Artificial Intelligence (AI) is a high-stakes business priority, with companies spending $306 billion on AI applications in the past three years. Companies that scale AI across a business can achieve nearly triple the return from their investments. But too many companies aren’t achieving the value they expected.
Scaling AI effectively for the long term will require the professionalization of the industry. Stakeholders—from practitioners to leaders across the private and public sector—must come together to distinguish clear roles and responsibilities for AI practitioners; demand the right level of education and training for said practitioners; define processes for developing, deploying and managing AI, and democratize AI literacy across the enterprise.
By formalizing AI as a trade with a shared set of norms and principles, companies will be poised to achieve more value from AI. They’ll be set up to create clear accountability which in turn helps avoid risks like bias, under delivering to clients, and other unintended consequences.
That’s why in professionalized fields such as medicine, construction and even food service, an inherent level of trust exists between customers and the businesses (or practitioners) that make up that industry. This trust is born out of standards that set expectations for everyone involved.
For example, you understand that architects, electricians and other construction professionals know how to build a house. They’ve had requisite training and understand their roles and responsibilities, safety standards and protocols to follow throughout the construction process. It’s unlikely that you’d trust a “citizen architect” to build your home in the same way that you wouldn’t visit a “citizen doctor” when you get sick.
Yet increasingly, companies are bolstering their core data science teams with “citizen data scientists” (or, people who create models using predictive analytics but whose roles are outside of the data science field), without providing them with necessary guardrails and standards to enable success. Even among trained and credentialed data scientists, there are varying degrees of standards. Beyond needing formalized and standardized training, organizations may find that these practitioners are working in siloes and can’t deliver on the promise of AI. Real value can only be realized when trained AI practitioners are working hand in hand with the business to accomplish their organization’s goals, and those interdisciplinary teams are guided by standards, rules and processes.
Only then will businesses be able to deliver the end product or service safely and predictably, thereby earning the trust of customers and raising standards for quality innovation and applications.
Why professionalize AI now
Three out of four executives believe that if they don’t scale AI in the next five years, they risk going out of business. According to Accenture’s study of 1,500 C-suite leaders, companies that successfully scale AI (to achieve higher returns from AI investments) are employing tactics of professionalization. These “Strategic Scalers” are 1.5-2.5 times more likely to establish dedicated multidisciplinary teams, training, and clear lines of accountability. Professionalization, then, should be seen as a precursor to successfully scaling AI.
The COVID-19 pandemic has further sharpened the contrast between those who have professionalized and scaled their AI capabilities and those who have not. As businesses race to embrace new data and AI capabilities in an attempt to recover and return to sustainable growth, it will be important for these new scalers to professionalize in parallel.
How do companies professionalize AI?
Because AI technologies and use cases are advancing too rapidly for governments and regulators to implement basic industry reforms and standards, organizations need to take professionalization into their own hands. By following these steps to standardize professionals and processes, organizations can better set themselves up to scale AI and, in so doing, make the most of this quickly evolving technology.
- Distinguish clear AI roles.
A hallmark of a professionalized industry or trade is that practitioners understand the individual roles that contribute to a final product. Take food service as an example. Farmers grow vegetables and raise livestock. Suppliers help markets source their ingredients. The staff at a restaurant each do their part to prepare, package and serve.
Similarly, multidisciplinary teams of diverse perspectives, skills and approaches, must work together to innovate and deliver AI products or services. As Accenture research shows, 92% of Strategic Scalers leverage and embed multidisciplinary teams across the organization. And, 72% say their employees fully understand how AI applies to their roles.
Strategic Scalers demonstrate the importance of distinguishing clear roles among multidisciplinary teams. They quickly stamp out redundant responsibilities and clarify individual remits. These teams, often headed by the chief AI, data or analytics officer, include data modelers, machine learning engineers and data quality specialists, to name a few. The mix and the ratio of roles is going to depend on the use cases you’re pursuing at the time and will vary from project to project. Tapping into partner knowledge and/or establishing a blueprint for how teams should operate will help this process become more turnkey over time. But one thing remains true across all projects — you need to establish ownership and expectations from the start.
By Fernando Lucini
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