Strategy Execution Research Builder

Discover real implementation examples and turn them into grounded OKRs, KPIs, Balanced Scorecard views, execution plans, and executive briefs.

This Apify Actor is built for teams that need more than raw research. It finds relevant implementation examples from trusted websites and optional search expansion, extracts structured evidence, and generates deterministic planning artifacts without requiring an external LLM.

It is designed for:

• strategy and transformation teams
• PMO and operations leaders
• consultants
• business analysts
• internal research teams preparing planning workshops or execution programs

What this Actor does

This Actor helps you move from research to execution support.

Instead of manually collecting articles, PDFs, and framework pages and then translating them into planning material, the Actor:

1. discovers relevant implementation examples from seed URLs and optional search expansion
2. extracts structured evidence from pages and optional PDFs
3. identifies framework mentions, challenges, timelines, lessons, metrics, and supporting quotes
4. generates deterministic execution-ready outputs based on the evidence
5. stores a run-level summary with aggregate insights and an executive brief

The goal is not to replace strategy judgment. The goal is to give your team a clean, source-backed starting point for planning and execution.

How to use

Using this Actor is straightforward:

1. Click Try for free
2. Add a few trusted seedUrls
3. Optionally add searchQuery for discovery expansion
4. Select the frameworks and execution artifacts you want
5. Run the Actor
6. Review results in:
   • the Dataset for per-source evidence
   • the OUTPUT record for the run summary
   • the DASHBOARD record for the HTML interface

HTML dashboard

This Actor also generates a polished HTML dashboard for live review and post-run sharing.

• DASHBOARD record: a browsable HTML workspace written to the Key-Value Store
• Live View support: the same dashboard updates during the run
• multi-view layout: Dashboard, History, and Dataset
• grounded presentation: the HTML stays deterministic and reflects real run data, not fabricated UI states

The dashboard is useful when you want a more presentation-ready layer on top of the raw dataset and OUTPUT record.

Live example:

• https://api.apify.com/v2/key-value-stores/lRQGvrZLsLQ053rc0/records/DASHBOARD

Example screenshot:

What it produces

Per-source outputs

For each crawled page or parsed PDF, the Actor produces normalized dataset items with evidence such as:

• framework mentions
• implementation challenge
• timeline references
• lessons learned
• success metrics
• supporting quotes
• publication metadata
• source URL and domain

Deterministic execution artifacts

Depending on your configuration, the Actor can generate:

• OKRs
• KPIs
• Balanced Scorecard views
• executive summary / executive report
• EOS components
• project plan rows
• RACI rows
• OCAI guidance

Run-level artifact

At the end of the run, the Actor writes a run-level OUTPUT record to the Key-Value Store with:

• aggregate counts
• top frameworks detected
• top domains
• run-level executive brief
• other summary indicators useful for downstream workflows

HTML dashboard output

The Actor also writes a DASHBOARD HTML record to the Key-Value Store. This output includes:

• a run summary view with key metrics and executive brief
• execution artifact previews for OKRs, KPIs, Balanced Scorecard mappings, and optional outputs
• a dataset view for current-run records
• a history view backed by persistent actor storage for previous executions

Why use this Actor

Turn scattered research into planning inputs

Most teams can find case studies. The harder part is converting them into something useful for execution. This Actor helps bridge that gap.

Stay grounded in evidence

Outputs are based on extracted source material. Unsupported values are not invented. Missing facts remain empty or null.

No external LLM required

The Actor is deterministic by design and does not depend on an external LLM provider.

Useful for real business workflows

The output is designed to support:

• planning workshops
• transformation program design
• PMO preparation
• executive briefings
• structured research pipelines

Best use cases

This Actor works especially well when you want to:

• research how organizations implemented ADKAR, Balanced Scorecard, or other strategy execution frameworks
• build a source-backed starting point for OKRs or KPIs
• collect implementation lessons from consulting firms, methodology providers, and case-study pages
• prepare executive briefs before planning sessions
• compare strategy execution approaches across domains, industries, or regions

Example input

{
  "searchQuery": "ADKAR balanced scorecard digital transformation case study",
  "seedUrls": [
    "https://www.bain.com/insights/management-tools-balanced-scorecard/",
    "https://www.prosci.com/methodology/adkar"
  ],
  "allowDomains": ["bain.com", "prosci.com"],
  "frameworkType": ["ADKAR Change Model", "Balanced Scorecard"],
  "industry": "Technology",
  "region": "Global",
  "languages": ["en"],
  "followDepth": 1,
  "maxResults": 5,
  "dedupe": true,
  "summaryWords": 150,
  "includePdf": false,
  "executionArtifacts": ["OKRs", "KPIs", "BSC", "EOS Components", "Executive Report"],
  "okrHorizon": "Quarter",
  "bscPerspectives": ["Financial", "Customer", "Internal Process", "Learning & Growth"],
  "pmMethodology": "Hybrid",
  "meetingCadence": {
    "weeklyL10": true,
    "monthlyOps": true,
    "quarterlyPlanning": true
  }
}

