In the fast-moving world of consumer packaged goods e-commerce, DSI Solutions has built a solid business, with roughly $75 million in annual revenue and room to grow. But like many mid-sized firms, the company is exposed to a structural risk: larger competitors can often move faster because their data is more unified, their decisions are more coordinated, and their systems are better integrated across the business.

The old way of operating, with siloed teams, intuition-heavy decisions, and data trapped in separate systems, no longer creates enough leverage. DSI's leadership understands that the challenge is not the absence of analytics. It is the absence of a coordinated decision system. The bet, therefore, is not on another dashboard or a collection of disconnected AI tools, but on a unified decision-engineering platform powered by agentic AI.

The premise is straightforward: when demand, pricing, customer behavior, marketing, and service decisions remain disconnected, local optimizations work against the business as a whole. The answer is to build a system in which data, models, workflows, and teams are connected tightly enough that decisions begin to reinforce one another rather than conflict.

The Real Problem: Decisions Happen in Isolation

DSI faces a pattern that is now common across growing e-commerce companies.

  • Demand forecasting may exist, but prices do not move with real demand conditions.
  • Marketing campaigns are often broad rather than behaviorally specific.
  • Customer experience teams see important signals, but those signals do not reliably trigger actions elsewhere in the business.
  • Trends appear in social and market data before inventory and pricing systems respond.
  • Teams lack a shared view of how one decision affects downstream constraints in other parts of the company.

The result is predictable: missed revenue, inefficient spend, lagging reactions to the market, and a customer experience that feels fragmented. Worse, trying to optimize each silo independently often harms the larger system, because each local win creates new friction somewhere else.

The Solution: One Platform, Three Intelligent Modules, Powered by Agents

The proposed platform begins with a unified data engineering and management layer. This layer pulls together transactions, reviews, CRM data, social signals, competitor information, support interactions, and other business context that would otherwise remain fragmented. On top of that layer, agentic workflows coordinate three core modules.

1. Demand & Pricing (DP Module)

This module combines long-horizon forecasting with real-time demand sensing. Social and competitor signals are brought into the loop, pricing reacts to elasticity and market dynamics, and what-if simulators allow teams to test pricing and inventory decisions before making commitments. The purpose is not simply better forecasting in the abstract, but better commercial action.

2. Customer Behavior & Experience (CBX Module)

This layer turns customer signals into coordinated experience design. Reviews, support calls, browsing behavior, and purchase history feed a customer foundation model that supports personalization, better search, stronger recommendations, churn detection, and proactive support flows. The central idea is to increase customer lifetime value by treating the customer journey as one connected system rather than a sequence of disconnected touchpoints.

3. Marketing & Socials (MarkX Module)

This module shifts marketing away from broad campaigns toward targeted intervention. Audience selection uses behavioral clustering and uplift modeling, promotions are optimized by segment and loyalty profile, and omnichannel measurement closes the feedback loop so spending can be adjusted intelligently rather than retrospectively defended.

These three modules are joined by an agentic orchestration layer. The agents do not merely summarize data. They help plan, simulate, execute, measure, and iterate. That is what makes this a decision-engineering platform rather than a reporting layer with AI added on top.

Why This Is a Decision-Engineering Case

The interesting point here is not simply that AI is being applied to commerce. It is that the real object of design is the decision system itself. The company is moving from isolated prediction tasks to an integrated operating model in which actions across pricing, inventory, customer experience, and marketing are coordinated under a common decision surface.

In other words, the work is not just data science, and it is not just software engineering. It combines:

  • data engineering and business context integration
  • machine learning and behavioral modeling
  • workflow design and systems thinking
  • simulation, measurement, and operational evaluation
  • cross-functional coordination across product, sales, customer experience, and leadership

That is exactly the kind of work that sits at the intersection of applied AI, enterprise systems, and forward-deployed engineering.

From Strategy to Results: A Practical Two-Year Roadmap

The strategy is intentionally phased rather than theatrical.

  • Year 1 centers on foundational data work, demand forecasting, customer foundation models, and initial segmentation and personalization.
  • Year 2 activates dynamic pricing, scales campaign intelligence, deepens CRM and sales integration, and expands agentic functionality across teams.

The proposed numbers are concrete enough to matter: an estimated $3.5 million investment over two years, approximately $14.1 million in projected revenue impact, and a modeled two-year ROI of 303%. The significance of these numbers is not that they are guaranteed. It is that the business case has been expressed in operational and financial terms rather than as generic AI enthusiasm.

Concrete Tactics Already Mapped

The case is strongest where the ideas become specific.

  • Personalization becomes behavior-triggered rather than campaign-centered: champions receive differentiated treatment, bargain-seeking customers receive targeted nudges, and at-risk customers are identified early enough for intervention.
  • Trend prediction uses social signals, velocity shifts, and other leading indicators to prepare inventory without simply overstocking in response to noise.
  • Customer feedback loops convert reviews and support signals into sentiment analysis, product improvement cues, and structured outreach so the system can learn from its own service layer.

Why This Matters

DSI is not trying to imitate Amazon at Amazon's scale. The more interesting ambition is to build a decision-making advantage that is difficult for competitors to copy quickly. A company that unifies data, AI, workflow design, and organizational response gains more than model capability. It gains speed of coordinated learning.

The platform does not replace people. It increases the effectiveness of the people already making the business run. Marketing can test and refine faster. Pricing teams can simulate before acting. Customer experience teams can operate from a full view of the customer rather than fragments. Leadership can reason across the system rather than from isolated departmental snapshots.

In a market where many firms claim to be AI-powered, the difference is whether AI is embedded into the actual operating logic of the company. This case argues for the latter. The point is not symbolic modernization. It is building the infrastructure for better decisions, every day, across a connected organization.

Prabakaran Chandran, Connor Feldman, Luis Gomez, and Yipeng Wang (Kane) presented this strategy as part of the DSI Case Competition.