Industrialized AI — a briefing for business and engineering leaders
Choose a slide
Make AI work at industrial scale with manufacturing principles
Industrialized AI applies manufacturing discipline to give businesses more control over the outcomes of their AI investment.
- 1
Treat models as imperfect machines
Design work around their capabilities and limitations, rather than assigning them human roles.
- 2
Put models in predefined pipelines
Keep business rules, required checks, and stopping conditions under deterministic control.
- 3
Measure and improve process performance
Assess and improve how work is created, checked, and corrected, so imperfection can be managed.
AI can produce convincing work that is wrong
Businesses must pay to check and correct the work or risk costly failures. Variation in results and working methods can lead to different business outcomes, making that trade-off hard to predict.
Work produced with AI
Expected standard, budget, and deadline.
- Usable work within budget and on time
Verification and correction fit the plan, with acceptable remaining risk.
- Usable work, but additional cost or delay
Reducing error risk costs more or takes longer than planned.
- Unusable work or costly failures
Defects remain unresolved or undetected, making the work unusable or causing costly failures after use.
Uncontrolled variation makes it hard to estimate checking and correction costs or the risk of failures after use. The business cannot confidently judge whether the expected benefit is worth the cost and risk.
Treat AI models as imperfect machines
Workflows built around people reflect limits on attention, coordination, and capacity that need to be reconsidered when machines can perform the work.
AI fitted into human roles
- Agents inherit tasks and handoffs organised around people.
- Work needed to coordinate the old process is carried forward.
- Growth can still depend on people directing and overseeing individual jobs.
Fitting AI into human roles encourages attempts to overcome the model’s imperfections.
Work designed around models
- Models and tools are combined around the required result.
- Tasks can be eliminated, combined, or repeated automatically.
- People can supervise many jobs, with the machinery handling routine work.
Treating AI models as machinery means accepting their imperfections, measuring them, and designing the work to manage them.
Designing work around the models’ capabilities and limitations can make a wider range of products and services commercially viable and bring them within reach of more customers.
Predefined pipelines enforce the business’s rules and limits
Three jobs, run under each approach.
Model-led process
The model chooses how the work proceeds, so both the process and the result can vary.
Predefined pipeline
The pipeline controls how work is done, checked, and accepted, enforcing the business’s standards and limits on every run.
A repeatable process gives the business greater control over the outcomes of its AI investment.
Measure and improve process performance
Models and inspectors remain fallible. Continual measurement establishes what the process can achieve and where it needs improvement.
Gather evidence across repeated runs
- Quality
- Defects and missed errors
- Cost
- Full cost per usable result
- Delivery time
- Duration and variation
Test reliability
Use examples whose correct answers have been established independently of the process to test the work and its inspection.
Assess and improve
Identify weaknesses, change how the work is done, and measure whether the changes improve results.
People supervise the process across runs, investigate exceptions, and improve the methods.
Organise AI work as a manufacturing process
Models and tools can manufacture, inspect, and repair the work automatically; people supervise and improve the process.
Predefined pipeline
Measure and improve every stage.
Required result
Agreed standard and limits
Manufacture
Generate a first version.
Inspect
Check the product against its requirements.
Accepted work
Required checks passed.
Repair
Correct detected defects.
Stop or escalate
When limits are reached.
The business can invest in a repeatable way to achieve a required result, with evidence of its cost, delivery time, and remaining risk.
Control the trade-offs between quality, cost, and delivery time
Choose models, inspection and repair effort, and capacity to meet the agreed quality, budget, deadline, and risk limits.
Business requirements
- Result and standard
Agreed scope, quality, and tolerances.
- Budget
Available investment in the work.
- Deadline
When accepted work is needed.
Production choices
- Models and methods
Choose models, methods, and reusable parts to balance cost, speed, and repair effort.
- Inspection and repair effort
Choose how much to inspect and repair, balancing cost, time, and missed defects.
- Capacity and parallel work
Choose how much work runs at once to balance delivery time and resource cost.
Expected performance
- Quality
Ability to meet the agreed standard.
- Full cost
Including inspection and repair.
- Delivery time
Time to accepted work.
- Remaining risk
Likelihood and impact of failures.
Measured process performance
Test how production choices affect results.
Greater control can make more products and services commercially viable
Knowing what it takes to achieve an outcome can make previously uncertain or expensive work worth offering.
Larger engagements
Commit to larger projects with better evidence of the cost, delivery time, and risk involved.
More frequent services
Make more frequent delivery viable by controlling production cost and turnaround time.
More tailored offerings
Use a shared manufacturing process to meet individual customer requirements.
Commercial viability still depends on meeting the required standard at a viable cost and an acceptable level of risk.
The opportunity is to change the economics of cognitive work by making it practical to deliver at industrial scale
- 1
Choose a valuable outcome
Agree the required result, budget, deadline, and acceptable risk.
- 2
Establish capability
Assign responsibility, set up the production process, and measure quality, full cost, delivery time, and remaining risk.
- 3
Decide how to invest
Use the evidence to decide whether to improve the process, expand its use, or stop investing.
