Contracting unit: - Office of the Chief Scientist, CGIAR System Organisation
Budget Ceiling: - USD 30,000 (all-inclusive; competitive open call)
Duration: - 6–8 weeks
2027 milestone: - Blueprint endorsed; build procurement decision made
Delivery lead: - Portfolio Performance and Results Team (PPT)
Oversight: - Steering Group (Office of the Chief Scientist, DTA, Digital)
Deadline for Applications: - 8 September 2026, 23:59hrs (Paris time GMT+2)
Background
The Performance & Results Management System (PRMS) is CGIAR's core infrastructure for portfolio planning, results reporting, quality assurance, adaptive management, risk management and accountability. It was identified in the CGIAR Performance and Results Management Framework as a common system housing planning, theory of change management, stage-gate decision points, and annual reporting — with linked datasets, an integrated dashboard for funders and management, and access aligned to international standards. A February 2021 Accenture fit-for-purpose assessment translated this into implementation requirements: integrated business applications, standardized data definitions across CGIAR systems, and comprehensive onboarding of end users. CGIAR invested approximately USD 800k per year to build and run that system during the period 2022-25, with joint Independent Advisory and Internal Audit review during rollout to ensure delivery against specification.
The PRMS was built for a context of relatively stable institutional requirements: a defined set of manual inputs, largely from Window 1/2 funding, processed sequentially — each stage waiting for the previous — to produce a predetermined set of outputs on a predictable cycle. Bespoke requests that fell outside those predetermined outputs required specialists to extract value case by case. That design was rational for the conditions in which it was built. Five shifts prompt a redesign.
The first is the nature of demand. The portfolio now requires dynamic, on-demand, user-defined outputs — some anticipated, many not. New business requirements arrive at unpredictable intervals: a GST decision requiring real-time performance data for resource allocation, a donor wanting a bespoke cut across Programs, an AI tool needing to interrogate the data layer directly. A system with predetermined outputs cannot serve this.
The second is the scope of what the system must hold. Window 3/bilateral funding — around 1000 non-pooled projects — is heterogeneous in structure, language, and reporting logic. W3/bilateral data cannot be pre-programmed into the system; it is a different problem type, and the current architecture has no way to accommodate it.
The third is the ability to orchestrate. CGIAR now deploys operational AI tools — for example SNAP, the zero-draft generator, the QA helper, the Progress Tracking Solution — that need to query, submit to, and coordinate across the system. The current PRMS is not orchestrable: its components are not designed to be called, sequenced, or managed by an AI agent. A system that AI can understand and act on any layer is a key requirement.
The fourth is the multiplicity of data sources the system must draw on. Portfolio evidence already sits across many instruments and repositories — among them legacy CRP and Initiative results, current Program and Accelerator results, Plans of Results and Budget (PoRB), Projected Benefits, the Impact Compendium, CGSpace, AnaPlan and others — each with its own structure and logic, with limited interconnections.
The fifth is the technological revolution in data management. The maturing of AI and large language models has changed what is possible: data no longer has to be captured through fixed forms and rigid fields to be usable, and outputs no longer have to be predetermined and assembled by hand. This reframes the redesign itself — not a faster version of the old model, but a shift to more flexible, AI-native handling of data that cuts duplication, lowers effort, and can generate higher-quality outputs on demand while maintaining core security safeguards.
The new PRMS must be modular, reconfigurable, and interoperable by design — not simply cheaper to build, but cheaper to run and extend, with a clear view of the trade-off between reduced human effort and the real costs of AI-driven processing at scale.
Two developments in 2026 show what this looks like in practice. The CGIAR Progress Tracking Solution — launched in July 2026 — demonstrates a possible target architecture: AI-assisted, modular, with a human validation layer, open enough for users to connect their own systems via API. The June 2026 GST-endorsed Guidance Note on Program/Accelerator-level prioritization establishes that actual performance against theories of change will directly inform W1/W2 fund allocation from 2026 onward: PRMS data must be queryable in real time to feed consequential resource allocation decisions, not compiled into static reports after the fact. Together, they define what fit for purpose now requires.
Purpose
This commission produces an architectural blueprint for a rebuilt PRMS — the design basis for a subsequent build investment. It is not a developer-ready technical specification. It is the front end of a two-stage process: design now, procure and build against it. Steering Group endorsement of this blueprint will be the decision gate for the larger investment.
The starting point is not the technology but the people the system serves — funders, senior leadership and governing bodies, Program and Accelerator directors, Centers, and external partners — and the outputs they need, from stabilized reports and dashboards to on-demand answers tailored to a specific question. The design works back from those needs to the data the system already holds, and it must make the system materially lighter to feed — less manual entry, less duplication, less double reporting — not only cheaper to run.
The design target is a system that is orchestrable: modular, reconfig