Machine learning (ML) has transitioned from an experimental technology to a core component of competitive business strategy. Early adopters have proven ML’s immense potential to extract insights, automate processes, and improve customer experiences. However, many companies still struggle to move ML from isolated pilots to scalable solutions that drive tangible financial returns. Turning ML investments into profit is crucial. This article explores pragmatic approaches to monetizing ML that accelerate growth.

Start by Quantifying the Opportunity

The value ML offers is often discussed generally rather than quantified. To build a business case, companies should start by digging into questions such as:

  • What specific pain points or unmet needs could ML address across our operations and offerings?
  • Where could ML automation or personalization most impact revenue, cost savings or customer lifetime value?
  • What new pricing, business model or product innovations could ML enable?
  • How much additional revenue, margin improvement or other financial upside could be realized annually if applied well?

Estimating even a ballpark TAM (total addressable market) focuses the strategy. It also helps secure buy-in and resources from stakeholders. Turning ML from a cost center to a profit driver requires crisp financial thinking.

Of course, projections will be imperfect given ML’s dynamically evolving potential. But even directional estimates bring more rigor than claiming ML will somehow “boost competitiveness.” Once opportunities are sized, priorities for investment should focus on use cases with clear profit pathways.

Monetization Models

ML offers numerous ways to drive revenue, reduce costs and improve margins. Common monetization models include:

  • Automation efficiencies: ML streamlines labor-intensive processes to cut costs in areas like document processing, call centers, claims management, invoicing, inventory processing and more. The labor savings drop directly to the bottom line.
  • Improved asset utilization: In manufacturing, ML optimizes production schedules, predictive maintenance and supply chain logistics. This increases capacity utilization and output with the same fixed assets.
  • Churn reduction: ML powers customer analytics and personalization that decrease churn. Retaining more customers boosts lifetime value.
  • Pricing optimization: ML parses pricing experiments, competitor data, and customer usage patterns to dynamically optimize price points. This maximizes revenue.
  • Upselling / cross-selling: ML sales automation and recommendation engines boost share-of-wallet through personalized upsells and cross-sells.
  • New business models: ML enables entirely new subscription services, marketplaces, data monetization streams or automated offerings. This unlocks new revenue pools.
  • Fee-based services: ML expertise itself can be productized, selling data analytics, personalized recommendation engines, predictive analytics, and other ML services to clients.

Looking across these models, there are typically two avenues to monetize ML. The first is improving existing processes and offerings. The second is developing entirely new businesses and revenue streams. Leaders should pursue both paths in parallel for maximum impact.

Moving from Pilots to Scaled Deployment

Unfortunately, many ML proofs of concept stall before reaching full deployment. Turning prototypes into productionized solutions requires addressing organizational and technical barriers. Key steps for scaling ML include:

  • Catalog existing data assets: Understand what customer, operational, transactional and other data is already available internally before looking externally.
  • Clean and structure data for ML: Make sure training data is formatted, normalized and labeled. This heavy lifting is required for accurate ML.
  • Build ML infrastructure: Establish scalable pipelines for data ingestion, model training, deployment, monitoring and retraining. Cloud ML platforms ease this burden.
  • Develop ML operations: Operationalize model monitoring, drift detection, explanation and ethical oversight. ML engineering is critical.
  • Align model KPIs: Ensure ML models optimize business KPIs not just accuracy metrics. Connect models directly to financial returns.
  • Shift mindsets: Move from “experimenting” with ML to productizing solutions that demonstrably improve core metrics.
  • Address risks proactively: Monitor ML risks like bias, latency, compliance, security, maintainability. Make governance explicit.

This end-to-end systems view is crucial. ML models alone offer little value. To monetize ML, it must integrate smoothly into workflows and enhance real business outcomes.

Organizational Readiness

Equally important as technical deployment is building organizational readiness across teams involved in ML:

  • Communicate the “why”: Ensure stakeholders understand the targeted business value. Connect ML to financial outcomes not just technological novelty.
  • Incentivize the right behaviors: Align team goals and incentives to the ML roadmap. Break down data silos.
  • Develop ML talent: A shortage of ML experts remains a bottleneck. Prioritize recruiting, training and retaining scarce talent.
  • Address ethics proactively: Form ethics panels. Conduct bias audits. Enlist diverse perspectives to avoid issues.
  • Partner across IT, analytics and business units: ML adoption requires tight collaboration across teams. Smooth hand-offs are key.
  • Provide ample access to data: Demystify data and provide self-serve access with proper governance to encourage ML experimentation.
  • Upgrade CX and UX skills: Ensure customer-facing teams have skills to integrate ML into customer experiences.
  • Evangelize wins: Celebrate examples where ML delivers clear value. Quantify impact. Promote role models.

Navigating organizational culture and change management is equally important as getting the technology right. Without buy-in across teams, ML decay into shelfware.

Measuring Returns

As ML scales, it remains critical to demonstrate tangible financial value. A focus on returns should permeate decision-making:

  • Quantify ML’s impact: Continuously measure how ML improves key performance indicators and financial returns. Make the benefits visceral.
  • Prioritize high-upside models: Double down on ML applications that drive major cost reductions or revenue lifts. Prune those that plateau quickly.
  • Review ROI periodically: Calculate ML return on investment regularly. Shift resources to where they will have greatest impact.
  • Optimize end-to-end value: Look beyond model accuracy. Ensure ML improves actual business outcomes. Connect technical metrics to financials.
  • Monitor model decay: Check regularly for degraded model performance and retrain when needed. Don’t let accuracy slowly decay.
  • Share wins across the organization: Celebrate success stories. Turn ML skeptics into advocates.
  • Raise data quality standards: Garbage in, garbage out. Continuously improve data used for training.
  • Watch out for technical debt: Keep architectural choices, code quality and documentation healthy as ML systems scale rapidly.

ML delivers the greatest value when solution architects and business leaders collaborate closely. Neither side can maximize returns alone. Setting shared objectives and KPIs keeps teams united around measurable business impact.

The Road Ahead

ML adoption remains early. On average, companies are only using ML in 4% of processes where it could plausibly deliver value. Myriad greenfield opportunities remain to develop, extend and personalize offerings through ML. Leaders who build organizational muscle to monetize ML – while carefully mitigating risks –will widen competitive gaps in their industries.

Approached strategically, ML can create step-function advances in automation, analytics and customer experience. But these outcomes are not automatic. They require concerted efforts to build, monitor, measure and maintain large-scale ML systems responsibly. Companies that excel at moving ML from isolated experiments to integrated business solutions will be poised to unlock outsized growth. By keeping their eyes on monetization and financial returns, savvy executives can capture ML’s full disruptive potential.