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Chapter 3.7

Recommendation Development

Insights without recommendations are just interesting facts. Recommendations without execution plans are just wishes. Learn to transform insights into actionable, prioritized, and implementable recommendations that drive real change.

Recommendation development is where consulting delivers its ultimate value. Everything before — problem definition, data collection, analysis, insight generation — builds toward this moment. A brilliant insight that doesn't lead to a clear, actionable recommendation is wasted. And a recommendation that can't be implemented is worthless. This chapter covers the art and science of developing recommendations that get approved, funded, and executed.

"Clients don't pay for analysis. They pay for decisions. Your recommendation is the decision you're asking them to make. Make it impossible to ignore."

The Recommendation Development Framework

1. Generate Options

Brainstorm multiple potential solutions. Don't settle on the first idea. Include "do nothing" as a baseline option.

2. Evaluate Options

Assess each option against criteria: impact, feasibility, cost, risk, timeline, strategic alignment.

3. Prioritize

Rank options. Recommend a primary path and, if helpful, secondary/fallback options.

4. Build Roadmap

Sequence recommendations into phases. Define dependencies, milestones, and success metrics.

5. Quantify Impact

Estimate ROI, cost savings, revenue uplift, or risk reduction. Be specific and defensible.

6. Build Buy-In

Anticipate objections. Tailor communication to stakeholder concerns. Pre-sell before final presentation.

Option Generation Techniques

Brainstorming

Generate as many ideas as possible without judgment first, then filter. Quantity over quality initially.

Benchmarking

What have competitors or companies in other industries done in similar situations?

Analogy

How have similar problems been solved in different contexts?

AI Generation

Use AI to generate option lists based on problem statements and industry data.

Evaluation Criteria for Recommendations

Criteria
Questions to Ask
Impact
How much will this improve the problem? ($$, % improvement, risk reduction)
Feasibility
Can we actually do this given resources, skills, technology?
Cost
What investment is required? (Capital, time, people)
Risk
What could go wrong? Probability and severity?
Timeline
How long until value is realized?
Strategic Alignment
Does this fit with overall company strategy and values?
Stakeholder Support
Who will champion it? Who might block it?

The Recommendation Development Process (Step-by-Step)

  • Step 1: Restate the problem and insights. Remind everyone what problem you're solving and what you've learned.
  • Step 2: Generate 3-5 distinct options. Include "do nothing" as a baseline. Avoid presenting only one option (it looks like you've pre-decided).
  • Step 3: Evaluate each option against criteria. Use a weighted decision matrix if criteria have different importance.
  • Step 4: Recommend a primary path. Be decisive. Clients hire you for judgment, not just options.
  • Step 5: Develop implementation roadmap. Phases, milestones, owners, success metrics.
  • Step 6: Quantify the business case. ROI, payback period, NPV, or other relevant financial metrics.
  • Step 7: Anticipate objections. What will skeptics say? Address concerns proactively.
  • Step 8: Pre-sell to key stakeholders. Share draft recommendations with champions before the final presentation.

The Weighted Decision Matrix

A structured tool for comparing options when criteria have different importance.

Criteria (Weight)
Option A
Option B
Option C
Impact (40%)
9/10 → 3.6
6/10 → 2.4
8/10 → 3.2
Feasibility (25%)
7/10 → 1.75
9/10 → 2.25
5/10 → 1.25
Cost (20%)
5/10 → 1.0
8/10 → 1.6
6/10 → 1.2
Risk (15%)
6/10 → 0.9
8/10 → 1.2
7/10 → 1.05
Total Score
7.25
7.45
6.70

Recommendation: Option B has highest weighted score. Option A close second — could be recommended with risk mitigation.

Real Consulting Example: ERP Recommendation Development

Problem: Client's current ERP is outdated, causing inefficiencies across 5 warehouses.

