Agentic AI as an employee is no more a future concept.
Organizations are viewing AI agents as tools and as members of an organization.
Different from standard AI, which acts as a rule-driven tool, an agency AI is capable of being autonomous and acting independently of human experience. An agency AI is capable of thinking, learning, experimenting, and making decisions in the moment.
This change typically alters the way organizations think about teams and performance. Organizations are onboarding AI not merely as their deployment of automation but as their digital coworkers.
These modern coworkers can share responsibilities and workloads with human employees. Collaboratively, they can provide all the value that humans do with processes, participants, and systems, and to the organization at large, to shape decision-making.
Consequently, this change begs the question of whether AI should be treated as an employee if we are to have it function as an employee.
Yes, you onboard humans, and you should onboard AI as you are onboarding your digital coworker to find parallels between their work, the development of their work. It would bring in an experience like humans by introducing redundancy as a means for ensuring their work reflects on company values, goals, data quality standards, compliance, and decision-making.
With the arrival of digital coworkers comes a rethink of purpose and meaning in onboarding. Onboarding is no longer about the welcome mat. Onboarding is a new human collaboration in how humans and AI work collaboratively.
Organizations ready for this change will build better collaboration, efficiency gains, better overall agility, and be more competitive.
The Wall Street Journal states that the Bank of New York Mellon is a pioneer of sorts, being one of the first global financial institutions to formally hire AI into its workforce.
The term "Digital Employees" covers AI agents and other AI-powered tools, designed to work alongside and augment the human workforce in everyday critical tasks (e.g., coding, validating payment instructions, etc.).
Digital Employees do not run in the same manner as traditionally back-end systems: they have user logins, they report to managers, and they communicate with other personnel through typical workplace applications like Outlook and Microsoft Teams.
BNY Mellon has employed AI agents as visible employees within the organisation, instead of traditional background tools.
Digital Employees inform their managers and interact with staff by using the established systems of the bank.
The bank seeks to have AI integrated within the daily workflows of employees, and through this seeks to build accountability and trust.
Agentic AI is any artificial intelligence system that is capable of functioning autonomously. Agentic AI is fundamentally different than traditional AI, which relies on predefined logic and produces predictable outputs.
Where traditional AI may be concerned about past performance and return on investment, agentic AI can plan, choose, and execute with minimal human engagement or oversight.
Agentic AI is adaptable and can change its goals based on feedback, and it can work cooperatively with humans, responding to changing needs as they happen. This flexibility elevates agentic AI beyond automation. Agentic AI represents a new class of workflow participants.
Agentic AI can reason, adapt, and make decisions without human engagement.
Agentic AI is more than automation because it takes the initiative in an ever-changing context.
Businesses can assign agentic AI roles, responsibilities, performance outcomes, and performance measures.
Applications for agentic AI monitoring can include CRM for customers, human resources, operations, regulated compliance, and potentially more.
BCG predicts that by 2029, the global market for AI agents is projected to grow at a 45% compound annual growth rate (CAGR) in the next 5 years.
Gartner states that AI will resolve 80% of all routine customer service requests without human intervention and reduce operating costs by 30%.
Agentic AI can act with little human intervention. It can change its goals over time and directly affect business outcomes.
Its ability to operationalize into workflows, learn, and collaborate with humans makes it fundamentally different than earlier AI tools.
That is where the conversation shifts.
Agentic AI is no longer serving as a myriad of task tools. Instead, it communicates, takes actions on its own, and is increasingly seen as contributing to business results.
This makes it more than 'just software.' Recognizing AI as an employee will help organizations assign a certain level of structure, assign it responsibility to act as an employee entity, and be clear about AI's role in the workspace.
The AI serves as a change agent and provides a degree of separation, which reduces friction and facilitates a smoother adoption or level of comfort within the human teams.
When AIs are instructed to perform tasks and have managers, their contributions can be tracked as any other employee.
This ensures that the output is measurable, rather than suggesting ambiguous proclamations of “automation.” It also builds confidence that AI will perform in a pre-defined scope.
There is a high possibility that employees would like to collaborate with AI when it is framed as a colleague rather than an anonymous, faceless tool.
By framing it this way, you are eliminating some of the resistance to working with it and subsequently allowing people to regard AI as a collaborator to solve problems. This creates circumstances that provide the support of facilitating a smoother workflow, with tension being reduced between all human and digital colleagues.
In defining AI's role, you are able to specify what it does, which is essential to avoid overlap and confusion with what people do.
By clearly defining the role, you help eliminate inefficiencies that come from each party doing similar requirements at the same time. You also allow your teams to focus on higher-value activities, while AI does all of the automated, routine, or complicated roles.
Adopting AI as an employee naturally creates an easier transition into acceptance across an organization.
The team accepts it as part of the workplace and its values, and not just something that is being placed upon them. Over time, this transition to include AI is an organic inclusion of the program into everyday operations.
Current AI technology is progressive, and we expect the future to bring even greater levels of independent decision-making capabilities to any AI system.
By already considering AI as an employee, each organization is prepared for that step. It allows (and prepares) organizations to scale roles of AI, without adjusting their structure every time.
Embedding a digital colleague is not much different from embedding a new human employee into the workplace; the AI agent needs clear work, the appropriate tools, and a way to embed into the existing processes and team hierarchy.
If these things are not considered or prepared, companies can, and often do, find themselves with disappointment.
Before deployment, organizations should clearly outline what exactly it is that the AI will be tasked with, aka: the scope, accountabilities, and objectives.
The more explicit the expectations are, the easier it will be for employees to accept AI as a teammate opposed to AI as a replacement.
For an AI digital colleague to start delivering value, it first has to be established with its credentials, logins, and access rights.
Oftentimes, companies will create profiles specifically for the AI in various systems like Teams, Slack, and email. This allows the AI to communicate just like any other human employee. To set employees up for success, introducing the AI to teams on "day 1" and establishing visibility of the AI will set the right tone and hopefully encourage interactions to feel seamless and natural.
During the onboarding phase, we want the AI to operate using real data, supported by ongoing supervision.
The goal of this phase is to help the AI learn patterns, calibrate accuracy, and adjust workflows. It is important for managers to keep an eye on results to ensure AI outputs advance business goals.
Feedback loops with human employees ensure continuous improvement and AI adaptability. By 90 days, the AI must have integrated into the team fully, with validated metrics that support its worth.
Builds trust with employees, as AI can follow the same process that is required of all human hires.
Creates accountability as the work and performance are connected to a defined role.
Reduces friction with employees as AI is provided initially with a low-commitment implementation that gets progressively more involved; it doesn't force adoption.
Facilitates scalability, as under our controlled implementation format, AI can take on more work in an increasing volume, with success.
Onboarding an equivalent digital colleague works well when it is both structured and planned, just like any human onboarding. A defined approach increases trust, creates accountability, and leads to an accelerated adoption.
Agentic AI as an employee is no longer just the stuff of speculation - with organizations like BNY Mellon already demonstrating the ability for digital colleagues (like VCRM) and human employees to co-exist and enhance the workplace with efficiency and accountability.
The challenge organizations must overcome is to develop the evidence, narratives, benchmarks, and indicators needed to shift the way we think about AI as what it is: a team player.
For organizations, changing their mindset is more than just integrating technology. It is about changing the culture, processes, and quasi-expectations of developing workplaces for colleagues (human and digital) to coexist.
The organisations that get ready for this new thinking now will be in a stronger position to leverage competition and engage in innovation in the coming years!
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