How to Get Started with AI for Your Business

Published: 4 July 2026 By: Kersai Research Team
Category: AI Strategy / Agentic AI

If you have been asking yourself how to get started with AI for your business, you are far from alone. According to McKinsey’s 2024 Global AI Survey, 72 per cent of organisations have adopted AI in at least one business function, up from 55 per cent just two years earlier. Yet the same research reveals that only 23 per cent of those organisations describe their AI adoption as ‘mature.’ The gap between experimentation and genuine, value-generating AI implementation remains wide and it is costing businesses billions in unrealised potential.

The 2026 window represents a critical inflection point. Large language models have matured beyond novelty, open-source alternatives have democratised access, and the competitive landscape has shifted decisively. Businesses that fail to embed AI into their operations within the next 18 to 24 months risk falling behind competitors who are already leveraging these tools to reduce costs, accelerate decision-making, and create new revenue streams. This guide provides a comprehensive, actionable framework for getting started with AI from initial assessment through to full-scale optimisation.

Why 2026 Is the Critical Window for AI Adoption

Several converging factors make this period uniquely important for AI adoption. First, the cost of AI implementation has dropped significantly. According to Gartner, the average cost of deploying a generative AI solution has decreased by approximately 40 per cent since 2023, thanks to more efficient models, better tooling, and increased competition among providers. Second, the regulatory landscape is crystallising. The EU AI Act, Australia’s Voluntary AI Safety Standard, and similar frameworks are moving from draft to enforcement, meaning organisations that establish robust AI governance now will avoid costly retrofitting later.

Third, customer expectations have fundamentally shifted. A 2024 Salesforce survey found that 67 per cent of consumers expect businesses to use AI to personalise their experiences. Organisations that fail to meet these expectations risk losing market share to more agile competitors. Fourth, the talent market is stabilising. While AI talent remains in high demand, the explosion of AI-powered development tools means that smaller teams and even individual contributors can now achieve what previously required departments of specialists.

Perhaps most importantly, early adopters are already seeing measurable returns. A 2024 Deloitte study of 2,800 executives found that companies with mature AI deployments reported an average 25 per cent improvement in revenue growth and a 20 per cent reduction in operational costs compared to their peers. The message is clear: AI is no longer a future consideration, it is a present imperative.

The 5-Step Framework for Getting Started with AI

At Kersai, we have developed and refined a practical methodology for helping businesses transition from AI curiosity to AI capability. Our approach follows a structured five-step framework that has been validated across enterprises in Australia, New Zealand, the United States, Canada, Sri Lanka, and beyond.

Step 1: Assessment Know Where You Stand

Before investing in any AI solution, you need a clear-eyed understanding of your current position. This means auditing your data infrastructure, evaluating your team’s digital literacy, mapping your existing technology stack, and identifying the operational pain points that AI could address. Many businesses skip this step and jump straight to solution shopping, which invariably leads to misaligned investments and disappointing results.

A thorough assessment should examine three dimensions: data readiness (do you have clean, accessible, and sufficiently large datasets?), organisational readiness (does your leadership team understand and support AI adoption?), and technical readiness (does your infrastructure support AI workloads?). Without all three, even the most promising AI initiative will struggle to deliver value. Kersai’s Diagnosis phase which we internally refer to as ‘the MRI’ is specifically designed to provide this comprehensive assessment, examining every dimension of your organisation’s AI readiness.

Step 2: Strategy Define Your AI Roadmap

With a clear assessment in hand, the next step is to develop a prioritised AI strategy. This is not a vague vision document, it is a concrete roadmap that identifies specific use cases, assigns ownership, sets measurable success criteria, and establishes realistic timelines. The best AI strategies are tightly aligned with business objectives rather than driven by technology hype.

Your strategy should answer several key questions: Which business problems will AI solve first, and why? What does success look like in quantifiable terms? What resources internal and external will be required? What risks need to be managed, particularly around data privacy and model accuracy? A well-constructed strategy serves as a decision-making framework that keeps your AI initiatives focused and accountable.

Step 3: Quick Wins Build Momentum and Proof Points

One of the most common mistakes businesses make is attempting a large-scale AI transformation from the outset. Instead, we recommend identifying and executing two or three ‘quick win’ projects that can demonstrate value within two to six weeks. These might include automating a repetitive data entry process, deploying an AI-powered email categorisation tool, or implementing a simple chatbot for frequently asked customer queries.

Quick wins serve multiple purposes. They generate tangible ROI that justifies further investment, they build internal confidence and enthusiasm for AI, and they expose organisational gaps in data quality, process design, or change management that need to be addressed before scaling. Quick wins create the internal case for further investment and provide valuable learning experiences that inform larger projects.

Step 4: Full Implementation Build and Deploy at Scale

With proof points established, you can move to full implementation of more complex AI solutions. This is where many organisations benefit from partnering with an experienced AI consultancy. Implementation involves selecting and fine-tuning models, building data pipelines, integrating AI capabilities into existing workflows, training staff, and establishing monitoring and feedback loops.

Kersai’s Architecture phase ‘The Build’ focuses on constructing robust, scalable AI solutions that are designed for your specific business context. This includes selecting the right combination of proprietary and open-source models, ensuring seamless integration with your existing technology stack, and building the data infrastructure that will power your AI capabilities over the long term.

