From factories to distribution centers, robots are beginning to move from repeating instructions to deciding what to do next – and that shift is where the real return on investment for executives will come into focus over the next few years.
FROM COBOTS TO DECIDING ROBOTS
For the past decade, collaborative robots have been framed as efficient, tireless helpers that execute tightly scripted, repetitive tasks while humans retain all meaningful decision rights.
They weld along preset seams, move pallets on fixed routes, and stop whenever a sensor detects something unexpected.
That model delivered efficiency, but it also hard-coded a ceiling in terms of what automation could achieve, because every exception still escalated back to a human.
Agentic artificial intelligence (AI) begins to erode that ceiling by giving robots bounded autonomy over their own micro decisions.
Instead of asking, ‘What did the programmer specify?’, these systems can ask, ‘Given what I see, what should happen next?’ and then act without pausing the line to wait for a supervisor.
The result is not science fiction and general intelligence, but a pragmatic step change –robots that own an entire loop of work, not just one motion within it.
HOW ROBOTS LEARN FROM HUMANS AND MANUALS
Two advances make this shift possible: learning from video and language. On the video side, robots can now be trained by watching highly skilled operators perform tasks, using vision models to map human motions, tools, and outcomes into machine-understandable patterns.
The robot is not simply replaying a recorded trajectory; it is learning how specific visual and physical conditions correlate with the right next action.
On the language side, Large Language Models (LLMs) and Vision Language Models (VLMs) ingest the same instruction manuals and work procedures that technicians use, turning dense documentation into operational ‘playbooks.’
Instead of a human reading a 200-page welding or casting manual and then translating it into parameters for a robot, the AI layer can consume that manual directly and infer rules such as acceptable tolerances, defect taxonomies, and escalation paths.
When you combine those capabilities, you get robots that are grounded both in how humans actually work and in how the process is supposed to work on paper.
THE AUTONOMOUS INSPECTION LOOP
The first place this new autonomy is showing up at scale is when it comes to inspection.
Inspection is data-rich, safety-critical, and historically under-automated, making it a perfect beachhead for agentic behavior. In complex welding, casting, and forging, for example, robots can now:
- Capture high-resolution visual and depth data across joints, surfaces, and internal geometries.
- Classify defects – porosity, cracks, undercut, misalignment, inclusions – against standards encoded from manuals and previous human judgments.
- Decide whether a given nonconformance is acceptable, reworkable, or scrap, without asking a human to review every frame.
Crucially, today’s systems can go one step further – they can close the loop by autonomously generating and inserting task orders into repair queues.
If a weld on an aircraft frame falls outside tolerance bands, the robot doesn’t just flag a red light – it logs the defect type, location, severity, and recommended remedy, then creates a digital work order for the appropriate technician or downstream robot cell.
That turns inspection from a passive gate into an active orchestrator of rework, shortening cycle times and making quality data immediately actionable.
For manufacturers and logistics operators, this loop translates into measurable outcomes: higher first-time yield, lower rework labor, better traceability, and more stable schedules, because fewer surprises surface late in the process.
The executive lens should be less ‘How many robots do we have?’ and more ‘How many closed loops have we handed over to autonomous systems?’
WHAT AI STILL CAN’T DO, AND WHY HUMANS STAY IN THE LOOP
Even as inspection becomes increasingly autonomous, robots are not yet ready to own the most complex process decisions.
In high complexity welding, human experts still synthesize subtle cues – a faint change in arc sound, a slight discoloration, the feel of heat through gloves – to decide in real time how to adjust technique, consumables, and temperature.
These judgments draw on years of tacit knowledge that has never been fully documented, let alone labeled at scale for training.
Current systems also struggle with genuinely novel scenarios, such as one-off repairs on unique assets, improvisational fixturing, or interpreting incomplete or inconsistent documentation.
When operators rapidly adapt to a slightly warped casting or a nonstandard joint configuration, they are blending formal rules with intuition about risk, cost, and downstream impact that today’s models only approximate.
As a result, the near-term equilibrium is clear – robots will increasingly decide within well-bounded domains, while humans continue to define the boundaries, handle edge cases, and refine the playbooks.
Executives should resist both extremes – the hype that robots will imminently replace skilled trades, and the skepticism that ‘they’ll never do what our people do.’
The more realistic path is a progressive handoff – inspection autonomy first, followed by autonomy in standardized rework procedures, and only later in complex, craft-intensive operations as more multimodal data is captured from expert human performance.
HOW LEADERS CAN CAPTURE THE ROI OF ‘THINKING’ ROBOTS
To capitalize on agentic robots, leaders should recast this shift as an information and decision rights transformation, not a hardware refresh. Three priorities stand out:
- Build a robust digital backbone – Agentic systems depend on consistent access to 3D models, historical quality data, manuals, and work instructions; fragmentary or siloed data will become the biggest brake on autonomy, not sensor performance.
- Treat expert knowledge as a strategic asset – Systematically capture expert welders’ and inspectors’ decisions in video and data form so that future models have rich ground truth for learning, rather than relying solely on documentation that lags practice.
- Redesign roles and key performance indicators (KPIs) – As robots own more closed loops, human work shifts toward oversight, exception handling, and continuous improvement; metrics should recognize reduced deviations, faster recovery, and quality stability, not just throughput.
A simple thought experiment for a plant leader illustrates the opportunity – imagine any repetitive, judgment-light activity where your best people say, ‘I know the answer the second I see it.’
Those are prime candidates for agentic inspection and triage. Start there, prove that a robot can own the entire loop from observation to action, and then expand outward into more demanding tasks as the technology and your data maturity evolve.
Executives who move early will not just own more robots; they will own more of the decision fabric of their operations.
In an era where resilience, quality, and speed are strategic differentiators, shifting decisions from ‘repeat what you were told’ to ‘decide what must happen next’ may be the most consequential automation upgrade of the next decade.
