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People. Process. Systems.

Ryan Feely
26th September 2024

Driving Digital Transformation Through ERP Image Header
Artificial Intelligence (AI) is undoubtedly one of the most transformative technologies of the 21st century. It promises unprecedented efficiencies, automation, and the ability to solve complex problems with speed and precision. From chatbots handling customer service inquiries to sophisticated algorithms optimising supply chains, AI’s potential is vast.

However, despite the excitement surrounding AI, many companies are simply not ready to implement it effectively. This gap between AI’s potential and a company’s ability to harness it stems from several fundamental shortcomings within organisations—particularly when it comes to people, processes, and systems. As a business change consultancy specialising in ERP (Enterprise Resource Planning) and transformation, we are witnessing first-hand the hurdles many companies face. So, why is your company not ready for AI, and what needs to be done to fix that?

 

The Current Capabilities of AI

Before exploring the reasons companies are unprepared, it is important to understand the capabilities of today’s AI. AI can now learn from vast data sets, recognise patterns, predict outcomes, and even make decisions based on predefined criteria. In ERP systems, AI can help streamline financial management, enhance predictive analytics, improve demand forecasting, and automate routine tasks like data entry and reporting. Some of the most common applications of AI include:

 

Natural Language Processing (NLP): allowing machines to understand and generate human language, which powers everything from virtual assistants to automated customer support.

Machine Learning (ML): enabling systems to learn from data and improve their performance over time without explicit programming.

Robotic Process Automation (RPA): allowing routine and repetitive tasks to be automated.

Predictive Analytics: helping businesses forecast future trends and make more informed decisions.

 

These capabilities have tremendous potential, but the reality is that simply possessing these tools is not enough. AI’s effectiveness depends heavily on the environment in which it is implemented. Many companies rush to deploy AI, attracted by the technology’s allure, without realising that their internal infrastructure—both in terms of technology and organisational alignment—is not ready to support it.

 

Why Many Companies Are Not Ready to Implement AI

There are several reasons why companies find themselves ill-prepared to adopt AI, with the majority revolving around three critical areas: people, processes, and systems.

 

People: Skills Gaps and Resistance to Change

AI implementation demands a significant shift in the skills required across an organisation. Yet, many companies lack the necessary in-house expertise. Data scientists, AI specialists, and technically proficient staff are in high demand, but they are often in short supply. This leads to a reliance on external consultants, which can result in fragmented knowledge and an inability to fully integrate AI solutions into everyday operations.

Moreover, there is often resistance to change within the workforce. Employees may fear job loss due to automation, or they may be sceptical of AI’s ability to improve workflows. Without clear communication and training, the workforce can become an obstacle to successful AI adoption.

 

Processes: Poorly Defined and Outdated Workflows

AI thrives in environments where processes are clearly defined, standardised, and digitised. Unfortunately, many companies have outdated or poorly structured workflows, which can make AI implementation more difficult. Automating a broken process doesn’t solve the underlying issues—it merely speeds up inefficiencies.

Furthermore, AI requires access to clean, well-organised data in order to function optimally. Many organisations struggle with data silos, fragmented information systems, or poor data quality. Without investing time in cleansing and integrating data across departments, any AI solution will be rendered ineffective or produce unreliable results.

 

Systems: Legacy Technology and Lack of Integration

One of the most significant barriers to AI adoption is outdated IT infrastructure. Many companies still rely on legacy systems that are either incompatible with AI technologies or incapable of handling the volume and complexity of data required for AI to operate effectively.

Moreover, AI works best when it can access data and systems seamlessly across the organisation. However, many companies suffer from a lack of integration between their ERP, CRM, and other business systems. Without interoperability, AI cannot provide the real-time insights and automation that businesses hope for.

 

What You Need to Consider First

Before jumping into AI implementation, companies need to assess their current readiness. There are several factors to consider:

 

Organisational Alignment: Is your leadership team fully aligned on the purpose and goals of implementing AI? Without a clear, strategic objective, AI efforts will flounder.

Data Readiness: Do you have access to clean, structured, and integrated data? Without a solid data foundation, AI cannot deliver accurate insights or drive intelligent automation.

Workforce Readiness: Is your team equipped with the skills necessary to manage, maintain, and leverage AI tools? A significant skills gap can slow down the adoption process and reduce AI’s overall effectiveness.

Technology Infrastructure: Are your systems modern, scalable, and integrated? Legacy systems or fragmented technology stacks can inhibit AI’s capabilities.

 

How to Build a Foundation for AI

To ensure your company is truly ready for AI, the focus should not be on the technology itself but on creating a solid foundation that allows AI to thrive. This foundation can be built through careful planning and investment in the following areas:

 

Develop a Clear AI Strategy

Start by defining why you want to implement AI and what business problems you hope to solve. Without a clear strategy, AI can become an expensive distraction. Your AI strategy should align with your overall business objectives and include measurable KPIs to track success.

 

Invest in People and Skills Development

Your workforce needs to be prepared for the changes AI will bring. This includes upskilling existing employees, recruiting new talent with AI expertise, and fostering a culture that embraces technological innovation. It is crucial to engage employees early in the process to minimise resistance and ensure they understand how AI can enhance their roles.

 

Standardise and Optimise Processes

Before AI can be applied, your processes need to be optimised. Start by mapping out your current workflows and identifying areas where manual interventions slow down operations. Standardising processes and ensuring they are well-documented will make it easier to automate and apply AI tools effectively.

 

Upgrade Technology Infrastructure

Your technology stack should be capable of supporting AI applications. This may involve modernising legacy systems, implementing cloud-based solutions for scalability, and ensuring seamless integration between business-critical systems. The more integrated and flexible your IT environment is, the better equipped you will be to leverage AI.

 

Ensure Data is Clean and Accessible

Data is the lifeblood of AI. You need to invest in data governance and ensure your data is accurate, up-to-date, and accessible. This may involve cleaning existing data, breaking down silos between departments, and implementing data integration solutions. Only with high-quality data can AI algorithms deliver reliable insights.

 

Conclusion

AI’s potential is vast, but the reality is that most companies are not ready to fully capitalise on its capabilities. While the allure of AI is undeniable, the real work begins with aligning people, processes, and systems. By building a solid foundation—rooted in strategy, skills, process optimisation, modern technology, and clean data—your company can pave the way for successful AI implementation. Jumping straight into AI without this groundwork will lead to frustration, wasted resources, and limited results. The key is to approach AI as part of a broader digital transformation strategy, ensuring that your business is truly prepared to unlock its full potential.

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