From a data-driven to an AI-driven enterprise: 4 topics to consider

Businesses are increasingly embracing AI, but what truly differentiates AI-driven and data-driven organizations? According to Anderson MacGyver, becoming AI-powered is more than just advanced analytics on steroids – successful AI adoption requires specific attention for four topics.

While data-driven companies focus on using data for insights, system exchange, and even direct commercial gain, AI-driven companies take this a step further. AI systems – which can learn, adapt, and generate new content (like video, text, and code) – have additional value potential and bring additional challenges.

The unique power of AI lies in generating new, commercially valuable content and providing dynamic, self-acting insights. Think of a navigation system: A basic route planner on its own is not AI. An AI would be able to predict future traffic, learn from historical data, and possibly even autonomously guide a vehicle, constantly reacting to changing conditions.

This evolution from data-driven to AI-driven is not a simple switch, but rather a journey. Building on its experience, Anderson MacGyver – a European digital strategy consulting firm – defines four key topics that businesses need to focus on in their journey to becoming an AI-driven organization.

Firstly, becoming AI-driven demands specific expertise in areas like neural networks, cognitive architectures and human-AI interaction – all areas which are even more scarce than other data capabilities.

Secondly, the widespread enthusiasm for readily available general purpose AI models can be a double-edged sword. While many of these services do offer exciting possibilities and this buzz is helping to accelerate traction, businesses must avoid getting side-tracked by the buzz around generic applications and instead focus on where AI can deliver the most impact.

The integration of new AI systems has a significant impact on people and processes. Unlike traditional data insights that typically supplement existing workflows, AI implementations require organizations to rethink business processes and human roles within these processes.

This rethinking of business processes and its impact on people, requires proactive people change interventions to help employees adapt to new ways of working and to address potential resistance from those concerned about job changes or displacement. 

Adopting AI additionally means addressing new sources of risk such as model bias, transparency and legislative compliance. The EU AI Act, for example, mandates oversight and transparency in AI usage, classifying systems by risk level and based on this imposes additional requirements.

Considering this, organizations need to establish mechanisms to identify, categorize, and govern their AI systems. Risk assessment in many cases triggers new mitigation measures and using the often-applied human oversight as a key measure might be marginalizing the whole value case.

In summary, Anderson MacGyver emphasizes that becoming an AI-driven enterprise as a next step in becoming data-driven requires additional attention for business value focus, talent sourcing, people change and risk control.