AI continues to be a high-stakes business priority, perhaps even more so in the midst of the COVID-19 pandemic, as organizations turn to AI to cut costs and, ultimately, increase efficiencies. Organizations have spent $306 billion on AI applications alone over the past three years, and this number is expected to continue to increase in the year ahead. However, even as more organizations adopt these technologies, 76 percent of C-Suite executives report they struggle with how to scale AI effectively.

Beyond the strategic implementation of the right AI tools for your business needs, treating AI like any other profession is the key to unlocking the value of these technologies. Organizations are increasingly 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), with no guardrails and standards to enable success. Through the professionalization of AI, organizations can better position and scale their AI investments, while standardizing processes to drive consistent results and incremental returns on investment. Quality use of AI technologies comes from standards and regulations – like any other industry – allowing practitioners to innovate in a responsible way that is sustainable for the future.

If AI is formalized within an organization as a trade, including proper training and standards, it can be strategically scaled to ensure it’s maximizing the best results for an organization and, in turn, providing the best return on investment. The contrast between those who strategically scale vs. those who do not has become increasingly clear in the COVID-19 pandemic as few organizations prove to be more agile than their peers in a time of constant crisis and change. These strategic scalers are also 1.5-2.5 times more likely to establish dedicated multidisciplinary teams.

Organizations can successfully professionalize AI in four steps:

• Create distinguished roles and responsibilities for AI practitioners: Formalizing teams for AI capabilities and setting clear standards and responsibilities for practitioners models other professions and builds trust between the practitioner and stakeholders, both internally and externally. It also creates internal organization and accountability on teams to know what the expectations are at each level. In fact, 72% of strategic scalers say their employees fully understand how AI applies to their roles.
• Implement education and training: AI practitioners should be given a clear career path, which will also allow for skill standards to be set in roles, avoiding a skill gap and inconsistencies in ability.
• Establish defined processes: AI products should be tested and implemented with specific benchmarks to ensure a standardized approach that can be repeated. It’s also important to analyze the way people work with these technologies and how to optimize human-machine collaboration.
• Democratize AI: AI is becoming increasingly important to different roles within an organization. Through training programs and AI literacy efforts focused on those outside of technology roles across an organization, other employees can gain confidence in AI and see how it applies to their roles.

As AI becomes increasingly prevalent in organizations’ operations, why should it be treated differently than other roles? Proper training and guidelines will maximize your investments, create long-term growth opportunities for AI in your organization and power your business to outperform peers.

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