The narrative around AI for customer engagement paints a seductive picture: frictionless interactions and hyper-personalized experiences at the push of a button. However, implementing effective AI requires organizational transformation, serious resource investment and cross-functional alignment, which many companies aren’t prepared for.
The data reality gap
According to a Gartner study, tech stack implementation challenges top the list of AI adoption barriers, with 55% of organizations hitting this wall first. These infrastructure hurdles block any benefits from algorithms, no matter how sophisticated your AI platform may be.
Effective AI doesn’t start with choosing a platform. It begins with building a coherent data foundation. Most companies operate with customer information scattered across disconnected systems. Even AI high-performers struggle, with 70% facing persistent headaches in data governance, system integration and training data quality, McKinsey’s 2024 assessment revealed.
In the real world, this plays out in frustrating ways. Marketing teams encounter this reality when they discover customer data trapped in different formats across their CRM, website analytics, social channels and support systems.
Each system speaks its own language and has its own view of the customer. Try building a unified profile from that mess. Without that single customer view, your AI makes recommendations that don’t align (e.g., your email system suggests products the customer just complained about to your support team). These disconnected experiences do more harm than good, leaving customers wondering if anyone’s paying attention.
Dig deeper: AI adoption in CX is rising, but implementation challenges remain
Cross-functional alignment: The hidden implementation barrier
Marketing rarely controls the technical infrastructure needed for AI success, creating tension. Marketing and IT priorities often clash.
Marketing teams want speed and flexibility.
IT focuses on security and integration concerns.
This misalignment creates real problems in AI projects. According to Forrester, one-third of enterprise AI leaders identify skills gaps as their biggest roadblock, while 28% can’t figure out how to integrate AI into existing systems.
The divide goes beyond operational friction into strategic territory. Customer data platforms designed to bridge marketing and IT frequently underdeliver due to this organizational disconnect. Success depends on collaborative governance, where both domains share accountability and objectives.
Consider what happens when marketing implements a personalization AI without IT involvement: the solution works beautifully in isolation but fails to integrate with security protocols, creating potential compliance vulnerabilities.
Or when IT implements technical infrastructure without marketing input, creating systems that don’t support the experimentation and rapid deployment that effective customer engagement requires.
The talent reality
Customer care transformation experts found widespread skills gaps blocking the move to AI-enabled services. That isn’t just about technical specialists. It includes business leaders defining use cases and operations teams adapting to new workflows.
The potential upside is massive. Banking sector analysis suggests AI technologies could generate up to $1 trillion in additional annual value, with improved customer service driving a substantial chunk of that opportunity. Yet most financial institutions stumble on talent acquisition and governance challenges.
This talent gap affects multiple levels. You need:
Data scientists who understand customer behavior.
Engineers who can connect disparate systems.
Business leaders who can envision and implement new operational models.
Everyone overlooks a crucial piece of the puzzle: you need people who speak both languages. Call them AI translators if you want. They’re the rare folks who:
Understand both the business side and the tech side.
Have worked in marketing but can talk data science.
Know what your CMO cares about, but can also explain model training to engineers.
Finding these unicorns is brutal, and without them, projects stall out. Your data scientists build impressive models nobody uses, while your marketers ask for features nobody can build. This talent gap is the silent killer of AI projects that nobody talks about.
Dig deeper: 6 steps to help improve your customer experience with AI
The cost equation
Budget forecasts for AI implementation often miss the mark by a mile. While license costs get all the attention, the complete financial picture includes:
Legacy system modernization that can run into millions for enterprise organizations.
Integration services that typically cost more than the core technology.
Premium salaries for specialized talent in a tight market.
Ongoing optimization costs that nobody accounted for in the initial planning.
Many marketing leaders face additional ROI challenges, as productivity improvements don’t automatically translate to cost savings or revenue growth without organizational restructuring.
A major cosmetics retailer learned this lesson the hard way when their AI-powered product recommendation engine delivered impressive technical metrics — higher click-through rates and increased time on site — but failed to impact revenue.
The missing link? The AI recommendations weren’t integrated with inventory management, frequently suggesting out-of-stock products, creating customer frustration instead of sales.
A pragmatic path forward
Companies successfully deploying AI for customer engagement take a different approach than those left disappointed. Analysis of AI leaders reveals several common success patterns:
Instead of attempting a complete transformation, they start with focused use cases tied to specific business outcomes.
They create cross-functional governance with clear ownership.
They get data fundamentals right before exploring advanced algorithms.
They honestly assess internal capabilities and bring in partners where needed.
Forrester found something interesting: innovative companies start with AI projects that stay behind the scenes. Instead of jumping straight to customer-facing chatbots or recommendation engines, they begin with internal tools that help service reps find answers faster or help marketers draft content more efficiently.
This inside-out approach lets teams learn the ropes, work out the kinks and prove the value before rolling out AI that customers directly interact with. It’s like practicing your swing before stepping to the plate in a championship game.
What does a compelling first use case look like? Look for opportunities where:
You already have clean, accessible data.
The business outcome is clearly defined and measurable.
The scope is limited enough for implementation within 3-4 months.
Success would create momentum for broader adoption.
For many, customer service automation represents this ideal first step — combining relatively structured data with clear metrics for success, like reduced handle time or improved resolution rates.
The reality of the results
Despite the challenges, AI is transforming customer engagement for organizations with realistic expectations. About 71% of organizations now regularly use generative AI in at least one business function — up from 65% earlier in the year, according to McKinsey.
Forward-thinking companies are capturing cost reductions and revenue increases where they’ve deployed AI. These results come from those who recognize implementation as a transformation journey requiring significant investment in people, processes and technology, not a quick fix.
For marketing leaders, the path to meaningful AI-enhanced customer engagement means:
Acknowledging these realities.
Addressing challenges in data infrastructure, talent, cross-functional collaboration and governance.
Organizations expecting plug-and-play solutions inevitably join those with expensive implementations that fail to deliver. The winners in this space get something fundamental: AI works best alongside humans, not instead of them.
McKinsey’s 2024 findings make this clear: most organizations favor a human + AI approach over full automation. They don’t fall for the replacement narrative. Instead, they find ways AI can handle repetitive tasks while people focus on judgment calls and creative thinking.
Gartner’s research reinforces this, emphasizing that effective customer engagement requires balancing automated responses with human interaction. This balanced approach delivers operational improvements and better customer experiences that help brands stand out in competitive markets.
Dig deeper: AI improves customer service only when it supports humans, not replaces them
Contributing authors are invited to create content for MarTech and are chosen for their expertise and contribution to the martech community. Our contributors work under the oversight of the editorial staff and contributions are checked for quality and relevance to our readers. The opinions they express are their own.
