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Digital Transformation Assignments: Moving Beyond 'We Should Use AI'

Digital Transformation Assignments: Moving Beyond 'We Should Use AI'

Digital transformation is now on almost every MBA strategy syllabus, and it produces a recognisable weak submission: a list of technologies, a claim that the firm must adopt them to remain competitive, and no analysis of whether it could. Technology selection is the easy part and the least assessable.

Start with a Maturity Assessment

Before recommending anything, establish where the organisation actually is. Assess across dimensions and rate each:

  • Data — is data centralised, governed, and trusted? Most transformation programmes fail here first.
  • Technology estate — legacy systems, integration debt, cloud position.
  • Skills — internal capability versus dependence on vendors.
  • Process — are processes documented and standardised enough to automate?
  • Culture and governance — decision rights, appetite for iteration, tolerance of failure.

Present this as a radar chart or a simple scored table with evidence for each rating.

The uncomfortable finding is usually the valuable one. If data governance is immature, an AI recommendation is premature — and saying so demonstrates better judgement than proposing a model the organisation cannot feed.

Anchor Every Initiative to a Business Outcome

For each proposed initiative, complete this sentence: "We will do X so that Y improves, measured by Z." If you cannot complete it, the initiative is technology for its own sake. Typical outcome categories: cost to serve, cycle time, conversion rate, retention, error rate, compliance exposure.

Build a Business Case a CFO Would Read

Include implementation cost, ongoing run cost, expected benefit with a stated basis, payback period, and NPV over a defined horizon. Be explicit about which benefits are cash-releasing and which are cost-avoidance or capacity-release — conflating them is a common and easily-spotted weakness.

Change Readiness, Not Just Technology Readiness

Most transformation failures are organisational. Address:

  • Who loses influence or headcount, and how that will be handled.
  • Whether middle managers have the capability to run new processes.
  • How adoption will be measured — licences purchased is not adoption; weekly active use is.
  • What the organisation will stop doing to make room.

Sequencing and Governance

Propose a phased roadmap: foundation work (data quality, integration), then quick wins that build credibility, then the larger structural changes. Define stage gates with criteria for proceeding. A roadmap without decision points is a wish list with dates attached.

Where AI Fits Honestly

If you recommend AI or machine learning, be specific about the use case, the training data required, how outputs will be validated, and the governance around them — including relevant regulatory context such as the EU AI Act for firms operating in Europe. Vague enthusiasm reads as unfamiliarity; a narrow, well-specified use case with a validation plan reads as competence.

Measuring Whether It Worked

Transformation programmes are notorious for reporting activity rather than outcome — systems deployed, staff trained, workshops run. None of these tell you whether anything improved. Build a measurement framework with three tiers:

  • Adoption — weekly active users as a share of eligible users, not licences bought.
  • Process — cycle time, error rate, cost per transaction before and after.
  • Outcome — the commercial measure in the business case, tracked against the forecast.

State a baseline for each before implementation begins. Without a baseline there is no way to demonstrate benefit, and post-hoc baselines are rightly treated with suspicion.

Common Weaknesses in Submitted Assignments

  • Technology-first reasoning. Starting from a tool and searching for a problem it might solve.
  • Ignoring legacy constraints. Recommending real-time personalisation for a firm whose customer data sits in four unlinked systems.
  • No cost of change. Counting licence fees while omitting integration, training, parallel running and the productivity dip during transition.
  • Benchmark transplanting. Citing what a large digital-native firm does without asking whether a mid-sized incumbent has the capital, data or talent to follow.

Naming the constraint that makes the obvious answer unworkable, and then proposing something achievable, is the single clearest signal of commercial judgement you can give a marker.

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