Digital Transformation Designed to Succeed: Fit the Change into the Business Strategy and People
Bibliographic record
Abstract
Digital transformation has become a necessity in our volatile, uncertain, complex and ambiguous (VUCA) world. In their 2019 report, APQC found that 75% of organizations are undergoing digital transformation. Successful digital transformation requires a strong foundation of people, process, technology and content. Selection of the right combination of strategies and deep stakeholder engagement is important in early phases of change when transformation initiatives inform leaders and users why change is needed. Top drivers for digital transformation have business (e.g., increased efficiency and productivity) and people (e.g., optimize user experience with knowledge discovery) facets. This paper illustrates an example of digital transformation in practice led by Knowledge Management, within Alberta Health Services (AHS). AHS is Canada’s first and largest province-wide, fully integrated health system with more than 102,700 employees. Employees need a platform for collaboration on projects, as well as documents and idea generation to meet business needs and enable them to become more efficient and effective in their daily jobs. The design, development, and implementation of a collaborative platform within this large organization required close orchestration of strategies, stakeholders’ commitments and engagement, represented by a continuum of stakeholders’ engagement formats, relationship and trust-building. Setting the stage for successful implementation and post implementation required a preview of technological and workforce trends to anticipate the future of work and worker. Fitting the change into overall business strategy, developing the knowledge of how change would affect the workers, and setting up a mechanism to inform leaders about adoption and user engagement were added as overarching strategies to better align with the line of sight in digital transformation. The platform was implemented with 23 business areas that expressed interest; it has demonstrated the potential to enable system transformation if implemented organization-wide. Business value was demonstrated with an ROI calculation on time savings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.019 | 0.045 |
| Scholarly communication | 0.038 | 0.035 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".