# Avid Problem Solvers — full text > Operations consulting firm using Lean Six Sigma, Agile and AI to diagnose and resolve backend and frontend operational inefficiencies. One-hour consultations, $1,199 flat. This file contains the full text of every published page on https://avidproblemsolvers.com, one after another. Each section begins with its canonical URL. --- Source: https://avidproblemsolvers.com/ # We fix the problems you can name. And the ones you can't — yet. > Operations consulting firm using Lean Six Sigma, Agile and AI to diagnose and resolve backend and frontend operational inefficiencies. One-hour consultations, $1,199 flat. Canonical: https://avidproblemsolvers.com/ Avid Problem Solvers combines Lean Six Sigma, Agile, and AI to resolve the operational problems slowing your business down — the visible ones and the hidden ones. ## What we do Avid Problem Solvers is an operations consulting firm. We diagnose and resolve inefficiencies across backend operations — process, workflow, systems, data — and frontend operations — customer experience, service delivery — for companies in any industry. - **Known problems:** Already on a dashboard. Somebody named it months ago and it hasn't moved. - **Unknown problems:** Nobody has measured it yet. It shows up as a number that feels wrong. ## How we work **Methodology is a means, not a mission.** We don't apply Lean Six Sigma, Agile, or AI as a fixed toolkit. We start with your problem and use whichever method it actually calls for — sometimes all three, sometimes one. - **Lean Six Sigma:** Root-cause discipline. Waste removed for good, not managed around. - **Agile:** Iteration and fast feedback where certainty is low. - **Ai:** Pattern detection across data you already have, surfacing what manual review misses. ## Who you'll be talking to Twenty years of operations work, across regulated, operational, and go-to-market problems. - **Bfsi & Insurance:** Regulated environments where the cost of getting it wrong is measured in fines, not friction. - **Quick Commerce & Logistics:** High-velocity operations where a small inefficiency compounds thousands of times a day. - **Gtm Strategy:** Commercial engines where the bottleneck is rarely where it looks. The sectors change. The shape of the problems doesn't. Our clients don't appear on this page. Some of them are competitors of each other. If your problem is one you wouldn't discuss in a board meeting, that's the sort of work we do. ## The offer **$1,199 — one hour, flat.** One hour with someone who has solved this before. Bring your hardest problem, leave with a path to the first three things to do on Monday. How it works: Secure Your Hour → Choose Your Time → Send The Brief. Payment secured by Razorpay · You'll choose your slot immediately after. No retainer. No proposal process. No discovery call first. ## Frequently asked questions ### What does Avid Problem Solvers do? Avid Problem Solvers is an operations consulting firm. We diagnose and resolve inefficiencies across backend operations — process, workflow, systems, data — and frontend operations — customer experience, service delivery — for companies in any industry. ### What methodologies does APS use? APS applies Lean Six Sigma for root-cause analysis and waste elimination, Agile for iterative delivery under uncertainty, and AI for pattern detection across existing business data. Methods are selected based on the problem, not applied as a fixed toolkit. ### How much does an APS consultation cost? APS offers a single flat-rate consultation at $1,199 for one hour, with no retainer, no proposal process and no preliminary discovery call. ### How do I book a consultation with APS? Payment is made first through Razorpay, after which you immediately choose your preferred time slot. Sessions run 60 minutes over Google Meet, with availability three days per week. ### What sectors does APS have experience in? APS brings twenty years of operations experience across BFSI and insurance, quick commerce and logistics, and go-to-market strategy, and works with companies in any industry. ### Does APS publish client names? No. APS does not name clients on its website as a matter of client confidentiality. --- Source: https://avidproblemsolvers.com/answers/ # Answers — Avid Problem Solvers > Complete answers to specific operations questions — bottlenecks, Lean Six Sigma with AI, sector operations and buying consulting — from Avid Problem Solvers. Canonical: https://avidproblemsolvers.com/answers/ ## Operations Diagnosis ### [How to find an operational bottleneck you cannot measure](https://avidproblemsolvers.com/answers/unmeasured-bottleneck) An unmeasured bottleneck is a constraint that does not appear in any existing report, usually because the process step it sits in was never instrumented. The standard way to find one is to compare theoretical cycle time against observed cycle time, then attribute the gap step by step until it concentrates somewhere. ### [Known vs unknown operational problems: how to tell them apart](https://avidproblemsolvers.com/answers/known-vs-unknown) A known operational problem already has a number attached — a backlog, a cycle time, an error rate — and the task is finding its root cause. An unknown problem has no measurement yet and shows up as a feeling that something is wrong. Telling them apart decides whether the first step is analysis or instrumentation. ## Lean Six Sigma In An Ai Era ### [Does Lean Six Sigma still work when AI handles the data analysis](https://avidproblemsolvers.com/answers/lean-six-sigma-ai) Lean Six Sigma still works when AI handles the data analysis, because its value was never the statistics alone. It supplies the problem definition, the process map, the root-cause discipline and the control plan that make an analysis