Options Generated:

  • Option A — Upgrade current ERP: Lower cost, faster, but limited new capabilities.
  • Option B — Replace with new ERP (Phased): Higher cost, longer timeline, but modern capabilities.
  • Option C — Best-of-breed (Warehouse + Finance): Highest flexibility, highest integration complexity.
  • Option D — Do nothing: Baseline.

Evaluation (Weighted): Impact (40%), Feasibility (25%), Cost (20%), Risk (15%)

Scores: Option B (7.8), Option A (6.9), Option C (6.2), Option D (2.1)

Recommendation: "Replace with new ERP in phased rollout. Phase 1: Finance and Procurement (6 months). Phase 2: Warehouse and Logistics (additional 6 months). Expected ROI: 3.2x over 3 years. Payback: 14 months."

Implementation Roadmap included: Month 1-2 selection, Month 3-6 Phase 1 design/build, Month 7-8 testing/training, Month 9 go-live.

Result: Client approved, implementation on track, Phase 1 delivered 22% efficiency gain.

Quantifying the Business Case

ROI Calculation

(Net Benefits / Investment) × 100. Target > 100% for most projects.

Payback Period

How many months until benefits exceed investment?

Net Present Value (NPV)

Sum of discounted future cash flows minus initial investment. Positive = value-creating.

Internal Rate of Return (IRR)

The discount rate at which NPV = 0. Compare to cost of capital.

Common Recommendation Mistakes

Only One Option

Presenting only your preferred recommendation looks biased. Show alternatives you rejected and why.

No Implementation Plan

"What should we do?" without "How should we do it?" leads to shelfware. Always include roadmap.

Ignoring Feasibility

Great recommendation that can't be executed is useless. Be realistic about resources and capabilities.

No Risk Assessment

Every recommendation has risks. Acknowledge them and propose mitigation — builds credibility.

How AI Enhances Recommendation Development

Option Generation

AI can generate dozens of potential options based on similar problems across industries.

Scenario Modeling

AI runs thousands of scenarios to test recommendation sensitivity to assumptions.

Risk Prediction

AI predicts which recommendations are most likely to succeed based on historical project data.

LOBO AI Recommendation Engine

Our proprietary engine generates draft recommendations, weighted decision matrices, and implementation roadmaps for consultant refinement.

Structuring Recommendations: The Pyramid Principle

Present recommendations using the Pyramid Principle:

  • Top: "We recommend Option B — Replace ERP in phased rollout."
  • Middle (3-5 supporting arguments): "1) Highest ROI (3.2x), 2) Acceptable risk profile, 3) Aligns with digital transformation strategy, 4) Phased approach reduces disruption."
  • Bottom (Evidence): ROI model, risk assessment, strategic alignment analysis, case studies.

Executives decide in seconds. Give them the conclusion first, then the rationale.

Ready to Turn Insights into Actionable Recommendations?

Professionals Lobby consultants don't just analyze — we deliver clear, prioritized, implementable recommendations with ROI models, risk assessments, and roadmaps. We help you make decisions with confidence.

Strategic Recommendations Business Case Development ROI Modeling Implementation Roadmaps Decision Support
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Key Takeaways

  • Recommendation development has 6 phases: Generate Options → Evaluate → Prioritize → Build Roadmap → Quantify Impact → Build Buy-In.
  • Always generate multiple options (including "do nothing") before recommending. Single-option recommendations appear biased.
  • Evaluate options against criteria: Impact, Feasibility, Cost, Risk, Timeline, Strategic Alignment, Stakeholder Support.
  • Use weighted decision matrices when criteria have different importance levels.
  • Every recommendation needs an implementation roadmap — phases, milestones, owners, success metrics.
  • Quantify the business case: ROI, payback period, NPV, or IRR. Specific numbers build credibility.
  • Anticipate objections and pre-sell to key stakeholders before final presentations.
  • Structure recommendations using the Pyramid Principle: conclusion first, then supporting arguments, then evidence.
  • Common mistakes: only one option, no implementation plan, ignoring feasibility, no risk assessment.
  • AI enhances recommendation development through option generation, scenario modeling, risk prediction, and draft recommendation engines.