AI is a high-stakes business priority. 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 requires the professionalisation of the industry. Stakeholders must come together to distinguish clear roles and responsibilities for AI practitioners, demand the right level of education and training, define processes for developing, deploying and managing AI, and democratise AI literacy across the enterprise.
Real value can only be realised when trained AI practitioners are working hand in hand with the business to accomplish their organisation’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 professionalise 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 a recent Accenture study of 1,500 C-suite leaders.
The Covid-19 pandemic has further sharpened the contrast between those who have professionalised 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 businesses to professionalise in parallel.
Training is key and it is great that Ireland launched its first master’s in AI at the University of Limerick in January 2018, a collaboration with the university, the Irish Centre for High-End Computing and private companies including Accenture, Citibank, Dell, Bell Labs and SoapBox Labs. But because AI technologies are advancing so rapidly, organisations need to also support.
Steps to take for professionalisation of AI
By following these steps, organisations can better set themselves up to make the most of this quickly evolving technology.
1. Distinguish clear roles
A hallmark of a professionalised industry or trade is that practitioners understand the individual roles that contribute to a final product. Multidisciplinary teams of diverse perspectives, skills and approaches must work together to innovate and deliver AI products or services.
Companies must demonstrate the importance of distinguishing clear roles and clarify individual remits. These teams, often headed by the chief AI, data or analytics officer, include data modellers, machine learning engineers and data quality specialists, to name a few.
The mix and infusion with business knowledge, in the form of product owners and business leads, is key to maintaining focus on the business outcomes. Tapping into partner knowledge 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, and align the focus on key business challenges or opportunities, from the start.
2. Demand education and training
As organisations invest more in their AI and data capabilities, employees understand the growing influence of these technologies on their careers. But despite their best efforts, many of these employees will not have the right training and qualifications to work effectively with AI.
To establish an effective professionalised workforce, it’s up to companies to assess which skills they need, their workforce skills gaps and the qualifications of their talent and match them to the appropriate roles. To enable a consistent approach to training, companies should create clear career paths for their AI practitioners.
Each career level should have established prerequisites, such as coursework and training, to help build necessary skills and proficiencies. These prerequisites should be shaped by a combination of leaders across technology, data and human resources.
This transparency and consistency will provide clear educational expectations for anyone working on AI projects – from data architects and test developers to machine learning engineers. The added benefits of establishing career paths are better talent retention, employee development and a market-leading, professionalised practice.
3. Define process
While some argue that formalised processes and governance could stifle innovation, our research has shown the opposite. Companies that govern innovation extensively over time said they expected to double revenue growth in five years.
In professionalised industries, there’s a standard approach to testing and benchmarking during the creation (or optimisation) of products and services. Similarly, whether a company is making smart devices or building a data science model to improve the online retail experience, establishing systems and processes to support the development of the AI product or solution allows people to innovate in a predictable and efficient way.
Once companies have distinguished clear roles for their AI teams, they should establish defined processes that formalise the development, deployment and management of AI solutions. These should inform how people work together, how they choose technologies to support production of the AI solution and how they interact with those technologies.
For example, once a data science group creates a new algorithm, an organisation with a professionalised approach to AI would establish a system to test the algorithm to ensure it does what it’s supposed to do in a safe, predictable and consistent way.
4. Democratise AI literacy across the organisation
While there is certainly growing interest from leaders to invest in AI technologies, true professionalisation will result in (and rely on) AI literacy across an entire organisation.
Organisations owe it to their employees and to their bottom lines to provide some form of AI education. We found that 62pc of workers believe that AI will have a positive impact on their jobs, and 67pc of employees say it’s important to develop skills to work with intelligent machines.
It’s clear employees recognise the impact AI could have on their jobs and are keen to learn more, and organisations have an opportunity – and I would argue, a responsibility – to enable it.
To start, companies should define the minimum level of AI knowledge they require from their employees. Helping the entire workforce understand what AI is, how it impacts their jobs and how it benefits the company are part of building confidence in AI and driving adoption and usage.
Denis Hannigan is the applied intelligence lead for Accenture in Ireland.
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