worth acting on. AI accelerates the measure and analyse steps; it does not replace deciding what to fix. ## Sector ### [Operational bottlenecks in BFSI: where they usually sit](https://avidproblemsolvers.com/answers/bfsi-bottlenecks) In banking, financial services and insurance, operational bottlenecks usually sit at control points rather than in core processing — onboarding and KYC checks, manual exception queues, maker-checker approvals, reconciliations and claims assessment. They persist because each control exists for a regulatory reason, so the fix is redesigning how the control runs, not removing it. ## Buying Consulting ### [How to prepare for a consulting session so it is not wasted](https://avidproblemsolvers.com/answers/prepare-for-consultation) To prepare for a consulting session, write the problem in one sentence with a number in it, gather the one or two figures you trust, list what has already been tried, and decide what outcome would make the session worthwhile. Twenty minutes of preparation usually moves the conversation straight past introductions and into the problem. ### [What a one-hour operations consultation can realistically deliver](https://avidproblemsolvers.com/answers/one-hour-consultation) A one-hour operations consultation can realistically deliver a sharper problem statement, a working hypothesis about where the cause sits, and the first three concrete actions to take. It cannot deliver a full diagnosis, an implementation or a validated solution. Its value is in redirecting the next few weeks of effort before they are spent. --- Source: https://avidproblemsolvers.com/answers/unmeasured-bottleneck # How to find an operational bottleneck you cannot measure > An unmeasured bottleneck is a constraint that does not appear in any existing report, usually because the process step it sits in was never instrumented. The standard way to find one is to compare theoretical cycle time against observed cycle time, then attribute the gap step by step until it concentrates somewhere. Question: How do I find a bottleneck that doesn't show up in any report? Canonical: https://avidproblemsolvers.com/answers/unmeasured-bottleneck Updated: 2026-10-18 Publisher: Avid Problem Solvers (https://avidproblemsolvers.com/) ## Why doesn't the bottleneck show up in any report? Reports measure what someone decided to measure, usually when the process was first designed or when a previous problem forced a metric into existence. The steps that get instrumented are the ones with a system behind them: a ticketing tool, a core banking platform, a warehouse management system. The steps that do not get instrumented are the ones between systems — a hand-off by email, a spreadsheet check, an approval that lives in someone's inbox. Constraints migrate toward those gaps. Once the measured steps have been tuned, the slowest remaining step is very often one that nobody can see. The dashboard is green, the end-to-end number is still bad, and the explanation is in the part of the process that produces no data. ## What is the gap between theoretical and observed cycle time? **Theoretical cycle time** is how long one unit of work would take if it never waited: the sum of the hands-on time at every step. **Observed cycle time** is how long it actually takes from start to finish, measured from timestamps you already have — the moment a request arrives and the moment it is closed. The difference is waiting. In most service and back-office processes, work spends far more time waiting than being worked on. That gap is the bottleneck's footprint. You do not need to know where the bottleneck is to measure the gap; you only need the two ends of the process and an honest estimate of the hands-on effort in between. If the gap is small, the problem is capacity or effort at the steps themselves. If the gap is large, the problem is flow — and somewhere work is queuing. ## How do you attribute the gap to a specific step? Work through the process in segments, not all at once: 1. **Split the process at every point where a timestamp already exists.** System logs, email headers, status changes and file modification times are all timestamps. Even three or four split points turn one opaque number into several smaller ones. 2. **Compare each segment's observed time with its hands-on time.** The segment with the largest unexplained wait is where to look next. 3. **Sample the dark segment by hand.** Take twenty or thirty recent items and trace them manually through that segment: who touched them, when, and what they waited for. This is slow, unglamorous work, and it is usually where the answer is. 4. **Look for queues, batches and loops.** Work piling up before a single approver, work released once a day instead of continuously, and work sent back for correction are the three most common shapes of a hidden constraint. The point of the exercise is not a perfect map. It is to concentrate the unexplained time into one place small enough to observe directly. ## What are the usual hiding places? Hidden bottlenecks tend to sit in the same few places across industries: - **Hand-offs between teams**, where ownership of the work is briefly nobody's. - **Approval steps**, especially ones that depend on a single person's availability. - **Rework loops**, where items fail a check and return to an earlier step without being counted as new work. - **Batching**, where work waits for a scheduled run, a weekly meeting or a full queue before it moves. - **Exceptions**, where a small fraction of items leave the standard path and consume a disproportionate share of skilled time. A rework loop is the hardest of these to see, because the volume metric counts each item once while the team handles it two or three times. If capacity feels too low for the volume on the report, check