Vendors sell intelligent academic advising as a product; it behaves in practice as a change programme. The distance between a working prototype of the digital and data-driven work capability and a system the business can depend on is where most budgets disappear. Post-incident reviews keep identifying weaknesses in the wider digital and data-driven work agenda that were visible long before the incident. Each session closes with a decision the participant must justify about intelligent academic advising in their own setting. Participants leave with a method for this area of digital and data-driven work, not a set of opinions about it. What blocks progress on the digital and data-driven work discipline is usually unclear ownership rather than unclear intent. It is appropriate for those preparing to take on wider responsibility for intelligent academic advising. They leave able to brief senior management on this aspect of digital and data-driven work in terms that support a decision. The closing exercise tests whether the participant's plan for this area of digital and data-driven work survives a hostile question.
Expected Learning Outcomes
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Design the integration points between intelligent academic advising and the systems already in production.
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Build a security and access model for intelligent academic advising appropriate to the sensitivity of the data involved.
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Recognise early indicators that intelligent academic advising is drifting away from its intended design.
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Estimate what intelligent academic advising costs to run properly, and what is lost when it is not.
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Define success criteria for intelligent academic advising in business terms before any technology is selected.
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Assess whether intelligent academic advising should be built in-house, bought, or delivered through a partner.
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Set the minimum documentation for intelligent academic advising that is genuinely necessary, and stop there.
Who Should Attend
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Chief information officers accountable for the investment in intelligent academic advising.
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Information security officers reviewing the exposure created by intelligent academic advising.
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Those responsible for briefing external stakeholders on intelligent academic advising.
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Managers with direct responsibility for intelligent academic advising within the transformation programme.
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Technology and digital transformation managers leading intelligent academic advising.
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Data and analytics leads responsible for the pipelines behind intelligent academic advising.
Course Modules
Open any module to see its sessions and topics.
01
Intelligent academic advising: from pilot to production
2 sessions · 8 points
Session 1The paperwork for intelligent academic advising that is actually needed
Specify the fallback path when intelligent academic advising is unavailable.
Identify every system intelligent academic advising must read from or write to.
Name a single owner for each element of intelligent academic advising.
Distinguish symptoms from causes when intelligent academic advising underperforms.
Session 2Reading the true cost of running intelligent academic advising
Build the user briefing that explains what intelligent academic advising does and does not decide.
Review whether intelligent academic advising is aligned with the objectives of the transformation programme.
Confirm that reporting on intelligent academic advising reaches the people who can act.
Record the rationale for each significant choice made about intelligent academic advising.
02
Intelligent academic advising: measuring benefit and retiring what it replaces
2 sessions · 8 points
Session 1Proving intelligent academic advising paid for itself
Estimate compute and licensing cost for intelligent academic advising at expected and at peak load.
Define acceptance criteria for intelligent academic advising in advance.
Confirm the retention and deletion rules applied to data inside intelligent academic advising.
Confirm that those complying with intelligent academic advising understand why it exists.
Session 2Choosing a supplier for intelligent academic advising without being captured
Assess the regulatory obligations intelligent academic advising triggers in each jurisdiction.
Set the review interval for intelligent academic advising and who attends.
Decide which legacy process intelligent academic advising retires, and set the date.
Establish the boundary of intelligent academic advising and record what sits outside it.
03
Intelligent academic advising: vendor selection and avoiding lock-in
2 sessions · 8 points
Session 1Testing intelligent academic advising before relying on it
Estimate the resource intelligent academic advising requires to run as designed.
Test intelligent academic advising against edge cases drawn from real historical records.
Establish who is informed, consulted and accountable in intelligent academic advising.
Classify the data in intelligent academic advising and apply access controls that match the classification.
Session 2Keeping intelligent academic advising alive after the initial push
Identify single points of dependency in intelligent academic advising and reduce them.
Define the exit route from the supplier supporting intelligent academic advising.
Design the pilot for intelligent academic advising so that a negative result is still useful.
Verify that intelligent academic advising still performs when input volume doubles unexpectedly.
04
Intelligent academic advising: business case, scope and the data it depends on
2 sessions · 8 points
Session 1Making intelligent academic advising secure without making it unusable
Agree who is on call for intelligent academic advising outside working hours.
Set out how exceptions to intelligent academic advising are requested and approved.
Set the metrics that will show whether intelligent academic advising is drifting from its intended behaviour.
Check that intelligent academic advising still works when volumes rise unexpectedly.
Session 2What breaks first when intelligent academic advising meets real volume
Anticipate the objections intelligent academic advising will raise and prepare the answers.
Arrange the handover of intelligent academic advising so capability survives staff changes.
List the data sources intelligent academic advising consumes and confirm each has a named owner.
Identify the skills the team lacks to operate intelligent academic advising independently.
Choose the package that suits you
Silver Package
At least 3 people
USD1,250
Workshop or Program Participation
Airport Transfers
Customized Badge
Expert Mentorship (Private Sessions)
Supervision & Secretarial Services
Accredited Certificate of Participation
Complete Training Kit
Coffee Break
Closing Ceremony
Gold Package
At least 3 people
USD1,850
5-night stay in a 5-star hotel
Workshop or Program Participation
Airport Transfers
Customized Badge
Expert Mentorship (Private Sessions)
Supervision & Secretarial Services
Accredited Certificate of Participation
Complete Training Kit
Coffee Break
Closing Ceremony
Complete your registration
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