AI Research and Development Automation: 15 Tasks

This page is for software and applied-research teams that already have a bounded protocol, versioned code and data, and an independent reviewer. It is not a proposal to automate hypothesis choice, novelty judgment, or the decision to publish a result. Arsum’s task model is a screening layer—not proof of savings or permission to automate—and this page turns it into a buyer test with explicit owners, thresholds, and stop conditions.

The R&D leader decision: clear a reproducibility backlog

The first deliverable is not an AI research conclusion. It is a versioned packet that lets another qualified person replay a completed benchmark run from the hypothesis and protocol through the environment, artifacts, metrics, exclusions, logs, negative results, and reviewer disposition.

Run this pilot when:

• One recurring benchmark or experiment family has a written protocol and a measurable evidence-assembly backlog.
• Dataset, code, model, dependency, hardware or runtime, seed, metric, exclusion, and result versions can be captured.
• A research lead defines the claim and acceptance rule before the run, and a separate reviewer can attempt replay.
• Sensitive data, models, tools, compute, and storage have named permission owners.

Do not fund it yet when:

• The experiment question or comparison cohort is still changing during evaluation.
• The team cannot freeze required data, code, environment, model, seed, metric, and exclusion identities.
• Only successful results are retained, or negative and contradictory runs can be silently discarded.
• A generated summary would be treated as accepted interpretation without independent replay and peer challenge.

The first commercial decision is therefore bounded: improve experiment evidence and reproducibility packets, not “automate computer research and development.” The source of truth, case definition, reviewer, exception queue, and rollback state must exist before a vendor demonstration counts as evidence.

Where R&D automation stops: execution evidence versus scientific judgment

R&D automation must preserve uncertainty and failed paths, not only generate plausible experiments. The output is a reproducible evidence packet; novelty, scientific interpretation, and the decision to continue remain human-owned.

This separation answers the ambiguity behind the keyword. A system may be capable of generating a plausible artifact while still being unqualified to choose the underlying model, scientific conclusion, or product truth. Permission follows the workflow layer and cost of error, not the fluency of the output.

Reproducibility-pilot architecture and exception queue

The hypothesis, protocol, dataset, code, environment, model, seed, metrics, and exclusions are versioned before execution. Automation runs approved steps and assembles evidence; independent checks reproduce results; a research owner records interpretation, uncertainty, and next decision.

1. Register the claim before execution. Record the bounded question, protocol version, primary and guardrail metrics, cohort, exclusions, expected decision, and explicit falsification condition.
2. Resolve immutable identities. Capture dataset snapshot and license, code commit, model and prompt or configuration, lockfile or container, runtime and hardware, seed policy, evaluator version, and authorized operator.
3. Execute only approved steps. Automation invokes the registered workflow in an isolated environment, records every command and artifact, and preserves failed, aborted, contradictory, and successful runs under one experiment identity.
4. Assemble the replay packet. Produce a source map, manifest, logs, metric calculations, comparison table, deviations, costs, negative results, and unresolved validity questions. Missing evidence is an exception, never inferred content.
5. Replay independently. A reviewer who did not operate the first run executes the packet against the frozen protocol, records differences, and accepts, rejects, or requests correction before interpretation enters a decision record.

A mutable dataset, missing artifact, unlicensed source, undeclared exclusion, seed mismatch, evaluator change, environment drift, data leakage, warm-cache effect, irreproducible metric, or suppressed negative result blocks acceptance. The system records the failed path instead of rewriting it into a clean narrative.

A 30-day reproducibility scorecard with stop conditions

The following numbers are illustrative pilot gates, not industry benchmarks. Before kickoff, replace them with thresholds derived from a 60- to 90-day local baseline or one complete operating cycle. Keep the definitions fixed for the pilot so the team cannot improve the result by silently changing the denominator.

Calculate complete-packet rate as accepted packets containing every required artifact / packets submitted; independent replay rate as packets reproduced inside the declared tolerance / packets replayed; unsupported-claim rate as material claims without protocol-linked evidence / material claims reviewed; and negative-result retention as failed or contradictory runs preserved / failed or contradictory runs observed.

Worked benchmark dispute and visible pilot economics

An optimization appears to improve latency. Replay on the frozen dataset fails because the first run used a warmed cache. The packet exposes the environment difference, the result is rejected, and the negative finding is retained instead of summarized away.

For 50 completed benchmark runs in 30 days, suppose evidence assembly and replay setup consume 90 research-engineering hours. A versioned replay-packet workflow removes 34 hours but adds 10 hours of protocol, security, and acceptance review, leaving 24 net hours. At $130/hour, gross capacity is $3,120; subtract $1,150 for environment capture, storage, integration, and maintenance. Continue only when independent replay rate and evidence completeness improve without suppressing negative or contradictory results.