how many times each item really passes through the system. ## When does AI help, and when is it premature? Pattern detection is useful once there is data to find patterns in. If system logs already capture status changes, an analysis across thousands of items can surface which paths are slowest and which item characteristics predict delay — faster than manual review would. That is covered in more depth in [Does Lean Six Sigma still work when AI handles the data analysis](/answers/lean-six-sigma-ai). If the dark segment produces no data at all, AI has nothing to work with. The first move is to make the step measurable — even a manual log for two weeks — and only then analyse it. ## What should you do first? Measure the two ends of the process, estimate the hands-on time honestly, and calculate the gap. If the gap is large, you have confirmed that the bottleneck is a flow problem and that it lives somewhere you are not looking. Whether you already know which kind of problem you have is the subject of [Known vs unknown operational problems](/answers/known-vs-unknown); in regulated operations, the usual locations are mapped in [Operational bottlenecks in BFSI](/answers/bfsi-bottlenecks). ## Related answers - [Known vs unknown operational problems: how to tell them apart](https://avidproblemsolvers.com/answers/known-vs-unknown) - [Does Lean Six Sigma still work when AI handles the data analysis](https://avidproblemsolvers.com/answers/lean-six-sigma-ai) - [Operational bottlenecks in BFSI: where they usually sit](https://avidproblemsolvers.com/answers/bfsi-bottlenecks) --- If this is the shape of your problem, one hour is usually enough to find the first move. One hour, $1,199 flat: https://avidproblemsolvers.com/#book --- Source: https://avidproblemsolvers.com/answers/known-vs-unknown # Known vs unknown operational problems: how to tell them apart > A known operational problem already has a number attached — a backlog, a cycle time, an error rate — and the task is finding its root cause. An unknown problem has no measurement yet and shows up as a feeling that something is wrong. Telling them apart decides whether the first step is analysis or instrumentation. Question: How do I tell whether an operational problem is known or unknown? Canonical: https://avidproblemsolvers.com/answers/known-vs-unknown Updated: 2026-10-18 Publisher: Avid Problem Solvers (https://avidproblemsolvers.com/) ## What makes a problem "known"? A problem is known when it can be stated with a number and the number is trusted. "Claims take eleven days to settle." "Our dispatch backlog is four hundred orders every Monday." "First-contact resolution dropped to sixty percent." Somebody named it, it is probably on a dashboard, and people argue about why it has not moved rather than about whether it exists. Known problems are not easy problems. They are often the ones that have survived several attempts to fix them. But they have a clear starting point: the metric exists, so the work is to find where in the process the variation enters and remove the cause. ## What makes a problem "unknown"? An unknown problem has no measurement attached. It appears as a symptom without a figure: margins that feel thinner than they should, a team that seems busy all the time without output rising, customers who leave without saying why. People describe it with words like *feels*, *seems* and *somehow*. Unknown problems are frequently the more expensive ones, because nobody owns them. There is no metric to assign, so there is no target, so there is no project. They persist precisely because they cannot be pointed at. ## Is there a middle case? Yes, and it is the most common shape in mature operations: **a known symptom with an unknown cause**. The effect is measured precisely — churn is up, cost per transaction has risen — but none of the existing data explains it. The dashboard is right and the diagnosis is missing. This case is easy to misread as a known problem, because there is a number. The test is whether the available data can actually explain the number. If every breakdown of the metric leaves the change unexplained, the cause sits outside what is measured, and the work looks more like an unknown problem than a known one. ## How do you tell which one you have? Write the problem down in one sentence and examine the sentence: - **Does it contain a measurable quantity?** A count, a duration, a rate, a cost or a percentage. - **Does it contain uncertainty language?** "We can't tell why", "nobody knows", "it seems", "it feels". A sentence with a number and no uncertainty describes a known problem. A sentence with uncertainty and no number describes an unknown one. A sentence with both describes a known symptom with an unknown cause. A sentence with neither — "our operations need improving" — is not yet a problem statement at all; it cannot be tested, and the first move is to turn it into something with a number attached. This is the same reading the problem probe on the [Avid Problem Solvers home page](/) applies to whatever you type into it. ## Why does the distinction matter? Because the first step is different for each, and starting with the wrong one wastes weeks. For a **known problem**, the first step is analysis: segment the metric, map the process, and isolate where the variation enters. Lean Six Sigma's root-cause tools were built for exactly this situation. For an **unknown problem**, the first step is instrumentation: decide what quantity would prove the problem exists, and start measuring it. Analysis before measurement produces opinions, not findings. The practical method for this is described in [How to find an operational bottleneck you cannot measure](/answers/unmeasured-bottleneck). For a **known symptom with an unknown cause**, the first step is to