How can consulting companies use AI automation infrastructure to become more efficient?

Here I will share how I developed an AI automation infrastructure for a technology consulting company to automate their HealthTech trend analysis workflow.

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If you’re spending countless hours manually gathering data, analyzing market trends, and creating reports, this automation setup will transform your research process.

I’ll walk you through a complete system to streamline every step of your HealthTech research workflow, from data collection to report generation.

As innovation specialists, we’re expected to deliver detailed, data-driven reports analyzing market trends, competitors, and emerging technologies. This comprehensive task involves multiple research sources, complex analyses, and consistent reporting standards.

Today, I’ll show you how to build an automation system that will save you hours of manual work while improving your output quality.

Let’s start with the foundation of your research workflow: data collection.

The first scenario we’ll create is an RSS Feed Monitoring system. This automatically scans healthcare news sources at 6 AM.

Based on specific project requirements, it filters for relevant keywords such as “Singapore,” “APAC,” “HealthTech,” and “Remote Monitoring.” The system then extracts article metadata and stores it in your database, while also categorizing content by technology segment through text analysis.

The second data collection scenario focuses on Database API Integrations. This weekly automation connects to various sources to pull the latest HealthTech market data and funding information. The system parses the JSON responses, formats the data consistently, and stores it in your structured database.

The third component is a Social Listening Module that monitors Twitter and LinkedIn for key HealthTech companies daily. It filters for announcements, funding news, and partnerships, performs sentiment analysis on the posts, and compiles everything into a daily digest sent straight to your inbox.

Now let’s explore how to automate the analysis phase of your workflow.

First, we’ll set up a Market Sizing Calculation Scenario. This automation triggers whenever new data enters your market statistics sheet. It pulls the latest figures, runs predefined formulas for both top-down and bottom-up market sizing approaches, calculates growth rates and projections based on historical data, and updates your master market data dashboard.

Next is the Company Analysis Workflow. When a new company is added to your tracking database, this scenario automatically scrapes their website, looks up their profile on Crunchbase, searches patent databases, generates a standardized company profile, and even alerts team members if the company meets your high-potential criteria.

Finally, our Competitive Landscape Matrix Generator runs monthly to pull the latest company data, apply your scoring algorithm, generate visualization data for 2×2 matrices, and export everything to your visualization tool of choice.

Let’s move on to automating the report creation process.

Our Report Template Population scenario can be triggered monthly or manually.

It pulls the latest analysis data, formats it according to your report template, generates an initial draft in Google Docs, adds standard visualizations, and sends a notification to your team for review.

The Data Visuals Generator creates consistent charts and graphs whenever new analysis data becomes available.

It formats your data for various tools, generates a standard set of visuals covering market size, growth, and competitive landscape, and saves everything to a shared repository linked to your report draft.

For executive communication, the Executive Summary Creator takes your completed report draft, uses AI text summary, extracts key metrics and highlights significant changes, formats everything according to your executive summary template, and sends it for final review before distribution.

Quality assurance is critical for maintaining research integrity.

Our Data Consistency Checker triggers whenever new data is added to your research database. It compares the entries for inconsistencies, flags outliers or questionable data points, runs statistical validation tests, and generates alerts if issues are identified.

The Source Verification Workflow evaluates every new source against your trusted source list, verifies publication dates and author information, calculates a reliability score, and flags sources that don’t meet your minimum criteria.

To take your system to the next level, consider these advanced integrations.

AI Enhancements can be implemented by adding modules for text analysis, and trend identification. These can generate initial insights and draft report sections.

Custom Data Processing Modules can be added to handle complex calculations, run market sizing algorithms, and apply proprietary scoring systems for company evaluation.

An Alert System can be configured to send notifications via SMS, email, or Slack for high-impact news items, significant market changes, emerging competition, and report readiness.

Let me show you a practical application, a HealthTech Funding Tracker for Singapore Startups.

For data collection, it connects to CrunchBase and PitchBook APIs, filters for Singapore-based HealthTech companies, and stores funding rounds, investors, and amounts.

The analysis processing calculates quarterly funding totals by segment, compares to historical averages, identifies trending subsectors, and tracks investor activity patterns.

For visuals, it generates a funding heat map by subsector, creates time-series charts showing funding evolution, and maps investor networks and relationships.

The reporting component populates a monthly “Singapore HealthTech Funding Landscape” template, generates standardized slides for team meetings, and creates investor activity briefing documents.

Finally, the alert system notifies your team of funding rounds above SGD 10 million, alerts when new investors enter the Singapore market, and highlights when portfolio companies secure funding.

By implementing this automation framework, you’ll transform your HealthTech trend analysis workflow. You’ll save countless hours on manual research tasks while improving consistency and comprehensiveness. Your system can evolve over time as you add more data sources and refine your analysis methodologies.

Through this AI automation infrastructure, you’ll have a powerful research engine that delivers high-quality insights while freeing you to focus on strategic analysis and recommendations.

Thanks for reading!

If you have any questions about automating your research workflow, do get in touch here.