There’s a lot of chatter out there regarding just how artificial intelligence will impact jobs. Some say we’re headed for an AI apocalypse, the end of the world as we know it. Others see a new shiny robotic city upon a hill, looking through AI-tinted glasses at an emerging utopia. The wonderful thing about predictions is they’re all wrong, although some can be more useful than others.
So, rather than try to convince you of my 2019 AI predictions, I’d rather take an Alan Kay approach to how I see the future. Alan once noted, “The best way to predict the future is to invent it.” How true this is. No simpler words have ever been spoken. No dramatic implication has ever been inferred. At Cognizant, we live by these words every day. We get on with real transformation– not by predicting it, but by inventing it.
Real AI Jobs, Being Done Now
In 2019, we’re seeing AI impact our people and customers. We see some roles deteriorating in importance and some disappearing altogether. Other new roles, meanwhile have sprung to life – exciting roles, giving new AI light to old technology skills. These AI jobs are real, materialized by flesh-and-blood humans.
- AI Ethicist: This role deals with the ethical challenges of AI, ranging from sensitive projects to risk assessment. An important question to address is not “should” we use AI but “where” should we. The AI ethicist works directly with the business unit and closely with legal.
- AI Futurist: Using AI to change the course of a company’s future presents its own challenges. An AI futurist charts all the possible realities that are possible through AI-based organizational transformations. This visionary realization role works closely with the AI ethicist.
- AI Biases Specialist: If the AI ethicist role is about helping define the AI approach, then the AI biases specialist is all about ensuring that realized AI-based operations are performing in an unbiased way. They’re responsible not only for ensuring no biases exist in the data used to create AI, but also that humans training the system do so in an unbiased way, and that AI systems creating AI systems are also unbiased. The AI biases specialist works closely with IT data management, HR and the AI ethicist.
- AI Architect: This position is responsible for determining where AI can help a business, assessing which AI capabilities are essential to the business, and measuring performance at enterprise scale. The AI architect works to ensure TOGAF and Zackman enterprise architectures become more inclusive of nuanced AI capabilities that scale with the power of IT, not people. The AI architect works closely with product innovation and development teams.
- AI Product Manager: Managing AI development at enterprise scale takes more than just training managers on AI. The AI product manager needs a new developmental frame of reference – a reference frame based on causality-based predictions and evolutionary-oriented prescriptions. The AI product manager works with the business unit and the AI architect to ensure outcomes are defined, managed and exceeded.
- Causality Specialist: Understanding the causal relationship between data (the debris of human activity) and outcomes is one of the most important insights that early-stage AI projects needs to generate. There is way too much data in our world for any AI project to effectively utilize, let alone manage. The causality specialist uses tools like mutual information theory to measure data’s ability to answer questions and find important relationships. The causality specialist works closely with business units and IT data management teams.
- AI Evolution Engineer: The only way to bend the business curve – to affect true digital transformation – is by implementing prescriptive solutions in the field. We call this sustainable business evolution. The AI evolution engineer uses neuroevolutionary techniques (yes, these are real) to organically grow prescriptive strategies based on predictive models (surrogates). This is a new, powerful enterprise role. The AI evolutionary engineer works closely with the AI data scientist and causality specialist.
- AI Data Scientist: This is not a new role but a recognition of the power this role plays in the new emerging AI-led organization. Yes, the data scientist is still superman, all powerful. But in today’s AI-based lifecycle, the data scientist moves from data munger to surrogate or digital twin creator, used in the predictive and prescriptive evolutionary value-generation lifecycle. Gone are the days of these resources spending 80% of their time transforming data (we now leave that to data engineers). The modern AI data scientist focuses on building surrogate models of critical business functions that are used by evolutionary engineers to create prescriptive models that effect business change.
There you have it – the eight new AI job roles we’re using here at Cognizant in 2019 to create real value-based business transformation. These are actual, realized roles that breathe life into an assortment of new skills. The people in these roles not only support the notion that AI will create jobs but will also help move businesses forward into the AI future.
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