widen the data: bring in sources the original metric never used — free-text notes, system logs, timing data from adjacent processes — and look for what correlates with the change. This is where AI-assisted pattern detection earns its place, as discussed in [Does Lean Six Sigma still work when AI handles the data analysis](/answers/lean-six-sigma-ai). ## What goes wrong when the type is misread? Treating an unknown problem as known produces a confident project aimed at the wrong metric. Teams optimise the number they have, it improves, and the underlying problem is untouched. Treating a known problem as unknown produces endless discovery. The number was there all along; what was missing was the discipline to trace it. Treating a known symptom as a fully known problem is the most expensive mistake, because the existing data has already been exhausted. Re-analysing it more carefully will produce the same unexplained result. ## What should you bring to a first conversation? Bring the one-sentence statement and whatever number you have, even if you distrust it. Saying which of the four shapes you think it is — and why — will shorten the diagnosis considerably. [How to prepare for a consulting session so it is not wasted](/answers/prepare-for-consultation) covers the rest. ## Related answers - [How to find an operational bottleneck you cannot measure](https://avidproblemsolvers.com/answers/unmeasured-bottleneck) - [How to prepare for a consulting session so it is not wasted](https://avidproblemsolvers.com/answers/prepare-for-consultation) - [Does Lean Six Sigma still work when AI handles the data analysis](https://avidproblemsolvers.com/answers/lean-six-sigma-ai) --- If this is the shape of your problem, one hour is usually enough to find the first move. One hour, $1,199 flat: https://avidproblemsolvers.com/#book --- Source: https://avidproblemsolvers.com/answers/lean-six-sigma-ai # Does Lean Six Sigma still work when AI handles the data analysis > Lean Six Sigma still works when AI handles the data analysis, because its value was never the statistics alone. It supplies the problem definition, the process map, the root-cause discipline and the control plan that make an analysis worth acting on. AI accelerates the measure and analyse steps; it does not replace deciding what to fix. Question: Is Lean Six Sigma still relevant now that AI can analyse operational data? Canonical: https://avidproblemsolvers.com/answers/lean-six-sigma-ai Updated: 2026-10-18 Publisher: Avid Problem Solvers (https://avidproblemsolvers.com/) ## What part of Lean Six Sigma does AI actually change? Lean Six Sigma projects traditionally spend a large share of their time in two places: collecting data and analysing it. Pulling extracts from several systems, cleaning them, segmenting a metric a dozen ways and testing which factors correlate with defects used to take weeks of analyst time. AI tools compress that work dramatically. Given access to transaction logs, ticket histories or free-text notes, they can surface which segments behave differently, which combinations of attributes predict delay or error, and which records look anomalous — across volumes no person would review by hand. For the measure and analyse phases of DMAIC, that is a real change in speed and reach. ## What part does it not change? Everything on either side of the analysis. **Define** is still a human judgement: which problem is worth solving, what "better" means, and whose outcome counts. An AI model will analyse whatever metric it is given. If the metric is the wrong one, the analysis is fast and irrelevant. **Improve** is still a change to how people and systems work. A finding that approvals over a certain value stall for three days does not remove the stall; redesigning the approval path does, and that involves policy, ownership and negotiation. **Control** is still about making the gain stick: standard work, ownership of the metric, and a response plan when it drifts. Without it, processes revert, whether the original insight came from a statistician or a model. ## Why does root-cause discipline matter more with AI, not less? AI is very good at finding correlations and indifferent to whether they are causes. Across a large enough dataset it will always find patterns, and some of them will be coincidences, artefacts of how the data was recorded, or effects mistaken for causes. Lean Six Sigma's habit of asking *why* repeatedly, verifying causes against the process as it actually runs, and testing a fix before rolling it out is the safeguard against acting on a spurious pattern. The faster the analysis, the more candidate causes it produces, and the more that verification step matters. There is a second, subtler risk: models trained on historical data learn the process as it was, including its workarounds. A model can tell you which claims will be slow; it cannot tell you that they are slow because of a rule nobody remembers introducing. Walking the process — the *gemba* in Lean terms — is still where that kind of cause is found. ## Where does AI genuinely outperform manual analysis? Three situations stand out: - **Unstructured data.** Free-text case notes, emails and call transcripts often hold the explanation for a metric, and were effectively unanalysable at scale before. - **High-dimensional problems.** When delay depends on a combination of product, channel, region and time of day, a model can find interactions a person testing one factor at a time would miss. - **Continuous monitoring.