By Jonathan Whelan
Occasionally, new technological innovations offer boundless opportunities, generate huge excitement and are accompanied by unprecedented levels of expectation. AI is one of those innovations. Although AI has been around since the 1950s – it was founded as an academic discipline in 1956 – it is only in recent years that its full potential is being more widely recognised. Today the answer seems to be AI; what was the question?
The potential benefits of AI span industries, from detecting fraud in financial services, analyzing X-ray images in healthcare services, enabling autonomous transport, to predicting and preventing terrorist attacks, to name but a few. For businesses in general, AI can increase efficiency, make the workplace safer, improve customer service, create competitive advantage and lead to new business models and revenue streams.
But like any technological innovation, AI has its risks and challenges. At the heart of AI is code and data; code can (and often does) contain bugs, and data can (and often does) contain anomalies. But that is no different to the technological innovations that we have embraced to-date. Arguably, the risks and challenges of AI are greater – not least of all because of the potential breadth of its application – and they include (but are certainly not limited to): overreliance, lack of transparency, ethical concerns, security, and regulatory and statutory challenges which typically lag behind the pace of progress.
So, what does have this to do with strategy and architecture, and in particular digital transformation? Too often in organizations, new technologies are rushed in, in the belief that there is no time to lose. Before you know it, the funds and resources have been found to embark on an initiative (programme or project) to adopt it, spearheading the way to the future. It is the future! If you are assigned to the initiative, great, you are a part of the future; if you are not, all you can do is watch the future pass you by. There is funding, enthusiasm, excitement, expectation … until the initiative starts to hit problems. Often what follows is uncertainly, frustration and disillusionment, usually because the organization is not adequately equipped to embark on such an undertaking.
For any new technology which may have significant impact and a long lifespan – and AI is one of those – there are a number of factors which should be considered at the outset to aid their adoption. Unfortunately, it is common for many of those factors to be considered reactively rather than proactively, usually because the focus is on getting to market asap. Here are some of the key factors which deserve more attention than they often get:
- Strategic fit – Is the initiative aligned to the vision and goals of the organization? In the case of a significant innovation like AI, the vision and goals may need to reviewed/revised.
- Business justification – Is the business case robust? There is often a tendency to jump on the bandwagon rather than considering timing and what is right for the organization.
- Stakeholder engagement – Are the key stakeholders across the organization supportive of the initiative, and committed to play their part in achieving the intended outcomes?
- Delivery capability – Does the organization have the knowledge, roles, skills, tools, structure and processes to deliver the capability? For new technologies, this is often not the case, and consultancies are engaged to get the ball rolling.
- Service operation – Does the organization have the capability to operate the new service, including the necessary business, change management and risk management processes?
- Governance – Is the governance structure in place to ensure appropriate rigor, from investment to delivery? This includes investment, architecture, delivery and execution governance.
- Compliance – How will the organization demonstrate statutory and regulatory compliance? In the case of new technologies such as AI, the (statutory and regulatory) directives may be evolving.
Clearly there is a balance to be drawn. The above factors need to be considered, but not necessarily all in advance of starting the initiative itself. Most organizations have existing delivery and governance structures and processes in place, and those will have evolved over time, and should have incorporated lessons learned from previous initiatives. But the dash for the (delivery) line does not mean that those existing structures and processes should be ignored; they should be embraced and enhanced to accommodate the new AI-related requirements.
One approach to assessing how an organization may need to adapt to adopt AI is to look through the lens of an enterprise-wide model such as a capability model or process model. By identifying the existing (or even new) capabilities/processes impacted, and attaching a relative importance to each, investment can be targeted accordingly. In this case it is the inward-facing (AI-enabling) capabilities/processes that are focus. However, the same enterprise-wide model can be used to outward-facing (AI-contributing) focus on where AI can provide benefit to the organization, and where its AI “sweet spot” lies. Investment can then be focused on those capabilities/processes so that the organization generates the greatest bang for its (invested) bucks. In both cases, attaching a simple visual status to each capability/process can provide a broad-brush view of the impact of adopting, and the potential contribution of AI – e.g. (Green, Amber, Red to represent a Significant, Middling, Limited contribution respectively).
The bottom line when adopting AI is the same as for any new technological innovation, and that is to do so with eyes wide open.
Author profile
Jonathan is an established business transformation specialist who has over 37 years’ experience in change-related roles. His commonsense approach to addressing complex business problems and shaping practical, sustainable solutions has been fundamental to the success of many transformation programs.
In his spare time, Jonathan writes about business transformation, especially in relation to the issues and opportunities associated with information technology. His latest book, co-authored with Stephen Whitla and published by Routledge, is titled Visualising Business Transformation – Pictures, Diagrams and the Pursuit of Shared Meaning.
Visualising Business Transformation – Pictures, Diagrams and the Pursuit of Shared Meaning
Jonathan Whelan and Stephen Whitla
Pub: Routledge; 1 edition (6 Feb. 2020)
Hardback, paperback and eBook (Kindle & VitalSource)
ISBN-10: 1138308242
ISBN-13: 978-1138308244