Step 5: Optimisation Continuously Improve and Scale

AI implementation is not a one-time event, it is an ongoing process of refinement and expansion. The Optimisation phase involves monitoring model performance, retraining on new data, expanding successful use cases to additional departments or functions, and continuously evaluating new AI capabilities that could provide competitive advantage. Kersai refers to this as ‘The Scale,’ and it is where the compounding returns of AI investment truly become apparent.

Organisations that commit to continuous optimisation typically see their AI ROI double within the first 12 to 18 months of deployment, as initial successes are replicated across the business and new opportunities are identified through data-driven insights.

Common Mistakes Businesses Make When Starting with AI

Understanding what not to do is just as important as knowing what to do. Here are the most common pitfalls we encounter when working with new clients, along with practical guidance on how to avoid them.

Starting with technology instead of problems. Many businesses become enamoured with a specific AI tool or model and then try to find a problem for it to solve. This backwards approach almost always leads to wasted investment. Instead, start with a clearly defined business problem and then identify the AI solution if one exists that best addresses it.
Underestimating data quality requirements. AI models are only as good as the data they are trained on. Before investing in AI, invest in data cleaning, standardisation, and governance. Poor data quality is the single most common cause of AI project failure.
Neglecting change management. AI adoption requires behavioural change across your organisation. Without proper training, communication, and executive sponsorship, even technically excellent AI solutions will fail to gain traction.
Setting unrealistic expectations. AI is powerful, but it is not magic. Expecting a generative AI tool to perfectly replicate complex human judgement from day one is a recipe for disappointment. Set realistic, incremental goals and build from there.
Ignoring governance and ethics. As AI becomes more embedded in business operations, the risks associated with bias, privacy breaches, and opaque decision-making increase. Establishing responsible AI governance from the outset is not just ethical, it is commercially prudent.

How to Identify the Highest-ROI AI Opportunities

Not all AI opportunities are created equal. The highest-ROI opportunities typically share several characteristics: they address a problem that is currently labour-intensive, they involve repetitive or pattern-based tasks, they have access to high-quality data, and they directly impact revenue or cost. To identify these opportunities in your business, consider the following structured approach.

First, map your key business processes and assign each a score based on three factors: the volume of work involved, the degree to which the work is repetitive or rule-based, and the potential business impact of improvement. Processes that score highly across all three dimensions are your prime AI candidates. This simple scoring framework can be completed in a single afternoon and will immediately clarify where AI investment will have the greatest impact.

Second, look at where your competitors are investing. If your industry peers are deploying AI for customer service, supply chain optimisation, or financial forecasting, these areas likely represent proven value opportunities. Third, consult with frontline employees they often have the clearest understanding of where manual, time-consuming tasks could be automated. Their day-to-day frustrations are your AI opportunity map.

Fourth, examine your existing data assets. The most cost-effective AI implementations leverage data you already collect but are not fully utilising. Customer interaction logs, transaction records, operational metrics, and support tickets all represent potential fuel for AI solutions that can generate immediate value.

The Role of Leadership in AI Adoption

AI adoption is fundamentally a leadership challenge, not merely a technology project. Research consistently shows that the single strongest predictor of AI success is active, visible executive sponsorship. When CEOs and senior leaders champion AI initiatives, communicate their strategic importance, and allocate resources accordingly, adoption rates improve dramatically. Conversely, AI projects delegated to IT departments without executive engagement frequently stall or fail to deliver meaningful business value.

Effective AI leadership involves several key behaviours. Leaders must articulate a clear vision for how AI will advance the organisation’s strategic objectives. They must create psychological safety for experimentation acknowledging that not every AI initiative will succeed and that learning from failure is part of the process. They must invest in building organisational AI literacy, ensuring that managers and frontline employees understand AI capabilities and limitations. And they must hold teams accountable for AI outcomes rather than just activities, measuring success in terms of business impact rather than technical metrics.

What to Look for When Choosing an AI Consulting Partner

The right AI consulting partner can accelerate your journey from experimentation to value. The wrong one can waste months and tens of thousands of dollars. When evaluating potential partners, consider their depth of domain expertise, their methodology, their track record, and their pricing model. Look for consultancies that demonstrate genuine industry understanding, follow structured and repeatable processes, can provide verifiable case studies, and offer transparent, value-aligned pricing.

At Kersai, our philosophy is straightforward: we decouple your revenue from your headcount. Our three-stage methodology Diagnosis, Architecture, and Optimisation has been refined through engagements with enterprises and mid-sized companies across multiple industries and geographies. With offices in Bundall, Brisbane, and Hillsboro, Oregon, we bring both local market understanding and global perspective to every engagement.

Budget Considerations and Expected ROI Timelines

AI investment does not need to be prohibitive. A focused AI strategy workshop typically costs between $5,000 and $15,000, while targeted implementations can range from $20,000 to $100,000 or more depending on scope and complexity. The key is to start with a clear understanding of the value you expect to generate and to structure your investments in phases that allow you to validate returns before committing to larger expenditures.

Most well-executed AI quick wins deliver measurable ROI within 30 to 90 days. More complex implementations typically show positive returns within 6 to 12 months. The organisations that see the fastest returns are those that combine strong data foundations, clear business alignment, and experienced implementation support.

The question is no longer whether to adopt AI, but how quickly and effectively you can do so. The businesses that act decisively in 2026 will define the competitive landscape for years to come. Those that wait will find the gap increasingly difficult to close.