** Instead of a one-off analysis, a model can watch for the early signature of a known failure and flag it before the metric moves. These are most valuable for the "known symptom, unknown cause" shape described in [Known vs unknown operational problems](/answers/known-vs-unknown) — where the effect is measured but the existing breakdowns cannot explain it. ## When is neither the right tool? When there is no data. AI cannot analyse a process step that produces no records, and Lean Six Sigma's statistical tools have nothing to work with either. The first move is to make the step measurable, which is the subject of [How to find an operational bottleneck you cannot measure](/answers/unmeasured-bottleneck). And when the problem is one of high uncertainty and fast change — a new product, a new market, an operating model that does not exist yet — iterative Agile delivery with short feedback loops usually beats a structured improvement project, whatever is doing the analysis. ## So how should the methods be combined? Use Lean Six Sigma for the structure: a sharp problem statement, a map of the process as it really runs, verified causes and a control plan. Use AI inside the measure and analyse steps, where it removes the slowest and most expensive work and reaches data that was previously out of range. Use Agile where certainty is low and the answer has to be discovered by trying things. Methodology is a means, not a mission. The right combination is the one the problem calls for, not the one a team happens to be trained in. ## Related answers - [How to find an operational bottleneck you cannot measure](https://avidproblemsolvers.com/answers/unmeasured-bottleneck) - [Known vs unknown operational problems: how to tell them apart](https://avidproblemsolvers.com/answers/known-vs-unknown) --- If this is the shape of your problem, one hour is usually enough to find the first move. One hour, $1,199 flat: https://avidproblemsolvers.com/#book --- Source: https://avidproblemsolvers.com/answers/bfsi-bottlenecks # Operational bottlenecks in BFSI: where they usually sit > In banking, financial services and insurance, operational bottlenecks usually sit at control points rather than in core processing — onboarding and KYC checks, manual exception queues, maker-checker approvals, reconciliations and claims assessment. They persist because each control exists for a regulatory reason, so the fix is redesigning how the control runs, not removing it. Question: Where do operational bottlenecks usually sit in banking, financial services and insurance? Canonical: https://avidproblemsolvers.com/answers/bfsi-bottlenecks Updated: 2026-10-18 Publisher: Avid Problem Solvers (https://avidproblemsolvers.com/) ## Why are BFSI bottlenecks different from other industries? In most industries, a slow step can be simplified or removed once it is found. In banking, financial services and insurance, many slow steps are controls: they exist because a regulator, an auditor or a past loss required them. Removing them is not an option, and the people who run them are rightly cautious about any change. That makes BFSI bottlenecks unusually durable. Core processing is typically automated and fast. The constraint sits around it — in the checks, reviews and exceptions that surround each transaction — and it is protected by the legitimate argument that the check matters. The cost of getting it wrong is measured in fines, not friction. ## Where do they usually sit? Across banks, lenders, insurers and payment businesses, the same few locations recur: - **Customer onboarding and KYC.** Document collection, verification and risk scoring are often split across teams and systems, with incomplete applications cycling back to the customer several times. - **Transaction monitoring and AML alert review.** Screening rules tuned conservatively generate large volumes of alerts, most of which are cleared as false positives after manual investigation. The queue grows faster than the team. - **Maker-checker approvals.** Dual control is essential, but when every item of every value needs the same senior checker, the checker becomes the constraint. - **Reconciliations and breaks.** Mismatches between systems are resolved manually, often in spreadsheets, and the unresolved backlog ages quietly. - **Claims assessment.** In insurance, the tension between cycle time and accuracy concentrates in assessment and the requests for additional information that pause a claim. - **Credit decisioning exceptions.** Applications that fall outside automated rules land in a manual underwriting queue that was sized for a fraction of the volume it now receives. ## Why do they not show up in reports? Because the reports measure the controls' outcomes, not their flow. A KYC team reports how many applications it approved; it rarely reports how long applications waited between steps, or how many times the same application was touched. An alert team reports alerts closed, not the share of investigator time spent on alerts that were never going to be suspicious. These are classic unmeasured bottlenecks — visible only as an end-to-end time that is too long, or a team that is always at capacity. The general method for finding them is in [How to find an operational bottleneck you cannot measure](/answers/unmeasured-bottleneck). ## How do you fix a bottleneck without weakening the control? The principle is to redesign how the control runs, not whether it runs. In practice that tends to mean: - **Risk-tiering the control.** Applying the full check to high-risk items and a lighter, still-compliant path to low-risk ones, with the tiering logic itself documented and approved. - **Fixing inputs upstream.** Many exceptions are created by incomplete or inconsistent data at the start of the process. Validating at capture removes work from every downstream control. - **Removing rework loops.** Asking for everything needed once, instead of discovering missing items one at a time. - **Separating the queue.** Routing simple items away from the senior reviewers so that their capacity goes where judgement is genuinely required. - **Tuning rules with evidence.