Artificial Intelligence (AI) is potentially the most powerful technology businesses have ever had access to. AI is not only transforming how businesses operate, but it is also transforming the services and products they offer to their customers. However, many see AI as risky or a leap of faith - something you go into without knowing the outcome. At Wyser, we know that high-performing organisations are those that adopt AI, who make time to prepare for change, have flexibility in their approach and can make balanced risk-based judgements.
Here are Wyser's key points to consider when preparing your business for AI:
Access to the right data – Good data fuels good AI. Trained on the right data, machine-learning algorithms can do amazing things, such as see (Machine Vision), read (Natural Language Processing), speak (Natural Language Generation), and much more. However, while businesses have access to more data than ever before, that data is often not ready to feed AI applications. Businesses need to foster a culture where data assets are valued, defined, mapped, classified and well-governed. To assess which data is appropriate and optimum for an AI project, companies need good comprehensive metadata libraries. Metadata helps describe and define data and should include a combination of technical and business detail. Effective metadata catalogues describe data characteristics such as type, ownership, sensitivity, relationships, lineage, business definitions, quality and more.
Access to the right talent and skills – AI skills are in huge demand and companies are competing for top talent. The solution is to develop a skills and data literacy strategy that acts as a pathway to acquiring and building relevant people and teams. The key to getting this step right is upskilling existing employees, boosting data and tech literacy across the company, and finding the right partners. To promote success, be prepared to start a collaborative journey with a business delivery partner, like Wyser, so you are working side by side to co-create and co-develop AI. It is critical for both sets of staff to work as a team as AI is a voyage of discovery – you may find insights you were not expecting, or realise you need to pivot your approach or method. As a team, you should approach this in an agile manner and work co-operatively, allowing for easy course corrections depending on your findings. To lay the foundations of this approach, your team will need to be adept at business insights, have good analytical skills, especially if they are not data scientists (consider online teaching services like Datacamp or Udemy (if needed), and have a vision for the outcome.
The right technology – The applications of AI are diverse, however, getting the technology right can be a challenge, particularly for established businesses that invested heavily in platforms a decade ago and are now struggling to bring their legacy system into the AI age. AI often requires an update to the technology used to collect, store and process data. Digitally native companies often have a huge advantage here as they can build nimble and scalable businesses in the cloud with modern apps, and APIs to collect and stream real-time data and, like Wyser, have created modern AI platforms. Alternatively, you can utilise Robotic Process Automation to bridge the gap, which Wyser can deliver through its X Flow platform.
Ethical and governance frameworks – To combat unconscious bias, organisations should have clear ethical and governance frameworks in place to ensure that AI systems follow legal requirements. If you need guidance on what these frameworks entail, consult best practice guidelines, such as the Alan Turing Institute’s Understanding Artificial Intelligence Ethics and Safety. Biases in AI are often a result of the provided training data set, which may be skewed or favour certain attributes over others. It is critical that any data selected to train and evolve your AI is continually checked and validated. It is equally important to validate AI results to ensure algorithms do not unintentionally favour one set of attributes over another. Thankfully, it is possible to use technology to help automatically identify bias and understand how an algorithm has utilised its training data. All of this is essential to creating fair AI.
Take a strategic approach to AI deployment – It is vital that your AI initiatives align with your business goals and your technology strategy. Too many companies experiment with AI instead of first questioning and identifying how AI can help them tackle business challenges and uncover opportunities. In an ideal world, your business strategy should guide your data strategy, which in turn, should align with your AI strategy. First understand the purpose of AI for your business, and then develop the approach based on the goal.
Considering all of the above before committing to an AI project will help you avoid the “leap of faith”; and instead, take a measured approach, knowing that your preparation has been optimised even if the outcome is uncertain.
If you would like to know more about the services Wyser offer including Robotic Process Automation and Wyser’s AI Grand Challenges, please contact us.