Beyond the hype: realising AI's strategic potential

The untapped power of machine learning and mature AI

Machine learning, the backbone of many AI applications, enables systems to learn from data and improve their performance over time without explicit programming. This capability opens up a world of possibilities, from predictive analytics and customer segmentation to fraud detection and supply chain optimisation. By leveraging machine learning, organisations can uncover hidden patterns, anticipate future trends, and make data-driven decisions that drive growth and efficiency. 

Mature AI technologies, such as data labeling and computer vision, offer equally compelling opportunities. Data labeling, while often overlooked, is crucial for training and refining machine learning models. Computer vision, which allows machines to interpret and understand visual information from the world around them, has applications ranging from quality control and safety monitoring to autonomous vehicles and medical imaging. These technologies can automate manual tasks, enhance decision-making, and create entirely new products and services. Think about the following question: what new capabilities/opportunities does our organisation have if we know that computers can “understand” what it sees? 

Implementing AI: A strategic roadmap

Successful AI implementation requires a well-defined strategy and a phased approach that emphasises both technology and the human element.

1. Start with planning and understanding use cases. Building internal AI awareness through workshops, training programmes, and dedicated expert groups is crucial for identifying and prioritising the most impactful AI use cases. Identifying external top AI voices can also provide valuable insights into the latest developments and world best practices. Here you have to be careful as many of them currently are either blatant lies or irrelevant/ impossible/ unfeasible use-cases for your organisation.
2. Experimentation with intended use cases. This, however, requires great deal of patience. Most probably all of you have experienced using ChatGPT (or any other GPT for that matter) failure of getting the result that you want. You cannot expect success with first tries or even with the intended use-case. Initial AI projects should focus on limited, well-defined solutions that go beyond simple pilots. By starting small and iterating, organisations can gain valuable experience, refine their approach, and build confidence in AI's capabilities. Patience is key, as real-world AI implementation often requires iterative experimentation and refinement.
3. Stabilisation and rollout. Once an AI solution has proven its value, it's time to stabilise and scale it across the organisation. This requires a "pull" mechanism that fosters phased adoption and engages both process owners and end-users. By demonstrating the tangible benefits of AI and addressing potential concerns, organisations can ensure a smooth and successful rollout.

Effective AI: Tools, Process, People

With the AI landscape evolving rapidly, selecting the right tools can be overwhelming. Organisations should prioritise tools that offer a great user experience. AI tools should be intuitive, easy to use, and seamlessly integrated into existing workflows. This seamless integration minimises disruption, encourages adoption, and maximises the value of AI investments. The best AI tools are those that you're not even aware are using AI.

AI tools should also provide tangible, measurable benefits and generate usable, effective outputs. Otherwise, their value remains elusive, similar to generative AI's current limitations. Focus on tools that can solve specific business problems and drive concrete results.

However, while technology is a crucial enabler, it's essential to remember that AI's effectiveness hinges on the processes and people that interact with it. Organisations need to carefully consider how AI will fit into their existing workflows, how it will impact employees' roles and responsibilities, and how it will be governed and managed. By taking a holistic approach that considers both the technical and human aspects of AI implementation, organisations can maximise its potential and achieve lasting success. Technology is just a small part of actual AI success, you cannot overlook good processes and people that will deal with this technology in the first place.

Practical insights

Use these practical insights to explore where you could benefit of machine learning and AI, and how you can implement these into your organisation.

• Start by identifying areas within your organisation where machine learning could address specific challenges or opportunities. For example, could predictive analytics help you optimise inventory levels or target marketing campaigns more effectively?
• Explore how mature AI technologies could streamline operations or create new value propositions for your customers. Could computer vision improve product quality inspection or enable new forms of customer interaction?
• Embrace a "fail fast, learn faster" mindset. Encourage experimentation and view initial setbacks as opportunities for learning and improvement.
• Before investing in any AI tool, conduct a thorough evaluation to ensure it is easy to use, easy to integrate in existing workflow, delivers that it promises consistently, and most importantly, provides meaningful value to you and you organisation. If one of those is not met, it is best to skip this tool or wait until it “works out the kinks”.
• Involve limited about of end-users early in the AI development process to ensure the solution meets their needs and addresses their pain points. Provide training and support to help them adapt to new ways of working, training does not end with one demo session or one-day meet-up, it requires multi-layered work.

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