The arithmetic is intentionally visible. It includes review, correction, integration, and maintenance rather than treating generated output as realized capacity. The example is a worksheet pattern; it is not a forecast for another organization.

Research reproducibility RACI

The assistant may prepare evidence and propose a next action. It does not acquire authority from the occupation score. A material source gap, permission error, failed replay, severe factual error, or unavailable accountable owner returns the case to the human-led path.

How the 53.6/100 task score maps to this pilot

Arsum assessed 15 O*NET tasks for occupation 15-1221.00. The current 53.6/100 score combines task importance, frequency, modeled automatable share, and wage context. It is a directional capability index: it does not estimate job loss, adoption, realized ROI, or the share of cases a company will authorize.

This mapping is the practical derivation the occupation average cannot provide. The pilot includes tasks where inputs and acceptance evidence can be specified. It keeps consequential interpretation and final approval human-owned even when an adjacent drafting task receives a higher technical score.

The published 12.1-20.1 hours/week and $46,424-$77,374/year ranges use a disclosed 30-hour modeled task budget and the May 2025 BLS wage input. They are portfolio-planning ranges, not observed savings. Replace them with the local equation below:

net hours = baseline effort - automated effort - review - correction - failed-case rework
net value = net hours × loaded rate - tools - integration - validation - maintenance

Narrow the pilot to completed benchmark runs that trigger one output: a versioned replay packet containing protocol, dataset, code, environment, model, seed, metrics, exclusions, logs, and negative results. Record baseline assembly minutes, missing-artifact rate, independent replay result, reviewer corrections by severity, storage/compute cost, and decision outcome. Compute reproducible-packet rate as independently replayed accepted packets / packets prepared; net hours as baseline assembly - automated assembly - review - correction - failed replay; and net value as net hours × loaded rate - compute - storage - integration - maintenance.

Buy, connect, or build the reproducibility workflow

Whatever path wins, require the same difficult-case replay, evidence export, permission isolation, named approval, and rollback test. A polished demo on a clean normal case is not an acceptance test.

Evidence that changes the ai research and development automation buying decision

• O*NET 30.3 database: O*NET supplies the occupation task statements, task ratings, work context, and related descriptors used by the Arsum model.
• BLS Occupational Employment and Wage Statistics: BLS supplies the employment and wage snapshot used to translate modeled task capacity into a gross wage-capacity planning range.
• NIST AI Risk Management Framework: NIST frames AI risk management through govern, map, measure, and manage functions across the lifecycle.
• NIST Secure Software Development Framework: NIST organizes secure software development around preparation, software protection, well-secured production, and vulnerability response.

Three practitioner signals shaped the test design, and none is treated as a prevalence, accuracy, productivity, or ROI statistic:

• A discussion among experienced developers describes debugging costs when local assumptions are missing; the pilot therefore captures environment and correction paths.
• A quality-assurance discussion about day-to-day AI testing emphasizes grounding outputs in observed systems and accepted patterns; the pilot freezes its protocol and requires independent replay.
• A discussion about coding-assistant dependency raises the risk of losing the expertise needed to detect plausible errors; interpretation and peer challenge therefore remain human-led.

Evidence limits for this ai research and development automation decision

The O*NET task statements and May 2025 BLS wage table establish the public occupation context. Arsum’s score, task-to-pilot mapping, operating design, and illustrative economics are first-party analysis. Review the full scoring methodology before comparing roles.

Why the 2029 computer research and development scenario is secondary

The 65/100 scenario holds today’s O*NET task mix constant and changes technical-capability assumptions. It does not predict employment, demand, company adoption, regulation, or authorized autonomy. Greater technical capability can accelerate preparation and comparison, but a research buyer should expand only when independent replay, evidence completeness, and claim safety remain stable under the local protocol.

AI research and development automation: buyer questions

What is the first 30-day deliverable?

A replayable evidence packet for one registered experiment family: predeclared protocol, immutable artifact identities, commands, environment, metrics, exclusions, logs, costs, failed and negative runs, anomalies, replay result, reviewer, and final disposition.

What result justifies expansion?

Expand only when every critical artifact is present, the independent replay gate clears, unsupported claims and leakage remain at zero, failed paths are retained, and net assembly-plus-review time improves. A faster experiment summary does not qualify.

Which adjacent workflows should be compared?

Compare this pilot with Business intelligence (59.4/100), Software development (58.3/100), Technical writing (63.9/100). Use the Software Engineering & IT Automation Index to prioritize the portfolio rather than applying one occupation score to an entire engineering or IT department.