The Four I Model lays down a reliable guideline for planning AI-transformations
The success of Artificial Intelligence projects (AI) in organisations depends on putting humans at the heart of the initiative. The secret sauce of making automation, digitisation and data work for an organisation is about transforming the business model itself where machines and humans are integrated to complement each other and create a new organisation structure that uses technology to bring out the best in people. This is what a five-year research by Harvard Business Review (HBR) has found. It has created a 4-layer model that begins with defining the organisational purpose, integrating humans with technology, focusing on execution and measuring steps towards goals as success, so as to help organisations in their journey towards maximising the benefits from AI initiatives.
The research underscores that uniquely human skills such as creativity, care, intuition, adaptability and innovation are factors that are critical to success. While machines will perform repetitive and automated tasks, “…these human skills cannot be “bot sourced,” a term used to characterise when a business process traditionally carried out by humans is delegated to an automated process like a robot or an algorithm.
Towards this end, HBR has developed a four-layer framework that reveals how leaders can create organisations that are human-centric, as well as with super-human intelligence. Named the “Four I Model” – the four layers comprise intentionality, integration, implementation, and indication. These layers are not really “steps” to be followed so as to achieve sequential progression. Rather,the four layers should be leveraged together, as one unified whole –or else the AI that is being created falls short of delivering a sustainable competitive advantage.
Layer 1: Intentionality of purpose – It is the first of the four layers, and the “purpose” here goes far beyond the regular profit-motive. Any enterprise of standing is well aware that it thrives not only on profits – but to realize some specific and overarching goals. In other words, it defines the purpose of its existence. That is the intentionality of purpose for that organisation. Such cultural shifts towards a more human-centric purpose will affect every strategic decision – from marketing and product design to capital redeployment and digital Infrastructure.Hence, creating human-centred, technology-powered approach will be the key driving force for financial performance in the age of AI.
In this regard, the HBR study offers the instance of Siemens, which evolved from a shareholder-profit-maximising power generation and transmission company into a leading provider of electrification, automation, and digitalization solutions with energy-efficient, resource-saving technologies driven by AI and the Internet of Things (IoT) in service to society.
Layer 2: Integration of resources – The second layer comprises integration of human and AI resources across an entity. Organisations have to think of newer operational structures in which teams are flexible and resources are integrated both horizontally and vertically. And this change must span right from product creation to strategic decisions.The new structure will have fluid roles and cross-functional teams that would allow talented resources a free hand in generating customer-centric products. Such teams would be created whenever specific solution requirements are to be met, and would be disbanded once the work is done. Rather than having fixed roles all through one’s career, this approach is more modular – where resources are allocated as and when requirements arise.
The study is careful to point out that this same level of integration may be forged in human-AI collaborative teams. While this would enable human team members to go beyond cognitive limitations by leveraging machine power, there would also remain a natural human check on an automated system for tasks that require high degrees of care and skill.
Layer 3: Implementation – At the third layer lies implementation, which involves engaging human talent, tolerating risk, and incentivising cross-functional coordination. As human behaviour is central to implementing AI, high-performing companies put special emphasis on communicating with employees and educating them, so that they realise how machines could make their jobs easier, instead of making them redundant. This trust-building step is crucial to the success of AI in organisations. Apart from clear and precise communication, the study suggests arranging visits to other companies that have undergone similar AI transformations, which could allay any doubts about AI that employees might have.
Layer 4: Indication – The fourth layer is indication or performance measurement. Any accomplishment can be objectively evaluated only if it can be measured. Organisations are always in search of appropriate metrices – gradually shifting from productivity measures used conventionally to aspirational metrics that incentivise innovation and creativity. Such metrices would encourage employees to exercise those uniquely human traits. The study points out that monitoring the wrong performance indicator might lead to opposite results; because humans are clever, and if incentives are not justifiably aligned to well-crafted performance metrics, human workers usually resort to “lazy, clever, and cynical hacks to game the system, maximizing the appearance of performance under one measure while actually failing to deliver the output that management was actually hoping for when they implemented that measure.”In fact, companies like Intel and Google have popularized the use of Objectives and Key Results to work around this problem.
Traditionally, most companies have been using KPIs (Key Performance Indicators).However, the HBR research found that successful companies are using Objectives and Key Results (OKRs) more often.The OKR system sets targets and provides a means of measuring results. They are usually set by C-level executives for the whole company to follow, keeping everyone on the same path. Some of the best companies including Google, Spotify, Airbnb, Twitter, and LinkedIn are trying out the OKR system with great results.
At times there might be an overlap in the understanding of OKRs and KPIs. While a KPI aims at measuring success rather than setting goals, OKRs precisely define how to achieve ambitious objectives where failure is imminently possible, through concrete, measurable specifications. They encourage creative, novel, and aspirational performance by showing progress toward a goal even if the goal itself is unattained.
Refining AI systems would require much involvement of the human element – and the Four I Model lays down a reliable guideline to follow.