Bring the prompt, the criteria, and whatever you know about your organization's systems estate, and a premium original doctoral sample returns inside 24 to 48 hours, written to the Distinguished descriptors with the investment case computed rather than described. On a transcript the course reads DB-FPX8720, Strategic Digital Transformation, worth 2 program points, a specialization course in Strategy and Innovation that is also available as a General Management choice, taught in FlexPath within the 45-point Doctor of Business Administration.
What DB-FPX8720 actually grades
The distinction this course is built on is the one between buying technology and changing an operating model, and the criteria test it in almost every deliverable. A paper that recommends a platform, lists its features, and predicts efficiency has described a purchase. A doctoral paper says which decisions move, which roles change, how work is funded and prioritized afterwards, what data the new arrangement depends on, and who owns the outcome once the implementation team disbands. The assessments in this course usually ask you to build or evaluate a transformation case, and your scoring guide decides how much of the operating model you have to specify.
Economics is graded closely here because digital proposals attract vague benefits. Faster, better, and more agile are not benefits; a lower cost per transaction, a shorter cycle time, a higher conversion rate, or a smaller error rate are benefits, and each of them has a denominator you have to state. Doctoral treatment separates the cost to build from the cost to run, notices that the run cost usually rises with a cloud consumption model even when the capital cost falls, and puts a horizon on the return rather than implying it arrives at go-live. Where a benefit resists measurement, the strong move is to say so explicitly and to give the decision-maker the operational consequence of doing nothing instead.
The third strand is the constraint set, and skipping it is the fastest route to a weak paper. Legacy estates carry technical debt that dictates sequence, since a customer-facing rebuild sitting on a system of record nobody will touch inherits every limitation of that system. Data governance decides whether the analytics ambition is legal and whether the numbers can be trusted, so lineage, ownership, quality, and retention belong in the design rather than in an appendix. Privacy and security obligations shape the architecture, and workforce capability decides whether the organization can run what it just bought or has quietly signed a long-term dependency on a vendor. A transformation case that discusses none of these has assumed the difficult parts away.
How we help in this course
Our 8720 drafts write the operating model before the technology. The decisions that move, the funding mechanism, the team shape, the data ownership, and the capability build all get specified, and only then does the paper discuss what is being implemented. The financial case is built as a model rather than a claim, with build and run separated, the benefit expressed per transaction or per cycle, and a payback and a discounted return stated. Give us the process you are targeting, your volumes, and the criteria, and the sample argues with your organization's numbers.
Terms are the studio's usual ones with an arithmetic guarantee attached. Work comes back within 24 to 48 hours built to the Distinguished column, and every cost model we build is recalculated independently by a second reader before delivery, including the discounting, because a transformation case with an internally inconsistent benefit line invites the reader to distrust the whole argument. Further rounds are included until the guide is met, and whatever your faculty returns is reworked without an additional charge. Submit-on-grade means the calendar is yours, so tell us the date you actually intend to submit and we will work to it rather than to a default.
The assessments, one by one
Assessment 1
Assessment 1 of Strategic Digital Transformation usually asks for the case and the baseline: why this change now, stated as an operating problem, and a current state measured in volumes, unit costs, cycle times, error rates, and the systems underneath them. Read the full Assessment 1 manual.
Assessment 2
Assessment 2 of Strategic Digital Transformation usually asks for the design and the money: the target operating model stated as decisions that move and roles that change, and an investment case with build and run separated, benefits expressed per unit of volume, and a discounted return rather than an implied one. Read the full Assessment 2 manual.
Assessment 3
Assessment 3 of Strategic Digital Transformation usually asks for the part that decides whether any of the benefit arrives: a release sequence justified by what the legacy estate can support, the capability and vendor position, the privacy and security obligations, and a governance design with a. Read the full Assessment 3 manual.
How to actually write DB-FPX8720: where to begin
Put the scoring guide beside you and state the problem as an operating problem before you name a single technology. Criteria become headings, and in a transformation course the clusters usually run from the strategic rationale, through the current state of process and systems, to the target operating model, the investment case, the implementation and risk plan, and the governance that will hold it. Keep the technology out of the first two sections entirely, because a paper that names a vendor before it has established what work needs to change has inverted the argument the criteria are reading for.
Then compute the case rather than describing it. Suppose the process you are targeting handles 2.4 million transactions a year at a fully loaded $1.85 each, which is $4.44 million. Your design automates 62 percent of them at $0.21 each, so 1,488,000 transactions cost $312,480, the remaining 912,000 still cost $1.85 each for $1,687,200, and the new total is $1,999,680, a saving of about $2.44 million a year. Build cost is $3.1 million and the platform adds $480,000 a year to run, so the net annual benefit is roughly $1.96 million and simple payback is $3.1 million divided by $1.96 million, or about 1.6 years. Discount the five-year benefit stream at 10 percent, which gives an annuity factor of 3.79, and the present value of the benefits is about $7.43 million against the $3.1 million build, for a net present value near $4.33 million. Now state the assumption that would break it, which is almost always the automation rate rather than the unit cost.
