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As the internet was to the printing press, Artificial Intelligence is to the internet—a game-changer. Combining ubiquitous distributed computing with the hitherto unimaginable power of machine learning, the tiniest packet of work transforms into a building block for a giant leap in productivity.
This is Part 3 of a series. Read the other parts here:
Part 1: Work is Work
Part 2: Integrating Work
Part 3: Quantum Work Management
Part 4: Productively safe where the worker meets the work
[ Listen to the audio version, read by David Hodes]
In my domain of work management, this is the most profound systemic change we are ever likely to experience. I have chosen to call this radically new approach to the planning and performance of work ‘Quantum Work Management’. This is not because I am looking for a trendy name that artificially inflates the nature of the concept, but rather because of the connotations of the word quantum.
On the one hand, when we think of quantum, we think of the smallest unit of a thing—like a quantum packet of energy. In our case, we want to think about the smallest unit of work—the quantum packet of work. We want to dig down to the lowest level where all the most significant drivers of a large industrial organisation—safety, volume, and cost—are represented in the form of data packets. When we integrate all these quantum packets to understand how they might be managed to deliver on system goals, we arrive at the other everyday use of the word quantum—a step-change.
Quantum work management focuses on managing systems holistically, orchestrating the placement and interactions between people, materials, and processes. Safety, cost, and volume outcomes are considered, and trade-offs are made based on point-in-time needs. Work can be exhilarating because of the unprecedented results that can be accomplished. The work management system is an empowering enabler.
Exploiting advances in machine learning and other AI, quantum work management is characterised by digital systems, working in real time to continuously update information, bringing a networked perspective to objective decision-making.
Enterprises that run within the paradigm of quantum work management are heavily technology dependent, not as an end in itself, but as a means of providing better, faster information for real-time sense-making and decision-making.
A vital characteristic of this type of work management is that, where necessary, all information is provided and gathered at the work-front—where the worker meets the work. Mobile devices used by the workers and their supervisors are connected to the backend engine rooms that serve and store the quantum packets of data. Thus, information relating to safety, work methods, operating procedures and task lists can be served, as required, in a timely fashion, to both the supervisor and the person they supervise.
Extensive use is made of the internet of things (IoT) to know, for example, where the worker is, what piece of equipment they are working on, when they started and stopped their work, what job hazard analysis they have done and any other observations that may be necessary to communicate with the central control room.
“All information is provided and gathered at the work-front—where the worker meets the work.”
In such an environment, operations are led by intelligent software, computing down to the individual task level all necessary and sufficient conditions for its safe completion. There is minimal need for the intervention of manual systems, with the software designed to be highly interoperable not only with the suite of the owner’s enterprise software but also that of the vendors. Through constant feedback from the field, master data can be easily updated and continuously improved to ensure safety, resourcing and schedule data provide for deeper and deeper trust in the management system.
Let’s look at some examples of what the value chain for quantum work management might look like.
Everyone in the global resource pool has the app needed to respond to requests for workers. They are precleared by skills or competency and can bid on the app for the work offered. For example, NDT is going slower than expected, and to meet the return to service of a significant piece of equipment, the call goes out via the app to all those suitably qualified. Those notified respond and are automatically booked to the job for the agreed amount of time at the agreed rate. On confirmation of completion of the work, payment is automatically credited under contract conditions—whether to the individual, the vendor or the department providing the resource.
The enterprise senses performance and problems in real time and immediately reallocates resources. This ensures optimal performance both now and responds to the knock-on effects to planned work. For example, unscheduled equipment failure in processing requires trades that are fully allocated on capital projects. The quantum work management system calculates the relative benefit of continued work on the capital project versus reallocation to restoring production and allocates people to the highest impact work.
Everyone has a deep sense of system dynamics and how their work affects them. The ubiquitous production of timely and relevant data assures every person is making their most valuable contribution to the safe, cost-effective delivery of throughput. For example, a team of fitters, boilermakers and trade assistants is assembled from employee and vendor ranks on the day of operations to move to a part of the plant that needs them most. The supervisor of the emergent work put the call out to the ‘market’ for work, and all concerned knew it was the right decision, based on the true data and the ‘mateship’ ethos.
Critical enterprise information is communicated to everyone as required. That means employees, vendors, contractors, and consultants, with tracking of review and engagement. For example, new site safety protocols are implemented and pushed to all people with additional prompts at first and subsequent site, area, and equipment visits.
In such a world, there are digital twins not only of the assets being managed but also of the people who have managed them. These could be engineers, trades, or administrators at all levels of the management hierarchy. This depth of data is an enabler of ubiquitous and ever more valuable information, which we have arranged into three buckets, hindsight, insight, and foresight.
