Some common examples (not exhaustive) include:

• Classification (e.g., categorizing products of acceptable quality) – Logistic regression, Discriminant analysis, Naïve Bayes, Support Vector Machines, CNNs

• Estimation (e.g., forecasting sales demand or predictive analytics) – Linear Regression, Feed Forward Neural Networks

• Clustering (e.g., segmenting customers or employee) – K means, Gaussian mixture, Affinity propagation

• Optimization (e.g., capacity or route optimization) – Genetic Algorithms, Differential Evolution, Markov decision process

• Recommendation (e.g., next product to buy) – Collaborative filtering, Content filtering, Hybrid

The most prevalent problem types are classification, estimation, and clustering, suggesting that developing the capabilities in associated techniques could be a good starting point. Also, in most cases, for a specific problem both the traditional techniques or advanced deep neural network-based techniques could be applicable and its important to assess the trade-off between them before selecting.

3. What types of data-sets are needed?

Data sets also play an important role while choosing between traditional and advanced techniques. They can vary from being structured or time series based to text, audio, image or video based. Typically, neural AI techniques excel at analysing image, video, and audio data types because of their complex, multi-dimensional nature compared to traditional techniques. However, sometimes even more value can be extracted from mining insights from traditional structured and time series data types rather than going for audio visual data sets. Building a deep understanding of use cases and how they are associated with particular problem types, analytical techniques, and data types can help guide companies regarding where to invest in the technical capabilities and the associated data that can provide the greatest impact.

4. What are the ‘training’ requirements?

Large labeled training data sets are required in most applications to make effective use of advanced techniques such as neural networks, and in some cases, millions of them to perform at human levels. As training data set increases, performance of traditional techniques tends to flatten and advanced AI techniques tends to increase. However, if a threshold of data volume is not reached, AI may not add value to traditional analytics techniques making it also an important consideration. While promising new ML techniques such as reinforcement learning, generative adversarial networks, one-shot learning are trying to overcome the requirements of supervised learning, which requires humans to label and categorize the underlying data. Companies still need to assess their ability to collect, integrate, govern and process data at scale before deciding on the right AI solution.

5. What is the optimum system (or hardware) configuration?

Depending on the end-use case and its requirements, data can be generated from a variety of sources including sensors, IoT devices & machinery or social networks. This could result in inmassive amount of fast, structured, semi-structured or unstructured data, time series or real-time which can be stored in original granularity or aggregated or pre-analysed. The choices are immense. It’s important to maintain focus on the ultimate requirements of the end application and prioritise accordingly. Similarly, the AI application can reside in the cloud or at the edge to minimise latency. Cloud typically offers a flexible and scalable environment at relatively low-cost and without huge initial investments. Many providers also offer APIs for computer vision, speech recognition, and natural language processing (NLP) or other cognitive domains on their cloud platforms which are pre-trained and preconfigured for a certain task and serve as gateways to AI applications. All these configuration options need to be considered before finalising the optimum system infrastructure to enable the preferred AI solution.

Ultimately, for CIOs to create value from their AI initiatives, it is important to develop an understanding about which business use cases have the most value creation potential, as well as which AI or any other analytical techniques is most suited to capture that value and can be deployed at scale across the company. The decision of choosing the right AI solution is often driven less by the sophistication of the technique but more by the available skills, capabilities, IT infrastructure and data at the end for organization-wide implementation.