3 Ways You Can Integrate AI in Omnichannel Operations

With AI reshaping the retail landscape, success is all about delivering hyper-personalized experiences at scale. In our previous article, we uncovered three pivotal ways AI is revolutionizing omnichannel operations.

But for many retailers, the question is: how do you start without overhauling your entire infrastructure? The answer lies in targeted, high-impact projects that deliver immediate ROI while minimizing disruption.

Luxury footwear brand They New York exemplifies this – they started by introducing a modest AI-powered sales assistant, which drove a 67% increase in e-commerce sales. This pilot project became a strategic cornerstone, showing how innovation at the margins can yield transformative results.

The formula is simple: start small, think big, and focus on measurable wins.

Here are three practical ways to integrate AI into your omnichannel strategy.

1. AI-powered agents for customer service

The days of static FAQ bots are over.

Today’s chatbots have evolved into powerful, AI-driven agents that do far more than just answer questions. They are revenue enablers, efficiency boosters, and personalization engines—all crucial to omnichannel retail success.

*According to McKinsey & Company Global B2B pulse

Here’s why AI-powered chatbots should be a priority:

They improve customer service efficiency

Chatbots reduce the burden of repetitive, resource-draining queries like returns, order tracking, or payment updates. By automating these workflows, they use agents to focus on complex customer needs.

The ROI speaks for itself. IBM’s Watsonx Assistant, for example, delivered a 370% ROI over three years, saving businesses $23 million while improving service quality.

Camping World, the world’s largest RV retailer, reduced wait times to just 33 seconds and saw a 40% boost in engagement by integrating AI chatbots across their service platforms. And they recorded faster resolutions, improved customer satisfaction, and lower operational costs.

They act as revenue accelerators

Chatbots today are revenue accelerators. Equipped with advanced analytics, they leverage customer data to deliver personalized recommendations, upsell opportunities, and curated experiences.

The numbers don’t lie. McKinsey reports that companies excelling in personalization achieve 40% higher revenue growth. Retailers integrating chatbots into their sales workflows are transforming customer service from a cost center into a growth engine.

They simplify demand-based scalability

Seasonal surges and product launches often test the limits of traditional service teams. AI chatbots effortlessly scale to handle these peaks, ensuring your customers experience the same level of support—whether they’re engaging on your website, app, or social channels.

According to industry data, 35% of retailers are embedding AI across the customer journey—including chatbots—to meet elevated expectations and enhance the shopping experience.

How to get started

1. Identify pain points: Identify repetitive tasks such as order tracking, payment updates or high-stakes moments like escalations during product recalls.

2. Integrate automation: Embed AI chatbots with your CRM, ERP, and inventory systems to deliver personalized, context-aware interactions across touchpoints.

3. Start with a pilot: Launch a pilot in a targeted geography or customer segment with specific KPIs like average resolution time, deflection rates and conversion rates.

💡 Pro tip: Enhance chatbot capabilities by transitioning from reactive to proactive responses, offering real-time updates and tailored suggestions.

2. AI-powered search to improve product discovery

In a saturated market, delivering the right product at the right moment can make or break a sale. AI-powered search recommendations go beyond static tools by using semantic understanding, intent prediction, and real-time data integration to surface products that align with individual customer needs.

Queries like “comfortable running shoes for flat feet” don’t just need keyword-matched results, but contextual results that can assess arch support, cushioning technology, user reviews.

And that's why AI search matters to your bottom line:

• Lower Search Abandonment: Poor search experiences cost retailers over $2 trillion globally. With AI, you can significantly lower search abandonment rates.

• Improved Cart Value: According to a Google Cloud-commissioned Harris Poll survey, 92% of customers purchase an item following a successful search, with over 75% adding at least one more item to their cart.

• Actionable Insights: The data captured from AI-powered search can feed into your merchandising, marketing, and inventory strategies. This provides a clearer picture of customer preferences, helping you make informed decisions that drive growth.

⚡ FleetPride unified their search index and integrated AI-powered tools to tackle poor search accuracy and incomplete SKUs. The impact was immediate – a 27% increase in search engagement and 9.6% lift in conversion rates.

How to get started

Start by conducting a comprehensive audit of your existing search systems. Identify gaps such as frequent "no result" queries or low engagement with specific product categories. Once you have this baseline, implement a search solution that integrates real-time behavior tracking, intent prediction, and advanced analytics.

For retailers looking to scale, tools like Vertex AI Search for Retail offer Google-quality search capabilities. By leveraging semantic understanding, query reformulation, and image-based search, Vertex AI ensures a seamless product discovery experience. Retailers who’ve integrated these systems report significant improvements in both search-to-cart conversions and overall customer satisfaction.

3. AI-powered lead scoring to optimize sales

For businesses with complex sales processes—high-value B2C commerce or B2B environments—AI-powered lead scoring transforms how opportunities are identified, prioritized, and pursued.

Here’s a closer look at how this works:

• Predictive Analytics: Leverage diverse data points—website behavior, cart abandonment rate, post-purchase activity, or social signals—to identify patterns.

• Actionable Insights: Get actionable insights, such as the optimal time and channel to engage each prospect to personalize your marketing and improve conversions.

Salesforce Einstein Lead Scoring uses machine learning to process custom fields, activity data, and segmentation criteria, building tailored scoring models that ensures sales teams prioritize high-potential leads with precision.

Companies leveraging AI for lead scoring have reported a 23% increase in sales conversions and up to 50% reduction in lead qualification time.

How to get started

Start small, iterate fast.

Begin by deploying AI lead scoring in one segment or geography, then gradually scale based on pilot results. Here’s a framework that focuses on both progression and measurable outcomes.

Phase 1: Discovery – Identify Opportunities

• Identify underperforming sales segments or geographies with inefficiencies.
• Define hypotheses, like improving conversion rates by 5%.
• Establish metrics (e.g., current lead-to-sale time or average time-to-close) to benchmark success.

This phase ensures the foundation is data-driven and aligns with strategic priorities, minimizing wasted effort.

Phase 2: Validation – Test and Build Confidence

• Use historical customer data to train initial scoring models.
• Conduct A/B tests to compare AI-driven scores against traditional methods.
• Engage marketing teams to collect qualitative feedback and refine models.

Building trust is as critical as technical accuracy. Early wins with validated models secure stakeholder buy-in and foster adoption.

Phase 3: Refinement – Adapt to Real-World Needs

• Adjust models to reflect regional, seasonal, or industry-specific nuances.
• Integrate real-time data, such as buyer engagement, to adjust scores dynamically.
• Introduce predictive insights like upsell or cross-sell opportunities.

One-size-fits-all models fail in complex sales environments. Customization ensures AI aligns with real-world nuances.

Phase 4: Expansion – Scale Strategically

• Scale based on pilot success, targeting areas with the highest ROI potential.
• Monitor critical KPIs like sales velocity and CAC to align with business goals.
• Provide ongoing training to maintain adoption and maximize impact.

Selective scaling keeps risks low and maximizes ROI by focusing on high-performing use cases.

Phase 5: Innovation – Continuous Evolution

• Regularly revisit lead scoring models to incorporate new data and trends.
• Explore advanced capabilities like intent-based scoring and churn prevention.
• Leverage AI insights to drive cross-functional initiatives, like CLV optimization.

AI isn’t static. Businesses that treat it as a living system unlock long-term competitive advantage.

The future of retail belongs to those who can adapt quickly and scale intelligently. Ready to take the first step? Explore how AI can elevate your omnichannel operations and deliver measurable ROI—let’s make innovation happen.

Get in touch at hello@coderapper.com for a free technical discovery.