Three new lenses for sharper focus
Hindsight leverages historical information to inform planning and operations using:
Insight provides real-time information to inform day-of-operations, such as:
Foresight is how we use predictive analytics for workforce planning, unfolding a progressive context from:
Decision-making in the quantum work management world is internalised and individualised. People are empowered at the lowest level competent to make the decisions with which they are entrusted. This trust is enhanced by employing AI-assisted learning, which assures people across the organisation are working safely and productively with the requisite level of knowledge. Learning cards are served on an app, direct to the worker, and fit for purpose to any level on the management hierarchy.
“This is the most profound systemic change we are ever likely to experience.”
Performance can be tracked by topic and, through knowledge of individual forgetting curves, training plans can be customised to provide just the right knowledge, just in time. We can also assess team dynamics and ensure that novices are paired with experts and star performers with laggards to create a tide that lifts all ships.
AI-assisted learning makes working safely and productively a way of being rather than a knowledge test. The posture is proactive and dynamic. Because there is high resolution and visibility of capacity available for work by resource type, in time and place, they can be directed in real time to the activities which represent the highest leverage for the desired business outcome.
Stepping into this brave new world of quantum work management demands a significant investment in work integration as well as change leadership. However, the business performance will be measured in increased ROI, Capex effectiveness, revenue, margins, speed to market, due date dependability and quality. At the same time, you could look forward to reducing unit costs, inventories, work in process, lead times, and Opex. Which may even reduce or eliminate the need for Capex.
In fact, quantum work management is already here, at the frontier, where the pioneers hang out. Your choice is not whether or not to pay it any heed but rather to ask if you have any chance of succeeding over the long haul if you don’t engage sooner rather than later.
This is Part 3 of a series. Read the other parts here:
Part 1: Work is Work
Part 2: Integrating Work
Part 3: Quantum Work Management
Part 4: Productively safe where the worker meets the work
____________________________
What’s next?
The change to using Theory of Constraints (TOC) as an underlying operating system is both profound and exhilarating. We’ve developed the Systems Thinker Course to bring the ideas into your organisation.
____________________________Concept validation
A concept can look convincing in a presentation and still make an important task harder. A patient may misunderstand an indicator. A clinician may need information the design does not provide. A promising workflow may depend on support unavailable in the intended setting.
These problems become more expensive when the organization discovers them after committing to architecture, tooling or a launch plan.
We must ask how effectively concepts are validated before significant resources are committed. Patient Centered Design treats this as a learning discipline: turn ideas into testable solutions rapidly using low fidelity prototyping, put them in front of relevant users and gather evidence about whether they improve the outcomes that matter.
“A convincing presentation shows what the team hopes will happen. A useful test reveals what people actually do.”
A prototype without a learning objective can generate interesting comments while leaving the key decision unresolved. Start by identifying what must be true for the concept to succeed.
Does the patient intuitively understand the required action without explanation? Can the professional recognize when the result is ready to use? Does the proposed approach fit the work surrounding the task? Will people take meaningful steps toward adoption?
State the assumption and the evidence needed to assess it. Agree what would support proceeding, what would require revision and what could make the concept unviable. Gather quantitative data along with qualitative verbal feedback.
Prioritize questions with substantial consequences for the investment. An attractive enclosure deserves less attention than an unresolved issue that could prevent safe or practical use of the whole solution.
A picture is worth a thousand words. A simple user experience prototype is worth a thousand pictures. Choose artifacts that maximize learning with the least effort. The appropriate artifact depends on what the team needs to discover.
A sketch may help users explain how they interpret a proposed interaction. A physical mock-up may expose handling difficulties. A simulated interface can support a sequence of tasks. A more complete prototype may be necessary to examine timing, feedback or combined interactions.
Build enough realism to test the assumption accurately. Excessive detail can consume resources without improving the evidence, while insufficient fidelity can conceal the issue being investigated.
Make limitations clear to participants and observers. A manually simulated function may help test an experience, but it does not establish that the technology can deliver that function reliably.
Patients and healthcare professionals can differ in experience, confidence, physical ability, responsibilities, and access to support. Those differences influence whether the concept helps them complete the job.
Define the participants and contexts relevant to the test. Include people whose circumstances could expose difficulties the development team has overlooked.
A familiar expert may work around a confusing interface that challenges a new user. A clinician from a highly supported center may assume resources unavailable elsewhere. Research needs to reveal these dependencies.
