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The Real Business Advantage: Optimize, Then Automate

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  Published in Business and Finance on Friday, September 12th 2025 at 8:26 GMT by Forbes
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The Real Business Advantage: Why Optimization Comes First, Then Automation

In a rapidly evolving marketplace where speed, flexibility, and data‑driven decision‑making have become the norm, many organizations rush to implement automation tools without fully understanding the processes they are trying to streamline. A recent Forbes Business Council piece, “The Real Business Advantage: Optimize Then Automate,” argues that the key to sustainable competitive advantage lies in a disciplined, step‑by‑step approach that begins with rigorous process optimization and ends with thoughtful automation. The article, which pulls insights from real‑world case studies and research, offers a practical roadmap for executives and operational leaders looking to turn routine operations into high‑value assets.

The Core Premise: “Optimize, Then Automate”

The article opens with a stark observation: most automation initiatives fail because they target poorly defined or inefficient processes. When a company simply plugs a software solution into an existing workflow, it often ends up amplifying the same bottlenecks or creating new ones. Instead, the author recommends a two‑phase strategy:

1. Process Optimization – Thoroughly map, analyze, and redesign existing workflows to eliminate waste, reduce cycle times, and align activities with strategic goals.
2. Strategic Automation – Apply technology to the optimized process, ensuring that the tools are tightly integrated, data‑centric, and designed for scalability.

By separating the “why” (process improvement) from the “how” (automation), businesses can avoid costly rework and build a more resilient operational foundation.

Phase One: Mapping and Refining Processes

1. Process Discovery and Documentation

The first step is to create a detailed map of the current workflow. The author emphasizes the value of value stream mapping, a lean technique that visualizes every step, decision point, and handoff. During discovery, teams should:

• Collect quantitative data (time, error rates, cost) from each process element.
• Conduct qualitative interviews with frontline employees to capture tacit knowledge and identify hidden pain points.
• Identify non‑value‑added activities—those that do not directly contribute to customer value or strategic objectives.

2. Root Cause Analysis

Once the map is in place, the article suggests a systematic root cause analysis using tools such as Fishbone (Ishikawa) diagrams or the 5 Whys. The goal is to differentiate between symptoms (e.g., high rework rates) and underlying causes (e.g., unclear quality standards, inconsistent data entry). Removing root causes, rather than merely treating symptoms, yields long‑term efficiency.

3. Redesign and Standardization

The next step involves redesigning the workflow to eliminate redundancies, reduce handoffs, and streamline decision criteria. Key tactics highlighted include:

• Standard Operating Procedures (SOPs) that codify best practices and ensure consistency.
• Automation‑ready data models that standardize fields and formats, paving the way for seamless technology integration.
• Performance metrics (key performance indicators, or KPIs) that reflect the redesigned process, such as cycle time, defect rate, and employee productivity.

By creating a cleaner, more transparent process, organizations can dramatically improve their ability to monitor, control, and evolve operations over time.

Phase Two: Thoughtful Automation

1. Choosing the Right Technology

The article warns against the temptation to buy “the latest bot” without aligning it to the process. It recommends a needs‑based technology assessment that evaluates:

• Fit for purpose – Does the tool solve the identified pain point, or is it a generic solution that requires significant customization?
• Integration capabilities – Can it seamlessly plug into existing ERP, CRM, or data lakes without creating new silos?
• Scalability and flexibility – Will it grow with the business, or will it become a legacy burden after a few years?

The author cites an example of a mid‑size manufacturer that moved from a custom spreadsheet system to an AI‑driven inventory management platform after first validating the data model and KPIs.

2. Pilot and Validation

Before a full rollout, the article stresses the importance of a small‑scale pilot that tests the automation against real‑world scenarios. Key considerations include:

• Real‑time monitoring of KPI changes post‑automation.
• User acceptance testing to gauge employee sentiment and identify training gaps.
• Iterative feedback loops that enable rapid adjustments to the automation logic or user interface.

Successful pilots often lead to higher adoption rates and a clearer ROI trajectory.

3. Scaling, Governance, and Continuous Improvement

Once the pilot proves successful, the article outlines a governance framework for scaling:

• Data governance to ensure data quality, security, and compliance across all automated touchpoints.
• Change management practices that involve cross‑functional teams, transparent communication, and continuous training.
• Continuous improvement loops that revisit process maps and KPIs every six to twelve months to keep the system agile.

The author underscores that automation should not be viewed as a one‑time project but as an ongoing evolution that continually refines both technology and process.

Real‑World Success Stories

Several case studies illustrate the power of the optimize‑then‑automate approach:

• Retail Supply Chain – A retailer streamlined its order fulfillment by first mapping its order‑to‑delivery cycle, reducing unnecessary manual checks, and then implementing an automated routing algorithm that cut shipping time by 15% and reduced labor costs by 12%.
• Healthcare Claims Processing – A mid‑size insurance firm identified duplicated claims handling steps, redesigned the workflow to eliminate redundancy, and then introduced AI‑driven claim triage. The result was a 30% reduction in claim turnaround time and a 20% increase in customer satisfaction scores.
• Financial Services KYC – A bank standardized its customer onboarding data fields, which enabled a robotic process automation (RPA) tool to ingest and verify KYC documents in minutes, eliminating a backlog that previously caused regulatory risk.

Each of these examples demonstrates that optimization creates a “clean slate” for automation to work effectively and sustainably.

The Bottom Line: Sustainable Competitive Edge

The article concludes that the real business advantage lies not merely in automating for the sake of automation, but in embedding automation within a disciplined, data‑driven process improvement framework. By first eliminating waste, clarifying responsibilities, and standardizing data, companies unlock the full potential of automation technologies. The result is a more responsive, agile organization that can:

• Reduce operating costs through predictable, efficient workflows.
• Accelerate time‑to‑market by eliminating manual handoffs and rework.
• Enhance employee satisfaction by freeing staff from repetitive tasks and allowing them to focus on higher‑value activities.
• Strengthen data quality and governance, creating a richer foundation for analytics, AI, and strategic decision‑making.

For leaders looking to secure a durable competitive edge, the optimize‑then‑automate mantra offers a clear, actionable blueprint. It reminds us that technology is a tool—powerful when guided by purpose, rigor, and continuous learning.