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Most Salesforce practices hit the same problem when they start using AI agents: they bolt AI onto existing delivery processes and wonder why velocity doesn’t improve. Agents generate configurations faster than traditional review workflows can approve them. What was designed to ensure quality becomes the bottleneck.
MIT Sloan and BCG just surveyed 2,102 executives across 116 countries. 76% now view agentic AI as a coworker, not a tool. That shift matters because you can’t manage something that acts like both infrastructure and a team member using processes designed for one or the other.
The Management Framework Problem
Sam Ransbotham, one of the MIT report authors, describes the challenge: “We had a nice, clean separation between technology and people, with management processes designed around that distinction. But agentic AI is neither a tool nor a teammate. It’s both.”
Most Salesforce practices try to manage agents like infrastructure (doesn’t work because they make autonomous decisions) or like team members (doesn’t work because they don’t need status meetings). The practices adapting fastest are the ones willing to redesign delivery processes before they have perfect clarity on what the end state looks like.
At realfast, we stopped asking “how do we add AI to our current sprint process?” and started asking “if we built delivery from scratch with agents as a foundational capability, what would we design?”
That shift required admitting that processes which worked for years needed fundamental redesign, not incremental adjustment.
What Changes When Roles Evaporate
The MIT report shows 45% of agentic AI leaders anticipate reducing middle management layers within three years. When agents autonomously handle status tracking, update coordination, and task assignment, traditional project coordinator roles become unnecessary.
What survives are the roles that do what AI agents cannot. Making judgment calls when a client’s stated requirement conflicts with what they actually need. Architecting integrations across Salesforce and legacy systems that each have different constraints. Coaching developers through problems where there’s no documented answer.
What doesn’t survive are roles where the primary value is keeping projects organized, updating Jira tickets, or translating between technical and business teams.
Understanding this pattern now saves months of trial-and-error. Most practices are treating AI like a faster way to write code while keeping everything else the same. That doesn’t match what agents actually do in production.
The Operating Model Shift
The report shows 66% of leading agentic AI organizations expect operating model changes within three years. Only 42% of organizations just starting with agentic AI expect the same. That gap reveals something: the more you use AI agents in production, the more you see that current delivery structures create bottlenecks.
Survey data shows expectations that AI systems will have decision-making authority grow by 250% over three years, with 58% of leaders anticipating governance changes. At realfast, our agents already decide which Flow template to use, how to structure permission sets, whether to use a trigger or Process Builder, how to architect test classes. They don’t suggest approaches for approval. They implement and notify.
That required rebuilding our quality process. We don’t review every line of code. We audit whether solutions solve the actual business problem and whether the patterns agents are learning match our architecture principles. Outcome review, not execution review.
The architects and developers who made this shift command premium rates in the market right now. Not because they write better Apex, but because they can design delivery processes that work with AI velocity instead of fighting it.
The Job Satisfaction Data
The MIT data shows 95% of people at leading agentic AI organizations report AI positively impacting job satisfaction. That contradicts the narrative about AI making work worse.
The reason makes sense: nobody became a Salesforce developer to write the same validation rule pattern again and again. When agents handle repetitive work, developers spend time on problems that require human judgment. Why is this integration failing intermittently? Which of these conflicting requirements should take priority? How do we architect this to handle the volume they’ll have in two years?
The work got harder (more judgment calls, more ambiguity), but also more interesting.
The differentiation concern is real though. 76% of individuals at leading agentic organizations believe AI affects how they differentiate themselves from coworkers. When agents commoditize configuration work, differentiation moves to business judgment and identifying which problems actually need solving versus which problems clients think they need solved.
The Redesign Challenge
The challenge isn’t adding Agentforce to your Salesforce org. The technology works and it’s getting better fast. The challenge is recognizing that current implementation models with sprint structure, review process, team roles, resource planning were designed for humans writing all the code.
Once agents handle execution, the real constraint becomes the system wrapped around them. Reworking that system is where the efficiency gains actually come from.
If you want to see how AI-native delivery operates in production, get in touch. We’ve already done that successfully for teams like TASC Outsourcing, and can do it for you too.