Then write the parts of the plan that determine whether any of that happens. Sequence the work against the legacy estate so that each release can actually run on what exists underneath it, and name the system of record that constrains the order. Specify the data foundation the benefits depend on, which means who owns each critical field, how quality will be measured, and what the retention rule is, because an analytics benefit built on data nobody owns will not be realized. Say how the capability gets built internally and what the organization would do if the vendor relationship ended, since a plan with no exit is a plan with a hidden cost. Then set the governance: who decides on scope changes, what the funding cadence is, and what evidence at which checkpoint would justify stopping. A named stopping condition in a transformation paper reads as senior judgment because most authors cannot bring themselves to write one.
| Section | What goes in it | What Distinguished looks like |
|---|---|---|
| Strategic rationale | The competitive or operating problem, the cost of the current state, and why now. | A rationale tied to a measured performance gap rather than to sector-wide digital pressure. |
| Current state | Process volumes, handling costs, cycle times, error rates, and the systems and data underneath them. | Volumes and unit costs stated with their sources, and technical debt described in terms of what it prevents. |
| Target operating model | The decisions that move, the roles that change, the funding and prioritization mechanism, and data ownership. | Accountability after the programme ends made explicit, including who owns the outcome and the data. |
| Investment case | Build cost, run cost, benefit per transaction or cycle, payback, and a discounted return. | Build and run separated, benefits expressed per unit of volume, and the discounting shown rather than implied. |
| Delivery and risk | Release sequence against the legacy estate, capability build, vendor dependency, privacy and security obligations. | A sequence justified by system constraints, with an exit position for the critical vendor relationship. |
| Governance and measurement | Decision rights over scope, checkpoints, benefit tracking, and the stopping condition. Current APA both ways. | Benefits tracked in reports the organization already produces, with a stated threshold for stopping. |
Developing the analysis
The evidence base on digital transformation is uneven, and knowing where it is thin protects you. Large reported failure rates for transformation programmes circulate widely and mostly originate in consulting surveys with undisclosed samples and self-reported success criteria, so cite them as practitioner evidence with the publisher named or leave them out, because a doctoral reader will ask what counted as failure. The scholarly literature is stronger on some questions than others: the productivity paradox research established that returns from information technology investment appear with a lag and depend on complementary organizational change, which is the single most useful finding for your argument and should be attributed properly. Absorptive capacity research explains why two firms buying the same system get different results, and it gives you a defensible reason to put capability building in the plan. Take the lag-and-complements finding as your central claim, and let it dictate a paper that spends more space on organizational change than on technology selection.
Citations that survive faculty review
Anchor the argument in four kinds of source. Theoretical and empirical foundations go to primary work, which means Brynjolfsson and Hitt on information technology productivity and complementary investment, Teece on business model design, Cohen and Levinthal on absorptive capacity, and the platform economics literature where network effects are genuinely present, each cited to the original. Peer-reviewed evidence comes from MIS Quarterly, Information Systems Research, the Strategic Management Journal, and the Journal of Management Information Systems, all obtainable through the Capella library and Business Source Complete. Standards, regulation, and framework documents carry your compliance and governance claims, cited as documents with versions and dates rather than as general knowledge, and this is where privacy obligations and control frameworks belong. Vendor material and analyst research can establish what products exist and what they cost, provided each is labelled commercial and never used to evidence a benefit. When you cite a case study of another organization's transformation, state the scale and sector before the lesson, because a lesson drawn from a firm ten times your size and in a different regulatory regime needs that qualification to survive scrutiny.
The mistakes that land Basic instead of Distinguished
- Recommending a platform instead of an operating model. Feature lists are procurement documents, and the criteria are asking what changes about how the organization works.
- Benefits with no denominator. Faster and more efficient cannot be tracked, and a benefit that cannot be tracked will not be delivered.
- Treating the run cost as an afterthought. Consumption pricing frequently raises ongoing cost, and a case built on capital savings alone overstates the return.
- Ignoring the legacy system of record. Sequence is dictated by what the underlying systems can support, and a plan that ignores it will slip at the first integration.
- A citation-free failure statistic. Widely repeated transformation failure rates come from undisclosed practitioner samples, and using one unattributed invites the challenge.
DB-FPX8720 questions students actually ask
How much technical detail does a doctoral business paper need?
Enough to make the operating argument credible and no more. You need to name the class of system, describe the integration points that constrain your sequence, and show that you understand what the data foundation requires, because those are the facts that determine whether the plan is feasible. You do not need architecture diagrams, product comparisons, or configuration detail, and including them usually signals that the analysis has migrated away from the criteria. The test to apply to any technical paragraph is whether removing it would change a decision in the paper. If it would, keep it and explain it in business terms. If it would not, cut it and use the space on the funding model or the capability plan, which is where the doctoral criteria actually live.
Can I write this case about a transformation that already happened?
Yes, and a retrospective case is often stronger than a proposal because the outcome is available as evidence. Establish what was intended, what was delivered, what it cost against what was approved, and where the benefit landed relative to the business case, then work backwards through the decisions that produced the gap. Keep the account de-identified, describe roles rather than named individuals, and be careful with figures that are not yours to publish, using ranges or indexed values where the absolute number is sensitive. The analytical payoff is that you can test the literature against a real result, and a paper that shows why the lag-and-complements finding predicted what happened in your organization is doing exactly what a professional doctorate is for.
Do I need to include artificial intelligence in a transformation paper?
Only where it does work in your argument, and then with the same rigor as any other claim. If the process you are targeting has a genuine candidate application, specify the task, the data it requires, the error tolerance the process can accept, the human review step, and the governance around it, then treat the cost and the benefit exactly as you would any other automation. If it does not, leaving it out is the stronger choice and no criterion penalizes you for it. What does cost marks is a paragraph asserting broad capability with no task, no data source, and no measurement, because that reads as an unexamined trend claim in a paper otherwise built on evidence.
Transformation case due?
Send the prompt, the criteria, and your process volumes. We will build the target operating model and compute payback and net present value line by line. Nothing to pay on sample one.