The nature of business decision-making is changing. While the greater accessibility of data might seem to be a positive development in reaching informed conclusions, many leaders are struggling.
A survey of more than 1,000 C-suite executives by Signal AI, for instance, found that the most common barrier to decision-making was ‘overwhelming amounts of data’, with 44% of respondents reporting difficulty in parsing the data.
But a solution to the demands of modern leadership could lie in augmented decision-making. Here, artificial intelligence (AI) or machine learning (ML) suggests or assists in reaching an outcome.
At digital transformation consultancy Publicis Sapient, for instance, AI and ML technologies have become an integral part of their client offering, and of its internal operations. The firm has developed models which predict employee churn, manage revenue and pinpoint marketing and sales opportunities using a mix of customer, website and third-party data. Nigel Vaz, its CEO, says the sophisticated use of data to inform business strategy will drive performance for many companies.
“The most successful organisations of today and certainly of tomorrow will be those that begin as or transform to become data-driven, with data-driven leadership at their core,” says Vaz. “As AI and machine learning technologies evolve, leading companies will feel the imperative to transform their ways of working to deliver value.”
How to prepare for the rise of augmented decision-making
Vaz adds that his leadership team uses augmented decision-making to “facilitate better informed and more data-driven conversations”. This process looks at macroeconomic trends, business metrics and client needs to pinpoint opportunities. Vaz attributes the company’s record growth of 19% over the past financial year to this approach.
Leaders need to be assured that the data is sanitised from known discriminatory practices that can skew algorithms
This technology has the potential to completely change how C-suites reach their decisions. Professor Chris Tucci teaches the ML and AI executive education programme at Imperial College London Business School. He thinks that the underlying models are likely to become more “creative” in the next few years.
“AI systems could soon be used for brainstorming ideas on developing a new market or customer segment, new products and processes, and new business models. It might even become a sort of adjunct for human decision-makers, in addition to the efficiency arguments for their use,” says Tucci.
Using data-processing tools in this way would require a significant shift in attitudes from certain leaders. Surveys of the C-suite repeatedly indicate a distrust of analytics.
One such report from Deloitte in 2019 reported that 67% of executives “are not comfortable” accessing or acting upon data from advanced analytic systems. The same percentage of CEOs told KPMG in 2018 that they had followed their intuition over data-driven insights.
Even so, there are indications that the tide might be changing. “C-suites have resisted data analytics for higher-level decision-making. They have preferred to rely on gut-level decision-making based on field experience and intuition,” says John Hill, founder and CEO of Silico, an AI-powered platform that allows businesses to simulate business decisions and processes. “But now, more executives are staking their business entirely on AI and ML to augment important business decisions and plan autonomously.”
Overcoming key barriers to AI adoption
The promised results are tantalising. For instance, Signal AI has estimated that the slow adoption of augmented decision-making resulted in US companies missing out on $4.26tn (£3.43tn) in revenue in 2020.
But adoption might not be quite that straightforward. Hill warns that businesses looking to capitalise on this opportunity won’t necessarily have the infrastructure or capability in-house, which could risk reputational damage if data is used incorrectly. “The most common barriers to adopting augmented decision-making include difficulty in interpreting results from AI, difficulty in integrating AI with existing systems, and concerns about data privacy and bias,” he says.
“Data, knowingly or unknowingly, can contain implicit bias, and the data used in higher-level decision-making needs to be vetted to assure C-suite executives that it’s sanitised from known discriminatory practices that can skew algorithms,” he explains.
Where there’s a fiduciary or ethical responsibility, there should always be an understanding of how decisions were derived
Further problems can arise if leaders use ‘black box’ technologies – those AI-powered algorithms that don’t reveal their methodology – without suitable care or understanding.
Tucci doesn’t necessarily believe the C-suite should avoid these programmes, as every use-case to this point has involved a human controlling the inputs and vetting the final decision. But Vaz argues that leaders should have some knowledge of the model’s methods, even if a human still acts as a controlling variable in the process.
“It is crucial that business leaders have an understanding of the fundamentals of AI and machine learning and how those technologies inform the decision-making process. Certainly, where there’s a fiduciary or ethical responsibility, there should always be an understanding of how decisions were derived,” says Vaz.
“The reality of today’s tools is that they require prompts to initiate recommendations,” he continues. “So, part of the process of creating solutions will be how the individual using these tools provides effective prompts to shape the outputs.”
Time for a hybrid C-suite?
One corollary to the rise of this technology is the question of how augmented decision-making stands to change the make-up of the C-suite itself. There has already been a proliferation of chief data and analytics officers (CDAOs) in recent years. Data from NewVantage shows that 77% of Fortune 500 companies now have a CDAO, compared with 12% in 2012.
For the other positions in the leadership team, Vaz believes that the technology should reduce some of the burden on the likes of the CEO. Decision-making responsibilities will instead be shared across the organisation, to capitalise on the agility offered by AI and ML processes.
“Augmented decision-making is showing new avenues for how data can travel through an organisation to inform better decision-making at every level,” says Vaz. “As a business leader, this is less about how these tools change how you as an individual make decisions. It’s more about how it can unlock potential across the organisation to create even greater impact.”
Progression through an organisation, and ultimately to the C-suite, could be predicated on an ability to understand and process data, delivering the correct prompts to assisting programmes and enabling desirable outcomes for the business. “You don’t have to be a coder or know how to make your own AI programme. But you need to appreciate how these things work from a broad point of view and how they might benefit your company,” says Tucci.
The nature of business decision-making is changing. While the greater accessibility of data might seem to be a positive development in reaching informed conclusions, many leaders are struggling.
A survey of more than 1,000 C-suite executives by Signal AI, for instance, found that the most common barrier to decision-making was ‘overwhelming amounts of data’, with 44% of respondents reporting difficulty in parsing the data.
But a solution to the demands of modern leadership could lie in augmented decision-making. Here, artificial intelligence (AI) or machine learning (ML) suggests or assists in reaching an outcome.