** Using historical outcomes to adjust screening thresholds so fewer obviously benign alerts are generated, without raising the risk of missing a true positive. Every one of these changes needs to be evidenced for the people who own the risk. That is why root-cause discipline matters so much in this sector: a change has to come with proof that it does not weaken the control. ## Where does AI help in BFSI operations? AI is most useful where the volume of manual review is high and the outcomes are recorded: alert triage, document classification, claims triage, and prioritising exception queues. It can rank which items need a person and which do not, and explain the factors behind the ranking. The caveat is governance. Regulators expect explainability, monitoring and human accountability for automated decisions, so a model has to be introduced as a controlled change in its own right. More on the balance between the two is in [Does Lean Six Sigma still work when AI handles the data analysis](/answers/lean-six-sigma-ai). ## What is a sensible first step? Pick the one control whose queue is always full, and measure the time items spend waiting in front of it compared with the time spent working on them. If the waiting time dominates, the control is a flow problem, not a capacity problem — and flow problems are usually cheaper to fix than adding headcount. Whether one hour is enough to locate it is covered in [What a one-hour operations consultation can realistically deliver](/answers/one-hour-consultation). ## Related answers - [How to find an operational bottleneck you cannot measure](https://avidproblemsolvers.com/answers/unmeasured-bottleneck) - [Does Lean Six Sigma still work when AI handles the data analysis](https://avidproblemsolvers.com/answers/lean-six-sigma-ai) - [What a one-hour operations consultation can realistically deliver](https://avidproblemsolvers.com/answers/one-hour-consultation) --- If this is the shape of your problem, one hour is usually enough to find the first move. One hour, $1,199 flat: https://avidproblemsolvers.com/#book --- Source: https://avidproblemsolvers.com/answers/prepare-for-consultation # How to prepare for a consulting session so it is not wasted > To prepare for a consulting session, write the problem in one sentence with a number in it, gather the one or two figures you trust, list what has already been tried, and decide what outcome would make the session worthwhile. Twenty minutes of preparation usually moves the conversation straight past introductions and into the problem. Question: How should I prepare for a consulting session so the time is not wasted? Canonical: https://avidproblemsolvers.com/answers/prepare-for-consultation Updated: 2026-10-18 Publisher: Avid Problem Solvers (https://avidproblemsolvers.com/) ## Why does preparation matter so much? In a short session, the first fifteen to twenty minutes usually go on context: what the business does, how the process works, what the problem looks like. That time is necessary, but most of it can be done before the conversation starts. Every minute of context written down in advance is a minute available for diagnosis during the session. Preparation also changes the quality of the conversation. Writing the problem down forces a level of precision that speaking about it does not, and it often exposes assumptions that are worth questioning. ## How should you state the problem? In one sentence, with a number in it if at all possible. "Average onboarding time for business accounts has risen from six days to fourteen over the last two quarters" gives a consultant something to work with immediately. "Onboarding is too slow" does not. If you cannot attach a number, say so explicitly, and describe how the problem shows up instead: who notices it, when, and what they do about it. That tells the other person you may be dealing with an unknown problem rather than a known one, which changes where the session should start. The distinction is explained in [Known vs unknown operational problems](/answers/known-vs-unknown). ## Which numbers should you bring? Not a data dump. Bring the one or two figures you trust most, and be honest about the ones you do not: - **The headline metric** that describes the problem, with its trend over time. - **Volume**, so the scale is clear: items per day, cases per month, transactions per hour. - **The two ends of the process** if you have them: when work arrives and when it is finished. - **Any figure you distrust**, with a sentence on why. Distrusted data is often a clue in itself. If the process has a stretch with no data at all, mention it. That gap is frequently where the answer is, as described in [How to find an operational bottleneck you cannot measure](/answers/unmeasured-bottleneck). ## What have you already tried? List previous attempts and what happened. This prevents the session from rediscovering ideas that have already failed, and the pattern of failed fixes is diagnostic: if adding people did not reduce the backlog, the constraint is probably not capacity; if a new system did not shorten the cycle time, the delay probably sits outside the system. Include partial successes too. A fix that worked for a month and then faded points toward a missing control rather than a wrong diagnosis. ## Who should be in the room? The person who owns the problem and can act on the outcome the following week. A session attended only by someone who will report back to the real owner loses a great deal in translation. If the problem crosses teams, one person from each side of the main hand-off is worth more than several people from the same team. ## What outcome should you aim for? Decide in advance what would make the session worthwhile. Realistic targets for a single session are a sharper problem statement, a working hypothesis about the cause, and a short list of first actions — not a finished solution. Knowing that going in keeps the conversation focused. [What a one-hour operations consultation can realistically deliver](/answers/one-hour-consultation) sets out the limits honestly. ## What should you avoid? - **Sending a long deck instead of a short brief.