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AI has moved from experimentation to expectation. It is no longer the side project in the lab or the prototype demo in the boardroom. It has become a strategic priority. Budgets are growing, teams are forming, and timelines are accelerating.
But as the pressure builds to deliver returns, so does the realization that something is missing. For all the pilots and proofs of concept, many organizations are not seeing meaningful value. Models work. Features launch. But outcomes stall.
Why?
It is not because the technology isn’t good enough. It is because AI is being treated like a tool, when it behaves more like a teammate. To get value, you don’t just plug it in. You have to hire it, define its role, embed it in workflows, and coach it to perform.
The Research Is Becoming Clear
In The State of AI (March 2025), McKinsey reported that only 21 percent of gen-AI-using companies had redesigned workflows to support AI. Those companies outperformed others in realized business value.
Their research found:
- Clear road maps and centralized coordination were essential.
- Leading companies relied on role-based training and workforce reskilling.
- Redesigning workflows and implementing change management yielded the greatest returns.
- Governance, content oversight, and leadership sponsorship were key differentiators.
These points reinforce the need for structured digital labor onboarding with cross-functional coordination, clearly scoped roles, and a strategy rooted in operational impact.
BCG expands on this in AI Shifts IT Budgets to Growth Investments (2025), providing a comprehensive snapshot of how organizations are deploying AI agents at scale.
According to BCG:
- 58 percent of companies report they are already deploying AI agents, with another 35 percent actively exploring use cases.
- These companies report an average ROI of 13.7 percent—higher than other generative AI initiatives.
- Reported use cases include 30 to 50 percent efficiency gains in RFP responses and up to 10-point improvements in supply chain margins.
- Agents perform best when workflows are modular, with feedback loops and shared control mechanisms.
Why Most AI Initiatives Fall Short
Despite the push to adopt, only 25% of companies see meaningful value from their AI initiatives (BCG, 2025). Widespread experimentation does not guarantee results. Success comes from structure, role clarity, and operational alignment, not from technology alone.
BCG also found that 68% of organizations plan to maintain workforce size through upskilling, rather than replacement. This is not about automating away jobs. It is about equipping the workforce to do more, with AI as an augmenting teammate.
From Tasks to Teammates
At Asymbl, we believe AI agents should be deployed as digital labor. Not as a feature. Not as an automation add-on. But as a measurable contributor inside your business.
That’s why we don’t talk about “quickstarts.” We talk about onboarding, because quickstarts are often one-size-fits-all templates that skip context, motivation, and long-term accountability.
Clarifying the Teammate Model
AI as a teammate does not replace the suite of tools your business relies on. Instead, your digital teammate learns how to use those tools to help your team achieve more. The difference is not in the tech itself, but in how it is embedded, coached, and positioned as part of your workforce.
Motivation vs. Task: A Critical Distinction
A task list doesn’t tell you everything. Sometimes, the most valuable part of a task isn’t the task itself. It’s the context, the relationship, or the insight that happens during the process.
- Common Pitfall: Automating tasks without understanding motivation.
- Why it matters: Focusing only on the “what” misses the “why” that drives business value. Motivation shapes outcomes, relationships, and retention.
Example: Recruiter Scheduling
Take the example of a recruiter at a staffing firm. One responsibility is scheduling interviews with hiring managers. That sounds like a perfect use case for a digital agent.
For some firms, it is. The recruiter wants to move fast, get the interview confirmed, and keep candidates in motion. A scheduling agent can handle that efficiently.
But in other firms, that conversation is a key business moment. The recruiter uses it to learn more about the role, understand what’s changed since the req was opened, or even identify new hiring needs. It might be one of the few times they get the hiring manager’s attention.
If you automate the task without understanding the motivation, you lose value instead of creating it. Motivation directly shapes how well tasks align with outcomes, impacting everything from fill rates to client retention.
Asymbl Digital Labor Onboarding: Structure That Scales
Phase 1: Assessment and Planning
We define the role of the digital teammate, understand where it fits in the workflow, and clarify success metrics. We don’t just scope what to automate. We ask what will improve the way work happens. Motivation is factored in from the start.
Phase 2: Configuration and Enablement
We configure agents with real access to the tools, data, and environments where work happens. This includes Salesforce, Slack, email, ATS, knowledge bases, and other enterprise systems.
Just like human teammates, agents need the ability to observe, reason, and act. That’s why Asymbl equips agents with the necessary system access and application control, so they can update records, schedule actions, move data, and collaborate in real time.
This is made possible by the Agentforce platform, which connects agents to business applications, secure CRUD operations, and operational logic. It’s how digital labor shifts from assisting to executing, and why DIY approaches fall short at scale.
Phase 3: Optimization and Coaching
We measure performance, monitor usage, and tune behavior over time. Just like human teammates, digital labor needs feedback, context, and support. Coaching helps agents stay aligned to changing needs.
For example, agents assigned to candidate prequalification or interview scheduling are evaluated on speed, accuracy, and recruiter handoff quality.
Measurable Outcomes
At Asymbl, we’ve been actively using digital labor internally to drive real business outcomes, and the results are already material.
- With Agentforce SDR, Asymbl generated $575K in annual savings while expanding customer engagement by 427%, achieving the same impact as a team five times larger.
- In recruiting, two human recruiters and one digital agent processed 17,000+ applications and filled 93 roles in 94 days.
These are not pilots. They are operating roles embedded into our day-to-day workflows.
The Infrastructure Making This Possible
The shift to digital labor isn’t happening in a vacuum. It’s enabled by infrastructure that connects data, applications, and decisions in real time.
Salesforce’s Data Cloud is a major example. What began as a strategy to unify CRM data has become a cross-functional platform capable of organizing structured and unstructured information from sales, service, marketing, recruiting, and beyond. Data Cloud uses zero copy architecture, which allows agents to access business context in real time without duplicating or transferring data. This enables faster, more accurate decision-making across systems.
This is what powers Agentforce, the digital labor platform from Salesforce. Agentforce connects agents to business data, secure application controls, workflow triggers, and shared interfaces, allowing them to operate as teammates within existing systems. They respond to change, take action, and contribute alongside humans, not in isolation.
Asymbl builds on this foundation with modular applications like Recruiter Suite and components from the Asymbl Agentforce Suite. A structured onboarding model ensures that digital labor is deployed intentionally, where it can make a measurable contribution.
What We’re Learning from the Leaders
The companies seeing success with AI today are not necessarily the most technical. They are the most aligned.
- They build roles before they build agents.
- They design for motivation, not just efficiency.
- They coach digital labor with the same intention they bring to their human teams.
- They scale with structure, not shortcuts.
The Future of Work Is Here. Are You Ready to Hire AI?
Success with digital labor requires structure, intention, and the right strategy. The companies moving now are setting a new standard. Everyone else will be trying to catch up.
At Asymbl, we know this because we’ve done it ourselves. Our goal is for at least 20% of our total labor cost to be powered by digital teammates by the end of 2025, and we expect that number to rise dramatically in 2026. This isn’t about replacing people. It’s about giving them capable, reliable teammates that help everyone move faster and do better work.
Checklist: How to Assess Your AI Readiness
- Have you defined digital roles with clear success metrics?
- Are your workflows aligned for human-agent collaboration?
- Is your onboarding process structured around motivation, not just automation?
- Are feedback and performance reviews in place for both people and agents?
- Are you committed to upskilling your workforce rather than replacing it?
- Do you have a partner that can help you build a digital labor strategy that actually works?
Let’s build the digital workforce your business needs to grow with strategy and intention. If you're ready to start or need a guide, let's talk.
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