Walkthrough for Adding New AI Technology to your Business

Artificial intelligence (AI) has rapidly evolved from theory to reality in the 2020s. The rise of large language models (LLMs) and the strong push for widespread adoption is evidence of this shift, offering businesses new ways to automate tasks, analyse data, and improve decision-making. However, as with any emerging technology, the rush to adopt AI must be tempered with strategic thinking, especially when compared to more stable, proven investments.

Early AI Development

AI is not necessarily new as a term in the technology landscape. The term was coined in the 1950s as the first AI tools were developed. These rudimentary tools exhibit similar features to modern AI tools such as complex, logic-based problem solving (Logic Theorist, 1956), conversational agents (ELIZA, 1966), and natural language processing (SHRDLU, 1968). Computing power was the limitation for further development at this time, and significant improvements were impossible until the rise of the internet and vast availability of website data in the late 1990s and early 2000s.

The limitation to wider business adoption has always been the accessibility and cost of the technology. Companies that have been willing and able to invest saw significant improvement in competitive advantage. This was more evident in the 2010s as the underlying technology was more able to support the processing power of AI, but without widespread generalisation or accessibility. For example, Google separated itself from other search engines, by its machine learning and analytical algorithms. Siri’s voice recognition was a landmark feature in early iPhone models. BakeryScan, an image recognition tool for baked goods in Japan, significantly improved product identification and sales processing speeds for non-barcoded items and now has applications in medicine.

The Rise of AI Technology in Modern Business

For modern businesses, ChatGPT’s (and other LLMs) landmark launch in November 2022 was the sign of the next big leap in AI progress. Now, due to its accessibility, businesses have access to a wider range of tools that can be applied to their processes and operations. These models, trained on vast datasets, are capable of understanding and generating human-like language, making them suitable for a wide range of business tasks from customer service to internal reporting.

Like any software system, AI is a tool. Its goal is to replace tasks, not roles, and to support the working efforts of your team to make them more efficient. Even the most sophisticated AI tools lack the contextual understanding, ethical reasoning, and strategic insight that human professionals bring to the workplace. Attempting to automate entire roles using AI often leads to operational inefficiencies, loss of domain expertise, and increased risk. The most effective use of AI in business is task-level automation. This means identifying repetitive, time-consuming activities that can be streamlined using AI tools—such as data entry, invoice processing, and initial customer query handling.

The Clamour for AI Adoption

It is clear how AI can improve efficiency for modern businesses. Its ability to rationalise data and draw conclusions at speed is a tantalising prospect for organisations looking to do more with less. Ideally, it can automate mundane tasks and let your team focus on more productive and important areas of the business.

The AI playing field is levelled compared to the 2010s, when only very large businesses had the capital to invest in AI. Organisations are under pressure to act quickly, driven by the fear of falling behind competitors who are already experimenting with or deploying AI tools. This brings significant risk to modern businesses that rush to implement AI tools under the impression that they will solve all their problems.

The Risks of Artificial Intelligence

Any software or technology framework that you implement in your organisation carries an element of risk. AI tools, especially LLMs, have known risks that you must be aware of and manage if you are to deploy them in your organisation:

• Rapid development could mean your chosen solution becomes obsolete compared to competitors,
• Bias and discrimination based on the data used for training,
• Data security and sovereignty,
• Misinformation and hallucinations,
• Environmental impact and resources required to run the processing servers,
• Lack of clarity in how data is processed or interpreted.

Implementing new AI technology without a clear understanding of its capabilities or alignment with business objectives can be disastrous at worst, and likely inefficient at best. In some cases, AI tools are introduced simply because they are available, not because they solve a specific problem.

The cost of misaligned technology can be significant, especially in larger, more complex organisations that manage the sophisticated interaction of multiple teams, processes, and workflows. Rushing into AI adoption can result in fragmented systems, duplicated efforts, unmet expectations, and wasted time. The key to managing this risk is careful consideration and evaluation of the AI tools.

Understanding the Technology Adoption Lifecycle

The lifecycle of technology follows a traditional, predictable pattern. The growth in the chart represents new-user adoption or active users of a system over time. This pattern can help organisations anticipate the challenges and opportunities that come with each software they use. 

While the lifecycle of an individual technology product may be 25 years, technology frameworks that form the foundation for a paradigm of cultural impact can span decades or longer. AI has the potential to impact human and professional culture into an endless possible future. While it has existed for over 70 years at this point, it is still firmly within the innovation stage. Growth, and rapid growth, is on the horizon due to its continual development and impossibly large training dataset. However, the technology is still largely unproven and unreliable compared to more established technology.