** Thirty slides of background usually hide the one paragraph that matters. - **Pre-deciding the solution.** If the question is "how do we implement tool X", the session cannot test whether tool X addresses the cause. - **Leaving out the uncomfortable part.** Problems that would not be discussed in a board meeting are often the real constraint. Confidentiality is the point of an outside conversation. ## What does a good brief look like? Half a page is enough: the one-sentence problem, the key numbers with their trends, what has been tried, who owns it, and what you want to leave with. Written that way, a session can start at the problem rather than at the introductions. ## Related answers - [What a one-hour operations consultation can realistically deliver](https://avidproblemsolvers.com/answers/one-hour-consultation) - [Known vs unknown operational problems: how to tell them apart](https://avidproblemsolvers.com/answers/known-vs-unknown) - [How to find an operational bottleneck you cannot measure](https://avidproblemsolvers.com/answers/unmeasured-bottleneck) --- If this is the shape of your problem, one hour is usually enough to find the first move. One hour, $1,199 flat: https://avidproblemsolvers.com/#book --- Source: https://avidproblemsolvers.com/answers/one-hour-consultation # What a one-hour operations consultation can realistically deliver > A one-hour operations consultation can realistically deliver a sharper problem statement, a working hypothesis about where the cause sits, and the first three concrete actions to take. It cannot deliver a full diagnosis, an implementation or a validated solution. Its value is in redirecting the next few weeks of effort before they are spent. Question: What can I actually get out of a one-hour consultation on an operations problem? Canonical: https://avidproblemsolvers.com/answers/one-hour-consultation Updated: 2026-10-18 Publisher: Avid Problem Solvers (https://avidproblemsolvers.com/) ## What can one hour actually achieve? An hour is enough time for an experienced operator to do three things well. **Sharpen the problem.** Most problems arrive described by their symptoms: costs are up, a backlog will not clear, a team is always stretched. Within the first twenty minutes, a good conversation turns that into a statement with a measurable quantity and a boundary — what is in scope, what is not, and what number would prove it is fixed. **Form a hypothesis about the cause.** Someone who has seen the same shape of problem in other organisations can usually narrow the likely locations quickly: a hand-off, an approval, a rework loop, a batching rule. The hypothesis may turn out to be wrong, but it gives the investigation a direction instead of a survey. **Leave with the first three things to do on Monday.** Concrete, small actions: a measurement to start, a sample of cases to trace by hand, a conversation to have with the owner of a specific step. These are the moves that test the hypothesis cheaply before anyone commits budget. ## What can it not deliver? It cannot deliver a full diagnosis. Finding a root cause usually requires data and observation that take days or weeks to gather, and nobody should claim a verified cause in an hour. It cannot deliver implementation. Changing a process means changing how people work, which needs ownership inside the organisation, time and follow-through. It cannot deliver a guarantee. An hour produces a direction and a plan for testing it, not a validated solution. Any consultant who promises more than that from a single conversation is either guessing or setting up the sale of the next engagement. ## Why is an hour often the right size? Because the most expensive mistake in operations improvement is spending months on the wrong problem. Teams commit to a project, a system purchase or a restructure based on the first plausible explanation, and discover late that the constraint was somewhere else. An hour with someone who has solved the problem before is a cheap check against that mistake. It is long enough to reframe the problem and short enough that there is no incentive to pad it. It also removes the friction of traditional consulting. There is no proposal to wait for, no discovery phase to fund, and no retainer to justify. You bring the problem; you leave with a path. ## What kinds of problems suit a one-hour session best? Sessions are most productive when: - **The problem is specific.** "Our claims cycle time doubled after we changed systems" is a better starting point than "our operations need modernising". - **Someone in the room owns it.** The person who will act on Monday should be the person in the conversation. - **There is at least one number.** Even a rough or distrusted figure gives the conversation something to test against. - **The problem has resisted the obvious fixes.