Match the study to its purpose. Exploratory sessions can identify problems and guide iteration. They do not automatically establish population-wide performance or replace the evidence required for later design validation.
An Experience Café gives participants a structured opportunity to encounter concepts and talk about their experience. Its value comes from the quality of the scenario, observation and follow-up questions.
Set up situations that reflect what the person is trying to accomplish. Let participants interact with the artifact rather than rely solely on a demonstration by its designer. Build rough and rapid.
Assign roles to facilitators and observers. Agree how comments, actions and difficulties will be captured. Keep the session focused on learning rather than defending the proposed solution. Hold a conversation around the prototype. “Can you show me how you would use this?” “Tell me what you are thinking here.”
Ask participants to explain what they believe is happening and what they would do next. Observe where the concept supports that understanding and where they hesitate, improvise or seek help.
A facilitator can unintentionally make a weak concept appear successful by explaining each step or supplying missing information.
Use neutral prompts and allow participants to reveal their interpretation. When assistance is necessary, record it along with the point at which the person became stuck.
Follow observations with questions about purpose and reasoning. What did the participant expect? Which information did they look for? Why did they choose that action?
Separate a preference from evidence about task performance. “I like this one” may reflect appearance or familiarity. Successful completion, appropriate interpretation and manageable effort provide a stronger basis for assessing whether the concept addresses the need.
“Every explanation the facilitator must supply is a question about what the design expects the user to know.”
The product interaction belongs within a wider job. Test preparation, readiness, execution, monitoring and what follows, wherever these activities affect the concept’s value.
A device may work well once configured while setup introduces avoidable complexity. A result may be easy to view while the professional still needs to transfer it manually before acting.
Include relevant dependencies on caregivers, staff, information and other systems. Investigate whether the concept reduces burden for one participant by increasing it for another.
Use realistic scenarios appropriate to the development stage. Important exceptions and recovery steps deserve attention alongside routine success. An incomplete or interrupted task can reveal weaknesses that a carefully guided demonstration leaves hidden.
Ease of use involves what a person must notice, understand, remember, decide and physically do. A tidy interface can still demand substantial mental effort.
Human performance depends on limited attention, working memory and feedback. Examine how the concept supports those processes during a task.
Does it make the relevant information perceptible? Can the user understand its meaning? Is the next action clear? Can they recognize whether that action succeeded?
Look for unnecessary steps, demands to remember information across screens and alerts that interrupt without helping a decision. The best change may simplify a sequence or improve feedback rather than add another feature. Confirm its effect through observation and appropriate measures.
Choose measures that reflect the priority outcome and the study’s purpose. These may include completion time, errors, requests for help, repeated actions or failure to notice important information.
Subjective workload measures can complement observed performance. NASA Task Load Index can help structure participants’ assessment of workload, while PURE provides an expert assessment of the effort required to complete tasks. Satisfaction ratings on a Likert scale can be used to compare concepts directly with each other and a baseline solution.
Do not treat one measure as a complete account of usability. Faster completion may accompany misunderstanding. A participant may complete the task correctly while reporting effort that makes repeated use impractical.
Interpret results together and examine the conditions under which they occurred. Record the limitations so decision makers understand what the test has established.
Pretotyping examines whether people take meaningful action toward using or adopting an idea before the full solution exists. It addresses a different question from whether a working product can be engineered.
A clearly described concept, service simulation or expression-of-interest process may provide evidence about engagement. The strength of that evidence depends on the action required and the conditions surrounding it.
A click demonstrates less commitment than a substantive implementation conversation. Neither establishes clinical efficacy or successful device use.
For medical concepts, keep representations accurate and make the test status clear. Interpret behavioral evidence alongside clinical appropriateness, access requirements and the responsibilities of the people making adoption decisions. Interest alone cannot establish that the solution should proceed unchanged, particularly when workflows in the clinical setting are involved.
A value proposition explains which customer the concept serves, the job it supports and the difficulties it intends to reduce. It should connect directly with the outcomes identified through research.
Compare that proposition with the evidence from concept tests. Did participants experience the intended benefit? Did another difficulty become more important? Did the concept require assumptions that the intended setting cannot support? Did the concept do something better than a competitor’s current solution?
Capture both positive and negative findings. Use them to revise the concept, the proposition or the team’s understanding of the need.
Then test the important changes. Iteration should answer a question left open by the previous round. Repeating sessions without changing the artifact or research objective can accumulate comments while leaving uncertainty untouched.
Early concept validation supports decisions about direction and investment. It does not establish all the evidence needed for a finished medical device.
Technical performance, reliability, clinical benefit and intended-use safety require appropriate subsequent work. A convincing simulated experience may depend on functions the team has not yet demonstrated.
