Why #PeopleFirstAI is mission-critical for Enterprise AI Strategy and how can it accelerate AI Adoption?
Before we get into the #PeopleFirstAI, let’s first understand the prevalent paradigm today. I call it Data-First AI. Most enterprise initiatives today are run by the data first paradigm. It started with the famous quote Data is the new oil and how every organisation must become a data-driven organisation. Let me break it to you as gently as I possibly can. You are already a data-driven organisation! Unless you make business decisions on the flip of a coin, you are data driven. If you use any type of ERP or even excel based transaction tracking system, you are data-driven. So what has really changed?
The change has just come not from the data per se. Of course, you now have access to a higher volume and variety of data than ever before. The real change has come from the availability of analytical insights and critical business data not just to managers but to individuals deeper in the organisational hierarchy. Before the data revolution, access to data was proportional to your position in the hierarchy. With the new data-driven paradigm, that access can be made available to anyone on a need-to-know basis.
Organisation and business are all based on human interactions and the data is the outcome of these human interactions. If we try to change the data without changing the human interactions, it is like trying to correct the decisions without changing the decision-making competence. The unfortunate reality is this — people are not that good at interpreting data and rationally acting on it. We are much less rational than what we imagine ourselves to be. If you don’t believe, think about your last impulse purchase. What came first — the decision or the logic? If you are like most people, you made the decision and then try to justify it with logic. Logic and rationality is needed by our conscious mind. But the ‘real-time’ instinctive decisions are taken from the subconscious mind that doesn’t necessarily require logic. It goes by what feels right. And this is where the data-first AI approach starts faltering.
Now coming to #PeopleFirstAI, let’s do a thought experiment. Imagine you are responsible for making an important decision — inventory level in a regional warehouse. The cost of a bad decision (stock-out or overstocking) is $1Mn and the benefit of a good decision is $5Mn. The system gives you the recommendation for the level but somehow it doesn’t feel right. What would you do? If you decide to follow your gut, you might make a mistake and will have to own the wrong decision. Is your organisation processes mature enough to allow that learning curve? And if you blindly follow the system, then who is eventually accountable if the system makes mistakes (and it will make a lot of them)? One of the main goal on #PeopleFirstAI is to resolve this problem of accountability.
The biggest issue of AI adoption in Enterprises is not the prediction, it is the judgement. The data-first approach focuses on the use-case, modelling, data management and deployment activities needed to develop and make the model available. The judgement aspect of the prediction is largely missing in this view. The prediction engine itself cannot create value unless there is a robust decision-modelling system in place to support the prediction engines. The biggest implication of enterprise-wide AI adoption is decentralisation of decision authority. For the last five decades, there have been several debates on the merits and demerits of decentralisation. With AI, there is no more debate. It has to be decentralised. Otherwise, what is the point of using the predictions if the front-line person has to first ask for permission! The value is not created by making predictions, the value is created by acting on them. But if front-line employees are not properly augmented and capable of taking those decisions, AI will remain just a fancy prediction tool.
The next important aspect from a leadership point of view is the so-called ‘AI-First strategy’ for ‘AI-first organisations’. It’s a popular battle-cry from the CXO office to go AI-first but very few companies understand the implications of AI-first. In a 2018 article, Ajay Agrawal and Avi Goldfarb (author of prediction machines) and Joshua Gans (Google) explain, “Adopting an AI-first strategy is a commitment to prioritise prediction quality and to support the machine learning process, even at the cost of short-term factors such as consumer satisfaction and operational performance.”
Are you really AI-first? Would you be willing to incur short-term costs (and maybe even losses) for a few quarters so that you can train your AI to make more accurate predictions? Are you willing to experiment with AI for your most important customer risking customer satisfaction in an existing business? Then what exactly is the meaning of AI-First? Is it really a strategy or just wild aspirations? And if it is a strategy, could there be another way?
What is #PeopleFirstAI and how does it help?
According to a recent study by McKinsey, only 6% companies have embedded AI into formal decision making and execution processes of their frontline employees and only 16% participants believe that their employees actually trust AI-generated insights. #PeopleFirstAI strategy is a human augmentation and business process oriented strategy that focuses on frontline AI adoption, employee readiness and process maturity. Unless AI is ingrained deep into core business logic and critical business processes, AI-first will remain a pipe dream for most companies.
Another point that differentiates #PeopleFirstAI with other approaches is the focus on transformation and orchestration of core business processes. According to the same McKinsey study, only 21% of companies have embedded AI in several parts of the business. Adoption of AI into peripheral processes will not create any significant transformation. It is the orchestration of various value stream processes across the organisation that creates high impact. Additionally, inability to transform business core leaves companies vulnerable to disruption because the disrupters don’t attack the peripheral processes — they attack the core business processes. The study concluded that one critical factor of using AI effectively is an organisation’s digitalization journey transforming the core parts of its business. The biggest challenge of any organisation is not to automate a single process chain, say invoice processing, but to orchestrate it with other business processes e.g. payment processing, vendor rating, procurement etc. This is another area where Data-first strategy misses the big picture by focusing on isolated (and sometimes peripheral) use-cases but not on the overall process maturity and orchestration.
A people-first strategy starts with accountability and decision-modelling. It first aims to strengthen and augment human capability before integrating automation. A people-first strategy understands that even the best AI system will be most accurate for only roughly 20% of the most-likely scenarios. The less likely, but equally significant scenarios still need to be dealt by humans. And lastly, a people-first AI strategy accounts for ‘in-process’ or ‘on-the-job’ training of AI. It considers AI not as a business expert rather as an intern who is eager to learn and perform but needs to be sufficiently trained first. Isn’t that a more realistic representation of AI’s current capabilities? Isn’t that not the real AI-First strategy?
Finally, the most important core competence needed for any organisation today is resilience. I define organisational resilience as the ability to handle variability — both planned and unplanned without sacrificing scale, productivity and profitability. Planned variability is introduced in the form of a broader product portfolio and personalised customer service. Unplanned variability is introduced by demand uncertainty and supply-side disruptions e.g. machine failure, logistics failure and supplier failures. Both these types of variability take a toll on productivity and profitability.
In 2016 study showed that as much as 33% of all organisations reported losses of more than $1 million over that year due to supply chain disruptions. A large part of these costs could have been avoided if the organisations were resilient. It is an incredibly important and difficult goal to achieve and yet there isn’t much discussion on it in the business forums. AI has been found extremely valuable to detect disruptions at global scale. But detecting disruptions is just one aspect of resilience. Ability to effectively manage disruptions also requires process and business-level maturity. To add to the complexity, if AI is not properly integrated into the business processes, it can even add significant uncertainty and variability due to modelling tolerances and errors. That’s where #PeopleFirstAI complements the data-first strategy and addresses the problem of resilience in two ways
1. By augmenting human capabilities to interpret and act on data
2. By strengthening the process capability and resilience to handle variability
Considering the challenges that most organisations are facing or will face, building organisational resilience must be the top priority for the leadership. And to develop this resilience, workforce augmentation through #PeopleFirstAI strategy is absolutely mandatory. A company’s AI maturity will not be judged by how accurate predictions it can make, rather by how quickly and intelligently can its front-line employees act in the face of disruption. Resilience is not an accident. It is a critical competence that needs to be deliberately developed over time. In the next course of articles, I will explain #PeopleFirstAI in greater detail. If you’ve enjoyed this article and understood the necessity, do share the claps. If you want to know more, feel free to contact me.
Disclaimer: The views shared in this article are personal and do not represent those of any affiliate.
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