How to build IT competencies for the AI era
- 12 April, 2017 06:46
Artificial intelligence (AI) companies generated an estimated $8 billion of revenue in 2016 and are on an incredible trajectory to increase that figure five times over the next three years. Enterprises are increasingly investing in artificial intelligence as a way both to drive down costs and transform customer and employee experiences.
According to Accenture’s Technology Vision 2017 survey of more than 5,400 IT and business executives, 79 percent agree that AI will help accelerate technology adoption throughout their organizations. While the disruptive growth of AI is a fact, the impacts to the workforce are more difficult for companies to articulate and address. What is clear is that leaders in every function must begin to take a nuanced view of the role that every type of worker – human and machine – will play in the workforce of the future.
These workforce impacts are particularly apparent for the information technology workforce. CIOs are already managing massive disruption – from analytics to cloud – and AI presents both significant challenges and opportunities for the IT organization to drive change and enable business capabilities. For IT to deliver on these opportunities, leaders must take decisive action to reshape the competencies and skills of people in the organization and prepare for an AI-influenced future. There are five key competency groups that the technologists of the future must develop to seize the value of machine learning and artificial intelligence.
1. Machine management
There is clearly a lot of angst among both managers and employees regarding the potential negative impact of machines replacing humans. One often overlooked element is how people in an organization will develop and care for robotic process automation (RPA) software, intelligent machines, or even physical robots. AI as an organizational capability is very much in its infancy which means, that in many cases, its use is diffused across the organization. Correspondingly, the planning efforts are immature and decentralized. IT organizations are well-positioned to develop the technical architectures and build the systems that will enable the future of intelligent machines across the enterprise. This positioning, however, is not a given. CIOs and IT leaders must align the career pathways as well as training and development away from traditional “keep the lights on” activities and shift into a paradigm that “makes a bet” on the fact that AI will come to dominate nearly every industry and organization and equips the workforce to function more efficiently.
Specific competency examples: RPA management (BluePrism, Verint), Types of AI and common uses/outcomes
2. Process consulting
IT groups have long been an underutilized resource for perspectives on how enterprise processes could be improved to optimize top or bottom line results. Leaders should build competencies in the workforce around both deep business process knowledge and how machine learning can improve processes and outcomes. For example, anti-money laundering processes can be greatly improved by the ability of AI to process multiple streams of information in ways that humans cannot. This means that AI can augment human decision-making in process flows by synthesizing data, making basic decisions and deferring more complex ones to humans. This means humans have to reengineer process and competencies to support for integrated approaches and more complex decisions. AI is already transforming customer-focused and internal processes in ways that humans have not yet been able to. There is clearly a convergence between traditional business process management and RPA as seen with Pega’s recent acquisition of RPA company OpenSpan. More broadly even, there are numerous examples beyond RPA such as voice-based customer authentication that can vastly improve customer service interactions in some industries.
Specific competency examples: Business process knowledge, industry knowledge
3. Platforms and data stewardship
The technology workforce must develop strong information management and technology platform (e.g. big data) skills. Machine learning methods only produce predictive models as good as their data. Organizational silos and data quality are certainly not a new challenge for companies. However, people run the risk of becoming a bottleneck to AI if they do not have the skills to support the models and platforms. As IT re-invents itself as an organizational cloud provider, new technologies and architectural concepts require that IT teams serve as enterprise stewards of data and ultimately break down silos to harness the power of machine learning.
Specific competency examples: Data systems management, API management and development, information strategy
4. Algorithm awareness
Not everyone must be a data scientist, but it is critical for technologists to have basic statistical competencies and the ability to articulate how AI algorithms are created, improved, and ultimately output data. There are two core benefits to companies. First, IT can articulate AI capabilities to the business and can work in partnership with the business to continually improve models. Second, a foundational understanding of the mathematical concepts that drive machine learning enables an essential degree of knowledge and creativity. This creativity can support IT organizations to create positive business outcomes as they build AI capabilities. In an example, Accenture has partnered with the Stevens Institute of Technology to develop advanced analytics skills in critical areas of its workforce.
Specific competency examples: Data set selection and subsetting, regression and classification methodologies, model accuracy estimation, model regularization and stabilization techniques
5. Leadership and judgment
Machines are becoming the coworkers of the future and handle the day-to-day administrative activities that encompass so much time today. Workers across the enterprise will need to be able to not only embrace a world where machines are making everyday operational decisions, but also where they are expected to exercise judgment on more challenging decisions. A shift like this one requires more focused problem-solving abilities and the skills to construct questions in a way that machines will be able to process and then ultimately create responses that correctly guide decisions.
Specific competency examples: Communication and EQ (spell), judgement-based solutioning, collaboration and cross-functional knowledge
What should leaders do immediately to begin to build the relevant skills and competencies?First, create an internal learning campaign to support AI readiness in the workforce focused on introduction to the technologies and benefits of AI and reducing the fear of machines. Through a series of virtual activities and in-person activities, organizations can generate learning and competency development about the opportunities of artificial intelligence. The level of the activities can vary by workforce and skill level and allow people to progressively increase their skills.
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Additionally, explore the workforce dynamics of AI by demonstrating that AI makes people work more efficiently and eliminates tasks, not jobs. There are even opportunities to deploy prototypes or actual demos in the campaign. Finally, conclude with workshops to increase creativity, open-mindedness, flexibility in the way that AI and robotics are leveraged in the workplace – particularly for managers.
The machines are here to stay and are coming to every company and government around the world. Leaders in those organizations will experiment with and adopt the automation and augmentation technologies for a multitude of reasons. The opportunity for IT executives is to transform their workforce and build the competencies required to enable AI in the future. By focusing on effectively managing machines, the data and algorithms that they use, and ultimately on leadership and judgment, technologists can be prepared to drive and optimize AI in their organizations.