Industrialized AI — a briefing for business and engineering leaders
Choose a slide
Make AI work at industrial scale with manufacturing principles.
- Treat models as imperfect machines
- Design around their capabilities and limitations, rather than assigning them human roles.
- Put models in predefined workflows
- The workflow controls what happens and enforces the business’s rules, required checks, and operating limits.
- Manage imperfection through continuous measurement and improvement
- Assess how well work is created, checked, and corrected, then improve the methods.
This gives the business more control over the outcomes of its AI investment, including how much it spends achieving them and what risk it accepts.
Variable AI output makes business outcomes harder to predict.
Check and correct individual results
Checking and correction add cost and delay delivery. Growing backlogs create pressure to release work before it has been adequately checked.
Use work without adequate checks
Undetected defects can become service failures, remediation costs, and damage to customer trust.
Human and AI reviewers can both miss defects, so the reliability of the checks also needs to be established.
When models control the workflow, the process becomes another source of variation.
Chat encouraged systems built around agents taking on human roles and coordinating through conversations. This can extend to letting the agents decide how work is carried out and accepted.
- What work to do
- Similar jobs can involve different amounts of work and cost.
- Which checks to run
- Results can be accepted on different grounds.
- When to stop
- Spending and delivery time become harder to predict.
It becomes harder to explain failures or establish whether a change improved the process.
Predefined workflows enforce the business’s rules and limits.
Manufacturing uses process control to make imperfect machinery produce work to an agreed standard. Existing AI models and tools can create, check, and correct work, while deterministic software controls the sequence, required checks, and stopping conditions.
Measurement gives the business evidence of what its process can achieve.
The methods used to create, check, and correct work can all fail. Each needs to be assessed and monitored.
Measure
Assess methods against known examples and track quality, cost, and delivery time across runs.
Improve
Address weaknesses in how work is created, checked, and corrected.
Check the effect
Establish whether changes improved results and reduced variation.
People can supervise the process across many runs, investigating exceptions and improving methods.
Control the trade-offs between quality, cost, and delivery time.
Different work calls for different standards, budgets, and deadlines. Measuring and improving the process gives the business more control over that balance, so it can choose how much to invest in the outcomes it needs and what risk it is prepared to accept.
- Required quality and acceptable risk
- How often results meet the standard and which failures still occur.
- Investment
- The complete cost of achieving the required outcomes, including developing, operating, and improving the process.
- Delivery time
- Time to a usable result, its variation, and the cost of meeting a tighter deadline.
Greater control can make more products and services commercially viable.
That gives businesses more choice over what they offer and whom they serve.
Larger projects
Commit to larger projects with better evidence of the cost, delivery time, and risk involved.
More frequent services
Offer updated analysis and reports as their source material changes.
More tailored offerings
Offer customer-specific reports where only a standard report was commercially practical.
These opportunities depend on the process meeting the required standard at a viable cost and an acceptable level of risk.
Start with a specific business outcome and measure what it takes to achieve it.
- Choose the outcome
- Select a valuable product or service and agree the required results, budget, deadline, and acceptable risk.
- Establish the process
- Assign responsibility and measure quality, complete cost per usable result, delivery time, and remaining risks.
- Decide how to invest
- Use the results to decide whether to improve, expand, or stop the work.
The opportunity is to change the economics of cognitive work by making it practical to deliver at industrial scale.
Make AI work at industrial scale with manufacturing principles.
- Treat models as imperfect machines
- Design around their capabilities and limitations, rather than assigning them human roles.
- Put models in predefined workflows
- The workflow controls what happens and enforces the business’s rules, required checks, and operating limits.
- Manage imperfection through continuous measurement and improvement
- Assess how well work is created, checked, and corrected, then improve the methods.
This gives the business more control over the outcomes of its AI investment, including how much it spends achieving them and what risk it accepts.
Variable AI output makes business outcomes harder to predict.
Check and correct individual results
Checking and correction add cost and delay delivery. Growing backlogs create pressure to release work before it has been adequately checked.
Use work without adequate checks
Undetected defects can become service failures, remediation costs, and damage to customer trust.
Human and AI reviewers can both miss defects, so the reliability of the checks also needs to be established.
When models control the workflow, the process becomes another source of variation.
Chat encouraged systems built around agents taking on human roles and coordinating through conversations. This can extend to letting the agents decide how work is carried out and accepted.
- What work to do
- Similar jobs can involve different amounts of work and cost.
- Which checks to run
- Results can be accepted on different grounds.
- When to stop
- Spending and delivery time become harder to predict.
It becomes harder to explain failures or establish whether a change improved the process.
Predefined workflows enforce the business’s rules and limits.
Manufacturing uses process control to make imperfect machinery produce work to an agreed standard. Existing AI models and tools can create, check, and correct work, while deterministic software controls the sequence, required checks, and stopping conditions.
Measurement gives the business evidence of what its process can achieve.
The methods used to create, check, and correct work can all fail. Each needs to be assessed and monitored.
Measure
Assess methods against known examples and track quality, cost, and delivery time across runs.
Improve
Address weaknesses in how work is created, checked, and corrected.
Check the effect
Establish whether changes improved results and reduced variation.
People can supervise the process across many runs, investigating exceptions and improving methods.
Control the trade-offs between quality, cost, and delivery time.
Different work calls for different standards, budgets, and deadlines. Measuring and improving the process gives the business more control over that balance, so it can choose how much to invest in the outcomes it needs and what risk it is prepared to accept.
- Required quality and acceptable risk
- How often results meet the standard and which failures still occur.
- Investment
- The complete cost of achieving the required outcomes, including developing, operating, and improving the process.
- Delivery time
- Time to a usable result, its variation, and the cost of meeting a tighter deadline.
Greater control can make more products and services commercially viable.
That gives businesses more choice over what they offer and whom they serve.
Larger projects
Commit to larger projects with better evidence of the cost, delivery time, and risk involved.
More frequent services
Offer updated analysis and reports as their source material changes.
More tailored offerings
Offer customer-specific reports where only a standard report was commercially practical.
These opportunities depend on the process meeting the required standard at a viable cost and an acceptable level of risk.
Start with a specific business outcome and measure what it takes to achieve it.
- Choose the outcome
- Select a valuable product or service and agree the required results, budget, deadline, and acceptable risk.
- Establish the process
- Assign responsibility and measure quality, complete cost per usable result, delivery time, and remaining risks.
- Decide how to invest
- Use the results to decide whether to improve, expand, or stop the work.
The opportunity is to change the economics of cognitive work by making it practical to deliver at industrial scale.