Industrialized AI gives the business a production process it can control, improve, and scale.
Make AI work at industrial scale with manufacturing principles
Industrialized AI applies manufacturing discipline to give businesses more control over the outcomes of their AI investment.
- 1
Treat models as imperfect machines
Design work around their capabilities and limitations, rather than assigning them human roles.
- 2
Put models in predefined pipelines
Keep business rules, required checks, and stopping conditions under deterministic control.
- 3
Measure and improve process performance
Assess and improve how work is created, checked, and corrected, so imperfection can be managed.
AI can produce convincing work that is wrong
Businesses must pay to check and correct the work or risk costly failures. Variation in results and working methods can lead to different business outcomes, making that trade-off hard to predict.
Work produced with AI
Expected standard, budget, and deadline.
- Usable work within budget and on time
Verification and correction fit the plan, with acceptable remaining risk.
- Usable work, but additional cost or delay
Reducing error risk costs more or takes longer than planned.
- Unusable work or costly failures
Defects remain unresolved or undetected, making the work unusable or causing costly failures after use.
Uncontrolled variation makes it hard to estimate checking and correction costs or the risk of failures after use. The business cannot confidently judge whether the expected benefit is worth the cost and risk.
Treat AI models as imperfect machines
Workflows built around people reflect limits on attention, coordination, and capacity that need to be reconsidered when machines can perform the work.
AI fitted into human roles
- Agents inherit tasks and handoffs organised around people.
- Work needed to coordinate the old process is carried forward.
- Growth can still depend on people directing and overseeing individual jobs.
Fitting AI into human roles encourages attempts to overcome the model’s imperfections.
Work designed around models
- Models and tools are combined around the required result.
- Tasks can be eliminated, combined, or repeated automatically.
- People can supervise many jobs, with the machinery handling routine work.
Treating AI models as machinery means accepting their imperfections, measuring them, and designing the work to manage them.
Designing work around the models’ capabilities and limitations can make a wider range of products and services commercially viable and bring them within reach of more customers.
Predefined pipelines enforce the business’s rules and limits
Three jobs, run under each approach.
Model-led process
The model chooses how the work proceeds, so both the process and the result can vary.
Predefined pipeline
The pipeline controls how work is done, checked, and accepted, enforcing the business’s standards and limits on every run.
A repeatable process gives the business greater control over the outcomes of its AI investment.
Measure and improve process performance
Models and inspectors remain fallible. Continual measurement establishes what the process can achieve and where it needs improvement.
Gather evidence across repeated runs
- Quality
- Defects and missed errors
- Cost
- Full cost per usable result
- Delivery time
- Duration and variation
Test reliability
Use examples whose correct answers have been established independently of the process to test the work and its inspection.
Assess and improve
Identify weaknesses, change how the work is done, and measure whether the changes improve results.
People supervise the process across runs, investigate exceptions, and improve the methods.
Organise AI work as a manufacturing process
Models and tools can manufacture, inspect, and repair the work automatically; people supervise and improve the process.
Predefined pipeline
Measure and improve every stage.
Required result
Agreed standard and limits
Manufacture
Generate a first version.
Inspect
Check the product against its requirements.
Accepted work
Required checks passed.
Repair
Correct detected defects.
Stop or escalate
When limits are reached.
The business can invest in a repeatable way to achieve a required result, with evidence of its cost, delivery time, and remaining risk.
Control the trade-offs between quality, cost, and delivery time
Choose models, inspection and repair effort, and capacity to meet the agreed quality, budget, deadline, and risk limits.
Business requirements
- Result and standard
Agreed scope, quality, and tolerances.
- Budget
Available investment in the work.
- Deadline
When accepted work is needed.
Production choices
- Models and methods
Choose models, methods, and reusable parts to balance cost, speed, and repair effort.
- Inspection and repair effort
Choose how much to inspect and repair, balancing cost, time, and missed defects.
- Capacity and parallel work
Choose how much work runs at once to balance delivery time and resource cost.
Expected performance
- Quality
Ability to meet the agreed standard.
- Full cost
Including inspection and repair.
- Delivery time
Time to accepted work.
- Remaining risk
Likelihood and impact of failures.
Measured process performance
Test how production choices affect results.
Greater control can make more products and services commercially viable
Knowing what it takes to achieve an outcome can make previously uncertain or expensive work worth offering.
Larger engagements
Commit to larger projects with better evidence of the cost, delivery time, and risk involved.
More frequent services
Make more frequent delivery viable by controlling production cost and turnaround time.
More tailored offerings
Use a shared manufacturing process to meet individual customer requirements.
Commercial viability still depends on meeting the required standard at a viable cost and an acceptable level of risk.
The opportunity is to change the economics of cognitive work by making it practical to deliver at industrial scale
- 1
Choose a valuable outcome
Agree the required result, budget, deadline, and acceptable risk.
- 2
Establish capability
Assign responsibility, set up the production process, and measure quality, full cost, delivery time, and remaining risk.
- 3
Decide how to invest
Use the evidence to decide whether to improve the process, expand its use, or stop investing.
Industrialized AI gives the business a production process it can control, improve, and scale.