Make these boundaries visible in the decision record. State who participated, what was tested, the conditions, findings and unresolved issues. Explain how those findings affect the next commitment.
Overall, having a “winning concept” in hand, backed by user Experience Café data along with user needs in the form of outcome statements formally structured with a direction of improvement and unit of measure, sets a development team up for success. The outcome statement user needs provide a solid foundation for the design history file.
Rapid prototype experience testing gives the team answers to key decisions and sets a target to pursue early on. This results in faster development cycles, more robust regulatory submissions, and better returns on investment spending.
A Value Management Office can connect this learning with resources and stage decisions. Testing should help the organization commit at a pace justified by evidence and concentrate effort on the uncertainties that govern progress.
“Early validation creates value when it changes the concept or the commitment before an unresolved assumption becomes expensive.”
Examine one proposed investment. Which assumption could defeat the concept, and what is the least costly credible way to test it?
Prepare an appropriate artifact, involve representative users and observe meaningful tasks. Review the evidence, revise the concept and record the next learning decision. This gives Patient Centered Design a practical route toward solutions people can successfully use and organizations can responsibly develop.
A development team can become committed to a concept before it has seriously considered an alternative. The idea is familiar, a senior sponsor favors it or an available technology makes it attractive. Detailed engineering begins while the reason for choosing that approach remains largely untested.
Concept engineering provides a more disciplined basis for commitment. It develops several viable approaches, compares them against important needs and combines their strongest elements where doing so improves the solution.
Within Patient Centered Design, the purpose is to identify a concept that can help patients and healthcare professionals accomplish their jobs successfully. Question 2.1.1 examines the effectiveness of that work.
“A winning concept earns its place by addressing important needs and surviving comparison with credible alternatives.”
Concept work begins with a concise problem statement and an agreed objective. Both should describe the progress the intended users need, rather than assume the answer.
An illustrative problem might concern a clinician’s difficulty identifying whether the information needed for a treatment decision is complete. “Build a new dashboard” commits to one response before exploring the problem.
Clarify the users, operating context and desired outcome. Review the patient journey and the clinical work surrounding the interaction. Establish which unmet needs the concept must address.
Resolve major differences in interpretation before the selection exercise. A mechanical engineer, clinician and commercial leader can otherwise compare ideas while quietly applying different definitions of success. Shared language makes their conversation more useful.
Concept selection needs criteria grounded in research and requirements. Include the outcomes patients and professionals consider important, particularly those existing approaches satisfy poorly.
Then identify necessary capabilities and constraints. Relevant considerations may include safety, reliability, usability, manufacturing, service requirements, implementation and the resources available to develop the solution.
Separate mandatory conditions from characteristics that permit tradeoffs. A concept that cannot satisfy a necessary safety condition needs resolution or rejection; attractive performance elsewhere cannot compensate for that failure.
Define the criteria clearly enough for consistent comparison. “Easy to use” leaves the team guessing. The number of required actions, time to complete an activity or ability to recognize readiness can provide more concrete grounds for conversation, depending on the need.
Concept engineering encourages broad idea generation, with constraints applied more deliberately during selection. This gives the team room to consider alternatives before familiar solutions dominate.
Use sketches and short descriptions to make ideas visible. Invite different functions to contribute approaches, concerns and connections the team might otherwise miss.
The concepts should represent meaningful alternatives. Several versions of the same preferred design may leave the underlying assumptions untouched.
Explore how the required function could be achieved differently. Could information arrive earlier? Could preparation become simpler? Could a service change remove work the proposed product was intended to automate?
A concept should communicate its operating principle, important interactions and dependencies. A simple drawing can expose differences that remain hidden in a verbal description.
Explain what the patient or professional would do, what the system would provide and what supporting resources would be required. Include the activities before and after the visible product interaction.
Build enough detail to support the decision at hand. Early concept selection does not require complete engineering specifications, but an appealing image alone gives little basis for comparison.
Record what is known and what is assumed. If one idea appears stronger because its description omits difficult tasks, the matrix will reward an incomplete account of the solution. Consistent descriptions support a fairer evaluation.
The Pugh matrix provides a structured way to compare concepts. List the criteria as rows and the candidate concepts as columns. Choose a reference concept, known as the datum, against which alternatives will be assessed.
For each criterion, judge whether a concept is better than, similar to or worse than the datum. The simple notation is positive, same or negative.
Discuss the basis for each rating. Record supporting evidence and unresolved questions. The matrix makes differences visible and gives the team a shared place to examine them.
