Realising the potential
What do you want from AI? Where do you begin? How do you keep pace with change?
1/Work out what AI means for your business
The starting point for strategic evaluation is a scan of the technological developments and competitive pressures coming up within your sector, how quickly they will arrive, and how you will respond. You can then identify the operational pain points that automation and other AI techniques could address, what disruptive opportunities are opened up by the AI that’s available now, and what’s coming up on the horizon.
2/Prioritize your response
In determining your strategic response, key questions include how can different AI options help you to deliver your business goals and what is your appetite and readiness for change. Do you want to be an early adopter, fast follower or follower? Is your strategic objective for AI to transform your business or to disrupt your sector?
AI provides the potential to enhance quality, personalization, consistency and time saved, but it’s also important to consider the technological feasiblity of AI and the availability of the data needed to support AI. How are you planning to overcome barriers and accelerate innovation?
To prioritise your response, it’s important to map the key process flows to be automated and decision flows to be augmented. What functions contain high potential processes that could drive near-term savings, for example? As data becomes the primary asset and source of intellectual property, what investments and changes would enable you to capture more data and use it more productively? With this map in place, you can then develop the cost-benefit analysis for automation and augmentation.
AI is applicable across all elements of the value chain, which can lead to multiple silos of initiatives or confusion in finding a good starting point. Developing the insight, governance and organisational collaboration to pick your spot and drive initiatives forward are therefore critical.
To prioritize your response, it’s important to map the key process flows to be automated and decision flows to be augmented
3/Make sure you have the right talent and culture, as well as technology
While investment in AI may seem expensive now, FORFIRM subject matter specialists anticipate that the costs will decline over the next ten years as the software becomes more commoditised. Eventually, we’ll move towards a free (or ‘freemium’ model) for simple activities, and a premium model for business-differentiating services. While the enabling technology is likely to be increasingly commoditised, the supply of data and how it’s used are set to become the primary asset.
To make the most effective use of this technology, it’s important to instil a data-driven culture that blends intuition and analytical insights with a focus on practical and actionable decisions across all levels.
Demand for data scientists, robotics engineers and other tech specialists is clearly growing. These are in short supply, especially in less developed markets according to the interviews we carried out with FORFIRM’s data and analytics’ regional leaders, so it will be important to gear long-term training and development to these emerging needs. As adoption of AI gathers pace, the value of skills that can’t be replicated by machines is also increasing. These include creativity, leadership and emotional intelligence5.
It’s important to prepare for a hybrid workforce in which AI and human beings work side-by-side. The challenge for your business isn’t just ensuring you have the right systems in place, but judging what role your people will play in this new model. People will need to be responsible for determining the strategic application of AI and providing challenge and oversight to decisions.
4/Build in appropriate governance and control
Trust and transparency are critical. In relation to autonomous vehicles, for example, AI requires people to trust their lives to a machine – that’s a huge leap of faith for both passengers and public policymakers. Anything that goes wrong, be it a malfunction or a crash, is headline news. And this reputational risk applies to all forms of AI, not just autonomous vehicles. Customer engagement robots have been known to acquire biases through training or even manipulation, for example.
AI should therefore be managed with the same discipline as any other technology enabled transformation. Key questions to ask while building AI include:
• Have you considered the societal and ethical implications?
• How can you build stakeholder trust in the solution?
• How can you build AI that can explain its logic so that a lay person can understand?
• How can you build AI that is unbiased and transparent? It’s important to put in place mechanisms to source, cleanse and control key data inputs and ensure data and AI management are integrated.
Transparency is not only important in guarding against biases within the AI, but also helping to increase human understanding of what the AI can do and how to use it most effectively.
Autonomous intelligence in action
Entertainment industry consumers now have an unprecedented choice of movies, television, music and games. While this provides consumers with more opportunity to enjoy content specific to their unique tastes, they can experience ‘choice overload’ during their search. And worse, sometimes they make no choice at all! Video and music streaming companies have begun using autonomous recommendation engines that combine segment trends, ratings and content similarity to personalise suggestions and engage customers. Engaging customers not only increases retention, but also allows companies to collect more data on individuals and improve the personalisation of offerings – creating a virtuous feedback loop that provides a significant competitive advantage.