** If the first two ideas have already been tried, an outside view is at its most valuable. Problems without a number are still welcome, but the first action will usually be to make them measurable. [Known vs unknown operational problems](/answers/known-vs-unknown) explains why that step comes first. ## What happens after the hour? That depends on what you find. Often the three actions are enough: the team runs them, the hypothesis is confirmed or rejected, and the fix follows inside the organisation. Sometimes the problem turns out to be large enough to need structured work over weeks. Either way, the decision about what comes next is made with far better information than before the conversation. ## How do you make sure the hour is not wasted? Preparation makes more difference than anything said during the session. A short brief, the one number you trust, and a clear statement of what you have already tried will move the first twenty minutes forward considerably. The practical checklist is in [How to prepare for a consulting session so it is not wasted](/answers/prepare-for-consultation). For a sense of how specific the conversation can get in a regulated environment, see [Operational bottlenecks in BFSI: where they usually sit](/answers/bfsi-bottlenecks). ## Related answers - [How to prepare for a consulting session so it is not wasted](https://avidproblemsolvers.com/answers/prepare-for-consultation) - [Known vs unknown operational problems: how to tell them apart](https://avidproblemsolvers.com/answers/known-vs-unknown) - [Operational bottlenecks in BFSI: where they usually sit](https://avidproblemsolvers.com/answers/bfsi-bottlenecks) --- If this is the shape of your problem, one hour is usually enough to find the first move. One hour, $1,199 flat: https://avidproblemsolvers.com/#book --- Source: https://avidproblemsolvers.com/privacy # Privacy Canonical: https://avidproblemsolvers.com/privacy Updated: 2026-10-21 Avid Problem Solvers collects the minimum needed to prepare for and deliver a consultation. This page lists all of it. ## What we collect - **The problem probe.** If you describe a problem on the home page and press "Read it", what you wrote is stored so we can prepare for a possible conversation: the problem, and the two optional lines about your business and what "fixed" looks like if you filled them in. Nothing is sent while you type — only when you deliberately submit. No name or contact detail is attached to it. - **Booking details.** When you book, we store your name, email address, company if you give it, the time slot you chose, and the brief you send before the session. - **Payment status.** Payment is processed by Razorpay. We receive confirmation that a payment succeeded and its reference number. We never see or store your card or bank details. - **Page views.** We count which pages are viewed and the country a visit comes from. The country is resolved on our server and the IP address is discarded immediately — we never store IP addresses. ## What we do not do - We do not use advertising trackers, third-party analytics or fingerprinting. - We do not sell, rent or share your information for marketing. - We do not name clients, on this site or anywhere else. ## Who processes data for us - **Razorpay** — payment processing. - **Supabase** — database hosting for bookings and briefs. - **Google** — the calendar invitation and Google Meet link for your session. - **Resend** — transactional email such as your booking confirmation. - **Cloudflare** — hosting and delivery of this website. ## Retention and your rights Booking records and briefs are kept for as long as needed to deliver the consultation and meet accounting obligations. You can ask us to show you, correct or delete what we hold about you by replying to any email we have sent you. All times are stored in UTC and shown in your local time zone. --- Source: https://avidproblemsolvers.com/terms # Terms Canonical: https://avidproblemsolvers.com/terms Updated: 2026-10-22 These terms cover the single service Avid Problem Solvers offers on this website: a one-hour operations consultation. ## The service - One 60-minute consultation over Google Meet, at a flat fee of $1,199. - No retainer, no proposal process and no preliminary discovery call. - The consultation is advisory. You decide what to implement and remain responsible for decisions made in your business. ## Payment and booking - Payment is taken in full, through Razorpay, before you choose a time slot. - A booking is confirmed only when Razorpay confirms the payment to us — not when the browser shows a success screen. - After payment you choose a slot from the available times and receive a calendar invitation with the meeting link. ## The brief Before the session we ask you to send a short brief describing the problem. The more specific it is, the more of the hour goes into the problem itself. ## Confidentiality What you tell us in the brief and during the session is treated as confidential. We do not name clients and do not reuse your information in published material in any identifiable form. ## Cancellation and refunds Rescheduling, cancellation and refunds are covered in one place: [Cancellation & Refunds](/refunds). ## Contact Reply to any email we have sent you, and a person will answer. --- Source: https://avidproblemsolvers.com/refunds # Cancellation & Refunds Canonical: https://avidproblemsolvers.com/refunds Updated: 2026-10-22 This is the complete cancellation and refund policy for the one service Avid Problem Solvers sells: a one-hour operations consultation. ## The price - The hour is $1,199, paid in full at booking. - One hour, one price, no retainer. ## Rescheduling - Reschedule free of charge up to 24 hours before the session, as often as you need. ## Cancelling - Cancel more than 48 hours before the session: full refund, returned to the original payment method within 5–7 working days. - Cancel within 48 hours, or don't attend: no refund. The hour was held and the preparation was already done. ## If we cancel - If we cancel or reschedule for any reason, you choose: a full refund, or a new slot at no cost. ## How refunds are paid - Refunds are processed through Razorpay to the original payment method. - We never hold or see your card details. ## Questions Email [aps@avidproblemsolvers.com](mailto:aps@avidproblemsolvers.com).