Example output ideas

Depending on the evidence found, the Actor can help produce outputs such as:

Source-backed OKRs

• objectives aligned with the selected framework
• candidate key results grounded in available evidence
• quarter-focused structure when okrHorizon is set

KPI drafts

• candidate KPI ideas mapped to the implementation context
• clearly separated from unsupported source metrics

Balanced Scorecard views

• structured into perspectives such as Financial, Customer, Internal Process, and Learning & Growth

Executive brief

• concise run-level summary
• top themes
• common challenges
• repeated lessons
• dominant frameworks and domains

HTML dashboard

• presentation-ready review surface for the current run
• live-updating dashboard during execution
• history and dataset views for easier navigation of results

How it works

1. Discovery

The Actor starts from seedUrls and can optionally expand discovery with searchQuery.

2. Filtering

You can constrain the crawl using:

• allowDomains
• frameworkType
• industry
• region
• languages

3. Extraction

The Actor extracts structured evidence from pages and optional PDFs.

4. Artifact generation

The extracted evidence is transformed into deterministic execution-ready artifacts.

5. Run summary

A run-level OUTPUT record is generated for quick review and automation.

6. HTML presentation layer

The Actor writes a DASHBOARD HTML record for live review, output sharing, and lightweight run navigation across dashboard, history, and dataset views.

Recommended first run

For the best results in v1:

• start with a few high-quality seed URLs
• keep followDepth low
• enable only the execution artifacts you really need
• use searchQuery as optional expansion, not as your only discovery method

A strong first run usually looks like:

• 2 to 5 trusted seed URLs
• 1 to 2 frameworks
• a single industry context
• a small result limit

This gives you cleaner evidence and more focused outputs.

Input field guide

Field	Purpose	Notes
seedUrls	Primary discovery source	Start here for the best results in v1
searchQuery	Optional search expansion	Useful for finding additional examples, but not exhaustive
allowDomains	Focus on trusted sources only	Strongly recommended for cleaner results
frameworkType	Guide extraction toward your planning model	Helps focus the generated outputs
industry	Add contextual relevance	Optional filter
region	Add regional context	Optional filter
languages	Restrict to target languages	Useful when sources mix locales
followDepth	How far to follow links from seed pages	Lower values are usually cleaner
maxResults	Limit the number of processed results	Smaller runs often produce better signal
dedupe	Reduce repeated or overlapping results	Recommended enabled
includePdf	Enable PDF parsing	Can improve coverage, but is slower
executionArtifacts	Choose which outputs to generate	Enable only what your team needs
summaryWords	Control summary length	Typical range: 100-200 words

Tips for best results

• start with seedUrls, because they are the highest-quality discovery path in v1
• keep runs focused with 2-5 trusted sources, 1-2 frameworks, and one industry context
• use searchQuery for expansion, not as your only discovery method
• keep followDepth low unless you truly need broader coverage
• enable only the execution artifacts your team will actually use
• validate quality on a small run before scaling up

Important notes

• seedUrls are the most reliable source of high-quality results in v1
• searchQuery is useful for expansion, but should not be the only discovery strategy
• the Actor does not invent unsupported numeric metrics
• missing evidence stays empty or null
• confidence should be interpreted in the context of available source support
• the Actor is deterministic and does not require an external LLM
• the HTML dashboard is a presentation layer over real stored outputs, not a separate analysis engine
• PDF parsing can be slower than HTML crawling

What this Actor is not

This Actor is not:

• a generic web search bot
• an LLM-only strategy writer
• a replacement for expert planning judgment
• a guarantee of complete market or literature coverage

It is a deterministic research-to-structure pipeline that helps teams move faster with better-organized evidence and execution-ready outputs.

Is it legal to scrape the websites this Actor accesses?

This Actor is designed to extract public information from websites and PDFs for research and planning purposes. Whether scraping is appropriate depends on the source website's terms, your jurisdiction, and how you intend to use the data.

Important:

• personal data may be protected by GDPR and other privacy laws
• you should not scrape personal data unless you have a legitimate reason to do so
• if you are unsure, consult your legal team before using the output operationally

We also recommend reviewing Apify's guidance on web scraping legality:

• https://blog.apify.com/is-web-scraping-legal/

Ideal users

This Actor is a strong fit for:

• strategy consultants
• transformation leads
• PMO teams
• operations and business planning teams
• researchers building structured strategy evidence packs

Positioning summary

Use this Actor when you want to go from:

articles, framework pages, and scattered case studies

to:

source-backed execution inputs your team can actually work with

Short store description

Discover implementation examples and turn source-backed evidence into deterministic OKRs, KPIs, Balanced Scorecard views, and executive briefs.

Suggested first test

Try a small run with:

• one framework
• two trusted seed URLs
• one industry
• one or two artifact types

Example:

• framework: Balanced Scorecard
• industry: Technology
• artifacts: BSC, Executive Report

This will help you validate quality before scaling discovery.