A focused course, tailored for you
The IBM Data and AI Programme Pattern for Enterprise Customers
An enterprise programme pattern for IBM Data and AI engagements in 2026: watsonx landing, Cloud Pak for Data positioning, customer-side governance integration, programme-director engagement structure.
Programme directors at IBM Data and AI face customer programmes that need watsonx landing, Cloud Pak for Data positioning, and customer-side governance integration in the same engagement. The course delivers the integrated programme pattern.
$199 one-time
Tailored to your situation. Access within 24 hours. 30-day money-back.
Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.
Why this course
Programme Directors at IBM Data and AI face a 2026 customer programme that does not split cleanly. The customer Chief Data Officer asks about watsonx.ai and watsonx.governance landing for the customer's first regulated AI workload. The customer Chief Information Officer asks about Cloud Pak for Data positioning against the customer's existing hyperscaler data-platform commitments. The customer Chief Compliance Officer asks about the customer-side governance integration with the customer's existing GRC platform. The default IBM Data and AI programme handles each thread separately and the customer reads fragmented progress.
The course works through the integrated programme pattern. The watsonx.ai landing pattern. The watsonx.governance integration pattern. The Cloud Pak for Data positioning. The customer-side governance integration. The customer-side data-platform integration. The customer-side identity-federation pattern. The customer-side observability stack integration. The customer-side FinOps integration. The customer-side change-management pattern. The programme-director engagement structure. Twelve modules with deliverables. Plus a hand-built playbook for your account mix.
The 12 modules
Module 1. The 2026 IBM Data and AI customer landscape
Walkthrough of the 2026 IBM Data and AI customer landscape. The watsonx product family (watsonx.ai, watsonx.data, watsonx.governance) market position. The Cloud Pak for Data positioning. The competitive landscape against Microsoft Azure AI, AWS SageMaker, Google Vertex AI, Databricks, Snowflake Cortex. The strategic decisions a customer Chief Data Officer faces. The friction profile that slows IBM Data and AI programme momentum.
Module 2. watsonx.ai landing pattern
Build the watsonx.ai landing pattern. The customer's first regulated AI workload selection. The customer-side data-residency posture. The customer-side identity-federation integration. The customer-side observability integration. The customer-side change-management integration. The integration with the customer's existing ML-platform commitments. Plus the worked example for a regulated customer's first three watsonx.ai use cases.
Module 3. watsonx.governance integration pattern
Build the watsonx.governance integration pattern. The customer-side AI governance committee integration. The customer-side risk-classification framework integration. The customer-side audit-trail integration. The customer-side EU AI Act high-risk classification integration. The customer-side NIST AI RMF integration. Plus the worked example for the customer's first three high-risk AI use cases under the integrated governance pattern.
Module 4. Cloud Pak for Data positioning
Build the Cloud Pak for Data positioning. The on-premise pattern. The hybrid pattern. The cross-cloud pattern. The integration with the customer's existing hyperscaler data-platform commitments. The integration with the customer's existing data-catalog. The integration with the customer's existing data-lineage framework. Plus the worked example for the customer profile where Cloud Pak for Data is the right answer and the customer profile where it is not.
Module 5. Customer-side governance integration
Build the customer-side governance integration. The customer's existing GRC platform integration. The customer's existing privacy programme integration. The customer's existing model risk management framework integration. The customer's existing internal audit cadence integration. The integration with the customer's existing AI governance committee. Plus the worked example for a customer with active enterprise risk management committee oversight.
Module 6. Customer-side data-platform integration
Build the customer-side data-platform integration. The Snowflake integration pattern. The Databricks integration pattern. The Microsoft Fabric integration pattern. The Google BigQuery integration pattern. The Amazon Redshift integration pattern. The customer's existing data-lake integration. Plus the worked example for the customer's typical heterogeneous data-platform footprint and the IBM Data and AI integration architecture.
Module 7. Customer-side identity-federation pattern
Build the customer-side identity-federation pattern. The customer's existing identity-provider integration (Microsoft Entra ID, Okta, Ping Identity, IBM Security Verify). The SCIM provisioning pattern. The role-based-access pattern. The session-policy pattern. The customer-side audit-trail integration. Plus the worked example for the customer's typical user population and the role-hierarchy that maps to the watsonx workload.
Module 8. Customer-side observability stack integration
Build the customer-side observability stack integration. The IBM Instana integration. The Datadog integration. The Splunk integration. The Microsoft Sentinel integration. The Elastic integration. The metric-and-log routing framework. The alerting framework. The integration with the customer's existing SRE operating model. Plus the worked example for the customer's typical observability stack pattern.
Module 9. Customer-side FinOps integration
Build the customer-side FinOps integration. The watsonx consumption model. The Cloud Pak for Data licensing model. The customer-side cost-attribution framework. The customer-side cost-management framework. The integration with the customer's existing FinOps cadence. The integration with the customer's existing financial-management cycle. Plus the worked example for a customer's first-year cost model and the customer-side cost-optimisation pattern.
Module 10. Customer-side change-management pattern
Build the customer-side change-management pattern. The customer-side opt-in pattern. The customer-side rollback pattern. The customer-side communication pattern. The integration with the customer's existing change advisory board cadence. The customer-side training pattern. The customer-side documentation pattern. Plus the worked example for a typical customer's first 12 months of watsonx rollout under the change-management framework.
Module 11. Programme-director engagement structure
Build the programme-director engagement structure. The discovery phase. The reference-architecture phase. The pilot-workload phase. The full-rollout phase. The sustainment phase. The renewal conversation. The customer-side programme-governance committee integration. The integration with the customer's existing IBM relationship. Plus the worked example for a 12-month customer engagement and the pricing framework.
Module 12. Your 10-week build plan
Week by week. Weeks 1-2: landscape and watsonx.ai landing pattern. Weeks 3-4: watsonx.governance integration and Cloud Pak for Data positioning. Weeks 5-6: customer-side governance integration and data-platform integration. Weeks 7-8: identity federation, observability stack, FinOps. Weeks 9-10: change-management pattern, programme-director engagement structure. Deliverable: an integrated IBM Data and AI programme pattern ready for the next enterprise customer.
How this addresses your situation
Specific modules that map to what you said you are dealing with.
CDO asks about watsonx.ai → Module 2.
CDO asks about watsonx.governance → Module 3.
CIO asks about Cloud Pak for Data → Module 4.
CCO asks about governance integration → Module 5.
Customer has multi-vendor data platform → Module 6.
Customer has IAM commitment → Module 7.
Customer FinOps integration → Module 9.
Customer change-management → Module 10.
Who it is for
For programme directors at IBM Data and AI, principal architects at IBM Consulting, senior solution architects on IBM Data and AI engagements, and senior consultants at IBM partners delivering Data and AI programmes.
Who this is NOT for. Pure non-IBM practitioners. Practitioners with no enterprise programme management experience. Pure non-Data-and-AI roles.
How it arrives
Text-based course via LMS, plus downloadable templates and worked examples and the hand-built playbook.
Time investment. Roughly 18 hours of reading and 60 to 120 hours of build effort across the 10-week plan.
FAQ
Does this cover watsonx Orchestrate?
Module 2 covers watsonx Orchestrate adjacency.
What about watsonx Code Assistant?
Module 2 covers watsonx Code Assistant adjacency.
Does this cover Red Hat OpenShift AI?
Module 4 covers OpenShift AI adjacency where the customer has OpenShift in production.
What is in the implementation playbook for me specifically?
Programme pattern tuned to your account mix, governance integration matched to your customer's GRC posture, engagement structure pre-loaded with your IBM relationship.
