Why companies need to put people at the heart of AI strategy
Technological progress is changing the world we live in and, with it, the very nature of how we work. Entangled in automation’s numerous pioneering benefits are societal challenges and concerns around job security and competence. With automation becoming more adept at routine tasks, the workforce is being augmented. However, human work is far from becoming obsolete. In fact, organizations who make the mistake of thinking so are likely to lose their competitive edge. Our into this area has found that the most successful organizations are those who can strike the delicate balance between technology and people. These organizations tend to report higher than average levels of productivity.
Organizations must manage not only their digital transformation with the adoption of automation, but also the upskilling and re-skilling of their workforce. Indeed, the very concern about automation should not be about jobs being lost, but rather the new jobs that are being created – and having the right people, with the right skills, to fulfil them.
The relationship between automation and upskilling
The benefits of upskilling are numerous for an organization: from increased productivity to cost savings and better efficiency. Upskilling enables employees to better understand and leverage automation tools. More significantly, though, upskilling allows employees to tackle more high-value, strategic tasks, thus improving enterprise output. Essentially, while automation reduces manual tasks, upskilling encourages and permits employees to nurture their soft skills – creativity, critical thinking and negotiation, for example – and use these to perform value-add activities.
It can also be said that upskilling improves employee loyalty and trust in an organization. This kind of training program reassures employees that their jobs – and futures – are secure. This also has a knock-on effect, in that employees will embrace automation and reject fear mongering around it, as they no longer feel that AI will “steal” their jobs. All of this is excellent for enterprise productivity.
As automation adoption continues to change job responsibilities and increases demands for new skills to be developed, companies face an urgent need to upskill their workforce. However, found that just 16% of organizations have undertaken the automation and upskilling transition of their current workforce.
Success factors for striking the balance between automation and upskilling
To help organizations understand how to create a viable upskilling program, below are four key success factors, utilized by successful organizations.
Be visionary: Many organizations get caught up in establishing a quantifiable business case for an upskilling program, leading to delays on the very important, first step. Comparatively, true leaders in this space are forward thinking and visionary about automation’s impact on the workforce. Rather than trying to prove a business case, they are proactive in predicting the impact of automation as they recognize that understanding future scenarios is critical to developing an appropriate upskilling program.
Understand the skills you need and the workforce you have: Once the impact of automation has been visualized, organizations must then devise their upskilling strategy. It is recommended they do this at three levels, based on skills clusters. These are
- Core skills that underpin the organization’s core competencies and long-term strategy, including core skills that exist today as well as core skills that will be required in the future to execute on the long-term plans
- Skills that are required for non-core tasks, but where the skill may become more essential and core in the future
- Those people with skills for handling non-core tasks that are likely to remain non-core
78% of businesses that are successful with upskilling, succeed at equipping employees with the competencies they need to stay relevant in an automated world. Creating these pathways for your workforce means reviewing your approach to learning and career paths, as well as having a good understanding of your employees’ interests, capabilities and preferences.
Aim for a win-win scenario: Upskilling programs only work if employees are on board and willing to learn. Creating a program that is seen as relevant and exciting is essential to make an impact in the upskilling of the workforce. Leaders must ensure that training is targeted, manageable and, ultimately, helpful. Moreover, training should be embedded into the organization’s culture – continuous learning will be key in the fast-moving age of automation. Organizations that have succeeded in scaling up their skilling programs also show greater unity of purpose in their leadership teams. Their programs are run by cross-functional teams comprising business and HR leaders, drawn from top and middle management.
Enable leaders to communicate effectively and manage change: Communication is critical for any successful change management initiative. Leading organizations realize this and enable their leadership to effectively communicate with the workforce. These organizations place a lot of focus on timely communication, communicating frequently and via a range of channels, such as mailers, internal collaboration platforms, and webinars.
Ultimately, it is clear that upskilling is an integral part of automation in workplaces, delivering excellent operational benefits while also boosting employee morale and productivity. For success, organizations must understand that upskilling programs are not just bolt-on initiatives. They require deep cultural change, support at the executive level, and need to be underpinned by strategic, big-picture thinking.
By Claudia Crummenerl, Managing Director, People and Organization at Capgemini Invent
The advantages and disadvantages of AI in cloud computing
Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.
We take a look at some of the benefits and drawbacks of AI in cloud computing.
The benefits of AI in cloud computing
A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.
AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.
Improved data management
AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments.
Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks.
With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly.
The drawbacks of AI in cloud computing
Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.
IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity.
While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.