Understanding the AI Skills for Business Framework — dimensions and implementation (part 2)

In our previous post, we explored the four key personas that organizations need to develop for successful AI adoption: AI Citizens, Workers, Professionals, and Leaders. Now, let's examine the five dimensions of AI competency that form the backbone of the framework, with a particular focus on what these mean for small and medium-sized enterprises (SMEs).

This dimension represents perhaps the most critical starting point for any business, regardless of size. It encompasses data protection, security, and responsible data management. For SMEs, this can seem overwhelming – especially given the complexity of data protection regulations and the cost of compliance.

The reality is that even small businesses can build strong foundations here. Start with basic data hygiene practices and gradually develop more sophisticated approaches. For instance, a local retail business might begin with proper customer data management and basic security protocols before moving toward more advanced data governance frameworks.

This dimension covers data collection, engineering, and system architecture – aspects that often frighten smaller organisations due to perceived complexity and cost. However, the emergence of cloud services and "AI-as-a-Service" platforms has democratised access to sophisticated technical infrastructure.

Small businesses don't need to build everything from scratch. A restaurant chain, for example, might start with off-the-shelf analytics tools for inventory management before gradually developing more customised solutions as their needs evolve.

Here's where many organisations, regardless of size, stumble. This dimension focuses on identifying business problems that AI can solve and effectively communicating about AI initiatives. It's not about technical complexity – it's about business clarity.

We've seen small manufacturing firms excel here by clearly defining specific automation needs and maintaining open dialogue with their workforce about AI implementation. The key is starting with well-defined, contained problems rather than attempting comprehensive digital transformation all at once.

This dimension might seem the most daunting, encompassing data analysis, modelling, and AI application. However, the framework recognises different levels of capability maturity. Small businesses can begin with basic analytics and gradually build more sophisticated capabilities as needed.

A local marketing agency might start with simple AI-powered content analytics before progressing to more complex predictive customer behaviour models. The key is matching capability development to actual business needs rather than pursuing technical sophistication for its own sake.

Perhaps the most overlooked yet crucial dimension, this covers performance assessment, ethical considerations, and continuous improvement. It's particularly relevant for smaller organisations where the impact of AI initiatives can be more immediately felt and measured.

While the framework is comprehensive, implementing it in smaller organisations presents unique challenges:

1. Resource Constraints: Limited budgets and personnel make it difficult to develop capabilities across all dimensions simultaneously.
2. Expertise Gaps: Smaller organisations often lack specialised AI talent to guide implementation.
3. Competing Priorities: AI capability development must be balanced against immediate operational needs.

The key to successful implementation lies in taking an incremental, prioritised approach:

First, assess your current capabilities across these dimensions. Many organisations have more existing capability than they realise – they just haven't formalised it.

Second, identify the dimensions most critical to your immediate business needs. A retail business might prioritise data privacy and basic analysis capabilities, while a professional services firm might focus on problem definition and communication.

Third, develop a staged implementation plan that builds capabilities progressively, aligned with business objectives and resources.

While the framework might seem ambitious, especially for smaller organisations, it provides a valuable roadmap for AI capability development. The key is not to attempt everything at once but to build capabilities systematically over time.

Successful AI adoption is about more than just technical implementation – it's about building sustainable capabilities aligned with business needs.

Organisations should navigate this journey by:

• Assessing current capabilities against the framework
• Identifying priority areas for development
• Creating customised implementation roadmaps
• Providing ongoing support and guidance

The AI Skills for Business Framework provides a comprehensive guide for building AI capabilities, but you don't have to navigate it alone. Whether you're just starting your AI journey or looking to enhance existing capabilities, expert guidance can help you avoid common pitfalls and accelerate your progress.