Every contact center vendor is screaming about their AI solution. Virtual agents. Chatbots. Generative AI. The promise is always the same: automate everything, reduce costs, transform overnight.
Most AI implementations are automating broken processes while completely missing the most valuable asset sitting in your contact center right now – scores of data logged from direct customer interactions
The $87 Billion Context Crisis
I’ve spent two decades in this industry. Built contact centers. Sold them. Optimized them for Fortune 500s. And right now, I’m watching everyone make the same expensive mistake—just with fancier technology.
The problem isn’t lack of data. Traditional QA teams can only review about 5% of interactions, sure. But even when they do analyze conversations, they’re measuring the wrong stuff.
Average Handle Time. Call abandonment rates. CSAT scores. First Call Resolution percentages.
What they’re NOT tracking is context. And context is everything.
What Happens When Context Dies
Picture this: A customer tells their whole story to your IVR. Then repeats it to your chatbot. Then explains it again to a live agent who has zero visibility into any of that.
More than half of agents say they’re constantly switching between systems just to answer basic questions. While they’re hunting through databases, your customer is on hold. Getting pissed. Planning their switch to your competitor.
Over 70% of customers expect you to have your act together, but 68% get annoyed when transferred between departments—which happens constantly because nobody has the full context.
Result? According to Statista, twenty-seven percent of customers are frustrated by how slow and ineffective your service is.
But here’s the kicker: Your best agents have already figured this out.
The Million-Dollar Intelligence You’re Ignoring
Your top 10% aren’t succeeding because they’re faster at clicking through screens. They’re winning because they’ve developed an instinct for context.
They know which questions to ask upfront. They recognize patterns. They understand when to show empathy and when to move fast. They’ve learned—through thousands of conversations—how to actually solve problems on the first call.
This expertise is worth millions. And it’s completely undocumented.
While you’re investing in AI that needs 6-12 months to “learn” from scratch, your best agents are already crushing it. Problem is, their knowledge lives only in their heads. When they quit (and with 30-45% annual turnover, they will), that intelligence walks out with them.
Traditional analytics can’t capture this. They tell you WHAT happened—handle time, sentiment scores, keywords. They can’t tell you WHY your top performers win or HOW they use context to drive results.
Why AI Is Automating Garbage
Most companies are deploying AI backwards.
They’re automating processes that shouldn’t be automated. Building chatbots that trap customers in “AI jail.” Training models on generic datasets that know nothing about their business, their customers, or what actually works.
And they’re doing all this while ignoring the contextual intelligence that already exists in their call recordings.
The Real Gap: Contextual Intelligence
Here’s what everyone’s missing: You don’t need AI to learn from scratch. You need AI to extract and scale the expertise that already exists.
Every customer conversation contains signals:
- How did your best agents handle this situation?
- What language reduced frustration most effectively?
- Which resolution paths led to upsells instead of escalations?
- What contextual cues predicted when customers needed empathy vs. efficiency?
If your AI learns from mediocre conversations, you get mediocre results. If it learns from your top performers, it becomes a weapon.
What Contextual Intelligence Actually Means
Contextual intelligence isn’t just knowing the customer’s name or account history. It’s understanding:
Behavioral Context: How do your best agents read customer signals? When do they escalate vs. resolve? How do they transition from problem-solving to relationship-building?
Process Context: Which workflows actually work in real life? Where do your documented processes diverge from what top performers actually do?
Business Context: Which conversations drive revenue? What behaviors lead to retention? How do you optimize for outcomes, not just activity metrics?
Historical Context: The full customer journey across every channel and interaction—not just what’s happening right now.
This is what transforms contact centers from cost centers into competitive weapons. And it’s exactly what traditional analytics—and most AI implementations—completely miss.
The Spearfish Approach: Reverse-Engineering Excellence
This is why we built Spearfish differently.
We extract contextual intelligence from your existing conversations. We analyze what your best agents actually do—not what your processes say they should do.
We identify the behavioral patterns, language choices, decision trees, and contextual cues that separate world-class service from mediocre interactions. Then we deploy that intelligence across your entire operation—human agents and AI agents—from day one.
No six-month learning curve. No “AI jail” customer experiences. No hoping your automated workflows eventually improve.
Just immediate, measurable value based on expertise that’s already proven to work.
Why This Matters Right Now
This year, up to 80% of customer service organizations will be using generative AI, according to Gartner. The race isn’t whether to adopt AI—it’s whether you deploy AI that actually understands your business context.
Your competitors are making massive AI investments right now. Most are automating broken processes and hoping for the best. A few smart ones are extracting contextual intelligence first, then automating from a position of knowledge.
Which approach do you think wins?
The Bottom Line
Context isn’t just important. Context is everything.
Nearly 70% of contact centers report agents spending 10-29% of their time on manual after-call work. Half agree agents spend too much time hunting for answers. This isn’t an efficiency problem. It’s a context problem.
Your best agents have already solved it. They’ve developed the contextual intelligence to deliver exceptional service consistently.
The question is: Are you going to extract that intelligence and scale it?
Or keep measuring yesterday’s metrics while your competitors turn context into competitive advantage?
Ask yourself: What would change if every agent performed like your top 10%?
That’s not hypothetical. That’s the opportunity sitting in your call recordings right now.
Ready to extract the hidden intelligence in your contact center? Schedule a contextual intelligence assessment and discover what your top performers already know.
About the Author: Ray Bohac is CEO and Co-Founder of Spearfish. Previously, he co-founded CallCopy/Uptivity (acquired by NICE Systems) and MotionCX, pioneering VoIP and speech analytics technologies. He’s spent 20+ years building and optimizing high volume contact centers for mid-market through Fortune 500 companies.