The risk of early adoption

A business that invests in technology during the innovation phase must endure a great deal of pain, especially compared to one that invests in the Maturity phase, when the technology is more established and proven to improve efficiency. This is because of the discrepancy between the perceived and actual benefits of the technology.

Perception and reality only converge when the product reaches maturity. It has gone through its innovation and growth and is now a stable and proven solution that delivers on its promises.

Software vendors are incentivised to promote or over-hype their products in the early phases. Whether to secure a place in the market, secure new lines of funding, or fuel the next phase of development, the product needs sales to continue. There is often a trough or chasm between the Growth and Maturity phases, as this is a common time when technology may stall and fail.

The perception of AI functionality is at an all-time high. It is easy to see the benefits of automation and intelligent software. However, when implemented at scale, current AI solutions often fail to meet expectations.

In this case, the early adopters are somewhat like guinea pigs, with their user data and investment shaping the development of the product. Managing your business as an early adopter requires a heavy investment of resources and patience, with equal measures of faith and luck that you have selected a product that will stand the test of innovation and growth. You may reap the benefits of having been an early adopter, but you will have endured significant disruption to get there. This trade-off is not always optimal for larger, more complex organisations.

Solving Business Challenges Before AI

AI is being positioned as a silver bullet, one that can solve all your business problems and immediately improve efficiency. However, digital transformation does not begin with the technology itself; it requires a clear understanding of your business challenges, the cause and effect, and the processes behind them. Any technology solution you implement should address these challenges and improve overall efficiency. 

For many businesses that are beginning to experiment with and research AI tools, implementing an ERP system might be a more prudent step, with a greater return on investment in both the long and short term.

An Enterprise Resource Planning (ERP) system is a stable, proven technology for larger, more complex organisations. These systems integrate data and processes across your organisation, automating manual processes, eliminating data duplication, and delivering accurate insights in real-time. In many cases, an ERP implementation can deliver the results that an organisation is trying to get from an ill-fitting AI tool. In the case of Acumatica, a prudent and strategic inclusion of A.I. tools within the software removes the risk of going it alone.

By investigating an ERP before AI, businesses lay a solid foundation for future innovation. Once core systems are in place and operational challenges are addressed, AI can be introduced in a targeted, strategic manner. This “walk before you run” approach reduces risk and improves ROI, without the negative impact that experimenting with early versions of AI can bring.

Clever cloud solutions for larger, more complex organisations

MYOB Acumatica is a cloud-based ERP system built for larger, more complex organisations across Australia and New Zealand. It is a flexible, modular platform that supports a range of integrated business capabilities:

• Finance – bank reconciliations, deferrals, tax, and accounting
• People – payroll, workforce management, and leave management
• Supply Chain – inventory management and demand planning
• Sales and Customers – customer relationship management (CRM) functionality
• Production – discrete manufacturing, bills of materials (BoM), and material requirements planning (MRP)
• Projects – job management, quoting, and billing
• Field Services – track and manage your mobile workforce

When implemented by a trusted, experienced software partner that understands your organisation, MYOB Acumatica is proven to improve efficiency. It is built to accommodate the needs of a range of industries and business types, including Construction, Wholesale and Distribution, Not-for-Profit, Healthcare, Government, Education, Financial Services, Multi-entity organisations, Field Services, Professional Services, Engineering firms, Project management and accounting, Insurance providers, Retail, and Manufacturing industries.

ERP AI: The Future of Intelligent Business Systems

Integrating AI into ERP systems is a strategic and structured approach for larger, more complex organisations that are ready for the next step in their technology future. MYOB Acumatica (formerly MYOB Advanced) is being developed at the forefront of AI technology. Its cloud-based architecture and modular design make it well-suited for AI ERP integration. MYOB Acumatica shares the same framework as the Acumatica Cloud ERP based in Washington, USA and is a localised version for Australian and New Zealand organisations. Companies that use MYOB Acumatica to manage their business will be at the forefront of AI-based business management technology in ANZ.

Acumatica takes an AI-First approach to the development of their cloud-based ERP platform. They recognise the importance of Artificial Intelligence in improving the user’s experience and business efficiency. But, to properly implement AI in your business, it needs to be included and integrated from the ground up. Acumatica does not treat AI as a replacement for ERP functionality. Instead, it embeds AI where it adds the most value. This ensures that MYOB Acumatica AI adoption is purposeful and aligned with business needs.

To explore how MYOB Acumatica AI can support your organisation’s goals, contact the Kilimanjaro Consulting team at sales@kilimanjaro-consulting.com or call 1300 857 464 (AU) or 0800 436 774 (NZ).