatives, and remix existing patterns. What it cannot do is bring lived experience, conviction, taste, emotional intelligence, or original commercial instinct in the way a strong leader, strategist, or creative professional can.

That matters more than some businesses realise.

If every company in your market has access to the same AI tools, generic output becomes easier to produce and easier to copy. In that environment, the differentiator is not access to AI. It is the quality of human judgement guiding it: your positioning, your customer understanding, your strategic choices, and your ability to spot opportunities that are not obvious from the data alone.

AI can absolutely support the creative process. But it should not become a substitute for point of view.

AI Doesn’t Replace Specialised Expertise

Whether you are navigating legal obligations, financial risk, engineering decisions, or industry-specific compliance, specialised expertise still matters. AI can summarise information and highlight relevant considerations, but it does not carry accountability, context, or professional judgement in the way a qualified expert does.

This is where some of the “AI will replace X profession” commentary becomes unhelpful. In most serious business settings, the better question is not whether AI can produce an answer. It is whether that answer is sufficiently reliable, contextual, and defensible to act on.

Very often, it isn’t, at least not without expert oversight.

Critical Thinking Remains to be Your Greatest Competitive Advantage

Perhaps the biggest misconception about AI is that it reliably produces the right answer. It doesn’t. It produces plausible answers based on patterns in data. Sometimes those answers are useful. Sometimes they are incomplete, overconfident, or wrong.

That is why critical thinking remains essential.

The leaders who will get the most value from AI are not the ones who accept its output at face value. They are the ones who use it to challenge assumptions, explore alternatives, test reasoning, and accelerate analysis—while still applying judgement to what comes back.