Totals can help identify promising candidates, but they should not make the decision automatically. The significance of a weakness depends on the criterion involved. Several small advantages may leave a concept unable to address its most important requirement.
“The matrix is useful when it exposes the reasoning behind a choice and the evidence still needed to defend it.”
Early conversations often rely on incomplete information. Simple comparisons can be more useful than elaborate scores that suggest a level of precision the team does not possess.
Weighted methods may help when criteria genuinely differ in importance. Paired comparisons can support more structured judgments about relative priority. When measured engineering performance is available, use those data directly in the evaluation.
Choose the method for the decision and evidence, rather than adding complexity by default. Explain how weights or ratings were established and which assumptions could change the result.
Check whether the leading concept remains attractive under reasonable changes to uncertain judgments. A decision that reverses after a minor rating adjustment deserves closer examination before the organization makes a substantial commitment.
Hybridization is a central part of concept engineering. A concept with several strengths may also have a weakness another option handles well.
Examine whether the useful elements can be combined into a stronger approach. For an illustrative monitoring solution, one concept may support straightforward preparation while another provides clearer feedback when a connection fails.
A hybrid might preserve both benefits, but it needs its own evaluation. Combining features can introduce new interfaces, added complexity or conflicting operating requirements.
Redraw the combined concept and reassess it against the criteria. Investigate whether it actually removes the weakness or merely moves the burden elsewhere. The aim is a coherent design whose interactions work together, rather than an accumulation of everyone’s favorite features.
Concept selection brings technical judgment, professional experience and organizational interests into the same conversation. The quality of that conversation affects the outcome.
Agree on meeting ground rules and use a team with relevant expertise across functions. Ensure that a strong personality or senior title does not become a substitute for evidence.
When disagreement cannot be resolved, capture the question and define an action. Assign an owner, the evidence required and a completion date. An independent facilitator can help when team dynamics obstruct useful comparison.
The method can make judgments more transparent, but it does not eliminate subjectivity. Leaders should expect the team to explain its reasoning and show how important uncertainties will be resolved.
The leading idea may depend on uncertain technical performance, user behavior, supplier capability or implementation conditions that significantly affect time to market. Identify those dependencies before detailed development absorbs significant resources.
Plan focused experiments, research or prototypes to address them. Match each activity to a question that could change the selection decision.
If a concept depends on clinicians adopting a new preparation routine, a technical bench test will not establish that assumption. If it depends on a material performing under particular conditions, user enthusiasm will not resolve the engineering question.
Prioritize uncertainties with serious consequences for the concept’s viability. Early learning should help the team avoid expensive redesign while preserving options until the relevant evidence is available.
At this point in the design process, using patient and healthcare professional journey mapping satisfies the Empathy stage of Design Thinking. Understanding the jobs to be done and desired outcomes satisfies the Define stage of Design Thinking. Concept engineering at its core is a practice that ensures the best solution direction is chosen before significant expense is incurred and satisfies the Ideate stage of Design Thinking.
With a selection of “winning concepts” in your pocket, complete the Design Thinking stages by rapidly prototyping and testing solutions with customers to gather data on which concepts your customers desire most. The Design Thinking stages provide an explanation of why a solution is preferred and a record of why alternatives were not chosen.
Retain the criteria, important comparisons, hybridization decisions and unresolved risks. This record helps future teams understand the consequences of a proposed change.
Selection establishes a direction for further learning and development. It does not prove that the solution meets user needs or performs safely in practice. Concept validation and subsequent engineering evidence must support those conclusions.
Keep the relationship with the original needs visible. A chosen architecture can gradually lose important benefits as specifications and implementation choices evolve. Traceability helps the team notice when a tradeoff undermines the reason the concept was selected.
The Voice of Business and Voice of Technology should inform selection alongside customer evidence. A concept needs a realistic development route and an operating environment capable of supporting it.
A Value Management Office can connect these choices with resources and portfolio priorities. Systems thinking helps the team examine whether the proposed improvement addresses a constraint governing patient access or successful use.
Evaluate the complete contribution. A technically impressive device can introduce training, service or clinical work that limits its benefit in the wider pathway.
“Concept engineering should leave the organization with a reasoned choice, a manageable learning plan and a clear connection to patient benefit.”
Review one current concept decision. Can the team show credible alternatives, agreed criteria, the basis for selection and the assumptions requiring further evidence?
Bring the relevant disciplines together, compare options and examine stronger combinations. Then test the uncertainties that could change the decision. That is how Patient Centered Design builds a defensible route from important needs to better products.
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We’ll only use your email address for this newsletter. No sales calls