AI and its ability to replace human jobs has generated much discussion, with some fears it might render some jobs obsolete while also creating ones more suitable to human skills. While AI may indeed change some jobs for good, its main function can also be creating entirely new ones more suited to people’s individual strengths.
By properly applying artificial intelligence (AI), businesses can benefit by cutting costs and improving decision-making. Let’s examine all of its possibilities together.
Collaborative AI Tools
AI tools are revolutionizing how business professionals collaborate and produce results. Their use frees up human workers to devote more of their time and focus on higher-value tasks while increasing customer service efficiency.
These new technologies, when coupled with a hybrid workforce model, hold great promise to revolutionize how businesses are run, empowering employees to complete their job functions faster and with more accuracy than before. Collaborative AI technologies have quickly become part of today’s workplace.
Businesses employ AI to increase collaboration within their teams as well as with clients and partners external to them. AI tools enable team members to share documents, schedule meetings and work together in real-time regardless of location or time zone.
These solutions make it simple for employees to create and distribute records of meetings quickly, so anyone unable to attend can still participate in the discussion. This can help increase employee productivity and alignment when working across global team members.
AI-powered Workstream Collaboration (WSC) solutions such as Avaya Spaces(tm) enable users to maintain context for conversations as they transition between tools. This improves meetings by providing teams with tools for sharing content, engaging in dialogue and taking swift action against any issues which arise.
Solutions like these also give users instant insights into when their colleagues might be available for meetings, enabling them to plan better meetings in advance and reduce scheduling conflicts. This helps teams align better, meet goals more easily while decreasing travel costs while staying compliant with policies.
AI solutions can also ensure data is managed and protected correctly, which protects both companies and their customers’ privacy. Furthermore, these tools can identify potential security threats such as phishing scams and malware to stop employees sharing sensitive company data with third-parties.
Machines as Subordinates
As machine intelligence becomes ever-more capable, we’re seeing new ways in which robots and humans can work together. AI-enabled robots now work alongside human workers in manufacturing plants as they contribute to helping with production tasks and other industries.
Collaboration among humans and machines necessitates machines acquiring new skills while engaging in safe, productive, and effective interactions with their human counterparts. They require humans to provide training and instruction to intelligent agents as well as work effectively alongside smart machines when performing tasks that require dexterity or human judgment – such as robotic-assisted surgery procedures.
Successful collaboratives provide benefits to both the organization and employees working there, such as freeing up workers’ time to focus on higher-skilled tasks and creating a more connected work experience for customers and staff alike.
However, AI and human collaboration is also fraught with risk, particularly when machines assume roles such as subordinate or supervisor. Also problematic are situations in which these digital doppelgangers act as subordinates or supervisors of real workers.
Workers could become disenfranchised and feel like machines without room for humanity, becoming disillusioned with their supervisor or machine’s solutions and recommendations, which could impede team performance.
HR professionals must consider these potential hurdles when redesigning the workplace and planning how AI will be deployed, taking special care in mapping its capabilities, goals and intentions as well as outlining worker roles and identities.
One way of accomplishing this is through carefully designing AI tools for specific types of work. For instance, companies might employ an “AI-enabled digital assistant” that serves salespeople by providing them with lists of possible products or suggesting those best suited for them to sell.
Cobots or co-robots are another AI-enabled tool that can assist humans by performing complex industrial tasks more safely, efficiently, and of higher quality output. They work alongside humans while adding an extra level of safety and efficiency in performance.
Changes to workforce composition could bring major transformation to work environments and the work people perform. Organizations could experience job losses when machines can do repetitive low-skill jobs better or create new employment opportunities for humans capable of handling more intricate, sophisticated tasks that require specific expertise or critical reasoning skills.
Machines as Supervisors
Machines as supervisors present humans with an opportunity to rethink how they collaborate and learn from one another, potentially revamping processes which have become rigid or ineffective. This type of collaboration may prove especially fruitful in improving efficiency.
Machines can help improve business performance by streamlining tasks and increasing accuracy, freeing humans to focus on more creative or meaningful work that requires human strengths, such as reading X-rays or analyzing financial statements.
However, AI as a supervisor also presents its own set of challenges. Unlike subordinate forms of AI, supervisory AI may limit worker autonomy or increase monitoring – something which could potentially diminish employee performance over time.
This can lead to feelings of loneliness, isolation and identity crises – something which is especially true within industries regulated by government such as healthcare or insurance.
Organizations need to develop innovative AI-based supervisory solutions in order to address this challenge, particularly AI explainers that will assist nonexpert users in understanding how a system came to its conclusions and outputs.
Companies must retrain employees in AI-enhanced processes in order to work effectively with artificial intelligence-enhanced operations. Employees need fusion skills – the ability to delegate tasks while working closely with intelligent agents.
Development of these skills requires investment in AI-driven training and on-the-job experience, possibly working alongside your organization’s IT and HR departments.
Leaders must take special care when assigning employees roles that involve AI as supervisors. Leaders should ensure the type of AI interaction fits in with each worker’s unique work style and preferences to avoid hindering productivity or engagement of workers under AI supervision.
To achieve this goal, companies may need to educate and reskill employees in delegating more effectively with new technologies, thereby making them more successful when using them. They may also need to reimagine how teams collaborate by assigning greater weight to human aspects of teams.
Implementing successful human-machine collaboration requires an effective leader with expertise in both people and technology domains. This may take the form of a chief analytics or AI officer, an information, data, or technology officer or even human resources (HR) officer charged with helping employees gain digital fluency.
Machines as Teammates
Machines as teammates provide teams with reliable information for making informed decisions and proposing solutions, with their recommendations often becoming more convincing than human proposals if their confidence intervals are accurately and efficiently calculated (256_14).
Machines could also replace or repurpose certain jobs, including tasks requiring low skill labor (267_15), logic and rational thinking (272_13), or specialized skills (319_31). This may create new employment opportunities while decreasing reliance on lower skilled workers.
However, using machines in human collaboration may have some unintended repercussions, including increased safety and wellbeing issues for humans, reduced job availability, reliance on machine teammates that lead to deskilling, as well as changes to decision heuristics or cognitive biases which impact individual team members.
Furthermore, machine teammates could create ambivalent effects or dualisms that collaboration researchers anticipate in terms of how people interact and perceive these interactions with teammates and perceive these interactions themselves. Such dualisms, outlined in the research agenda can explain and predict differences in individual experiences with machines as teammates.
MaT’s research agenda includes 12 themes to define design choices of machine teammates in terms of artifact, collaboration and institutional environments. This will allow researchers to typify and prototype a machine teammate that sustainably augments human team collaboration while yielding benefits for both individuals and organizations alike.
This area’s themes include:
1. What should a Machine Teammate Appear Like? In order for machine teammates to function effectively within teams, they should appear as human-like partners if possible; this will foster trust and build loyalty from peers as opposed to serving as potential opportunistic agents who could be taken advantage of by human teammates.
2. What characteristics must a machine possess to be liked by its audience?
To avoid annoying or alarming others, its appearance and function should make it clearly distinguishable from a person – not simply its intelligence or capability of accomplishing certain tasks.
