Open to AE or GTM Engineering roles | Call me: 727 637 2778 | Follow to find out what the heck Clay.com actually does | Will do a paid post for $1,000,000

Full cycle AE  and GTM Engineer specializing in early stage SaaS companies going from 0 -> Unicorn status.

Worked for companies backed by the biggest names in VC. Base10, Ridge, Susa Ventures, Altos, Alt Capital, Greylock and Microsoft. 

Taking big swings :)

🏍benyamin ‎

I'm joining AirOps as a GTM Engineer.

I gotta say this was a weird one, I had at least 3 people reach out to me separately, maybe more, telling me they were hiring for this role and that I should look into it. Once I did it became very obvious I should seriously consider talking to these folks. 

If you've followed me for a while, you know I've avoided purposefully GTM tech. The market is fickle. Costs are deflationary. And I think seat-based SaaS pricing is going to buckle under the weight of headless tools and agents operating APIs way more efficiently than humans ever would. 

So why break my own rule?

Because AirOps isn't selling more seats or licenses or a fancy workflow, they're solving for how businesses get discovered in a world where brands are found through prompting and not SEO. 

Google built the infrastructure for how we found information for 25 years. It's not dead yet, but when you look at the buyers of the next five years, they will not be relying on the first page of Google anymore. 

ChatGPT, Perplexity, Gemini, Copilot, Rufus on Amazon. The way people find answers is fragmenting across general-purpose AI, shopping AI, and proprietary search engines we haven't even seen yet. Most companies have no idea how to show up in those contexts.

AirOps is building the workflows that help them figure it out. And as more AI search vectors emerge, I think we're positioned to capture that market.

People have asked me why I decided to make this move. The reason is simple. if AirOps gets the technology right and grabs enough land early enough, this is a chance to ride one of the biggest technological shifts of the modern era.

Thank you to Eoin Clancy and Lucas Hulleberg for the opportunity. And to all the other team members I haven't met yet who have built something that I will get to grow. 

Travis Tyler accepting a role here had a non-zero effect on my decision. I was mulling over whether to make a move when I heard he started here, and I figured there must be something to it. He's a pretty discerning guy. 😉

AirOps is looking to grow headcount very aggressively this year. If you are looking for a new role with high performing folks - please reach out to me, I will connect you with the right person.

P.S. Sweater not real

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📂 GTM Engineering
  ┃
  ┣ 📂 Prospecting & Enrichment
  ┃ ┣ 📂 Contact Discovery
  ┃ ┣ 📂 Multi-Source Enrichment Chains
  ┃ ┣ 📂 ICP Fit Scoring
  ┃ ┗ 📂 Intent Signal Detection
  ┃
  ┣ 📂 Outbound Engine
  ┃ ┣ 📂 Email Sequencing
  ┃ ┣ 📂 Sender Infrastructure & Warmup
  ┃ ┣ 📂 Persona-Based Copywriting
  ┃ ┗ 📂 Reply Classification
  ┃
  ┣ 📂 CRM Automation
  ┃ ┣ 📂 Salesforce Operations
  ┃ ┣ 📂 HubSpot Sync
  ┃ ┣ 📂 Pipeline Reporting
  ┃ ┗ 📂 Data Hygiene
  ┃
  ┣ 📂 Meeting Intelligence
  ┃ ┣ 📂 Transcript Processing
  ┃ ┣ 📂 MEDDPIC Extraction
  ┃ ┣ 📂 Follow-Up Generation
  ┃ ┗ 📂 Deal Stage Automation
  ┃
  ┣ 📂 Account Research
  ┃ ┣ 📂 Prospect Briefs
  ┃ ┣ 📂 Tech Stack Detection
  ┃ ┣ 📂 Web Scraping & Crawling
  ┃ ┗ 📂 Competitive Analysis
  ┃
  ┣ 📂 AI Visibility
  ┃ ┣ 📂 Answer Engine Optimization
  ┃ ┣ 📂 Structured Data / JSON-LD
  ┃ ┗ 📂 AI-Search Publishing
  ┃
  ┣ 📂 Workflow Orchestration
  ┃ ┣ 📂 Task Management
  ┃ ┣ 📂 Visual Dependency Boards
  ┃ ┣ 📂 EOD Reporting
  ┃ ┗ 📂 Team Dashboards
  ┃
  ┗ 📂 Self-Healing Infrastructure
    ┣ 📂 Auto-Correcting API Docs
    ┣ 📂 Reusable Script Library
    ┣ 📂 Automated Smoke Testing
    ┗ 📂 Persistent Memory

I started putting my GTM work in a GitHub repo. Not code in the traditional sense. Operational logic. API schemas, enrichment routing, slash commands, context files that teach my AI copilot how to do my job.

It started as a practical problem. Everything I'd built at Cyft lived on my local machine. If my SSD died, a huge chunk of how I actually worked would die with it. GitHub was the obvious fix.

But it turned into something bigger. When I accepted my offer at AirOps, I had three weeks before my start date. So I started prepping a brand new repo for the new environment. Currently I'm building the scaffolding for all new and improved raw code and slash commands that I'll be running in Claude Code at Airops

Example of things I'm building: When I was at Cyft I had a growth meeting command that would pull from three APIs and compiles a structured agenda. Salesforce, Email Bison and Typeform to form a holistic picture of our funnel. At AirOps, the tools will be different. The architecture and ideology transfers. 

I think GTM engineers are going to start maintaining repos the way software engineers always have. I think highly technical revops teams are already started to resemble SWE teams more than the revops of 2015. 

If I were interviewing a GTM engineer today, I'd ask if they have a Github I could look at. I wouldn't expect it. But I'd ask. Not because the code matters that much, but because it signals how someone thinks about their work. Systems vs. tasks. Reusable vs. disposable.

Wrote the full breakdown 👇

Toward the tail end of my time at Cyft, I started seeing an existential threat to my work. All of the workflows, Claude Code slash commands, API integ

Open article: GTM Engineers Should Have Github Repos by 🏍benyamin ‎ • 6 min read

if you sell API-based SaaS and you're not optimizing your site for Cursor, Claude Code, and the other coding agents... you're about to get cooked by those who are.

I believe in the future these agents will sign up for services on my behalf, run test queries against real data, and benchmark outputs head to head. The evaluation stage of buying dev tools is about to skip humans entirely. And honestly I trust Claude Code to evaluate many of these outputs much better than I'm able to. 

This is what we're building for at AirOps. AI search isn't just ChatGPT and Perplexity anymore. 

Buying decisions are happening inside coding agents, IDE environments, SaaS-embedded chatbots. 

If your content isn't structured for machines to read and evaluate, you're invisible to an entire buying motion that's growing faster than any of us expected.

There's already more agents on the internet than humans. Soon they won't just be reading and reporting, but making decisions. Real business decisions with real dollar amounts attached to them. This is already happening at small scale. 

I had Claude Code buy some domains for me on Dynadot earlier this week. Now this is a small example, right? But an example nonetheless. I loaded up some money to Dynadot, fed Claude all the domains we currently owned and asked it to make some new ones and then go buy them throug the Dynadot API... That's a microcosm of what's about to start happening at many companies. 

Hope you're getting ready for it :)

how to source candidates from your own linkedin network for $40 and make 5k in commissions
 
if you're a seller with a big linkedin network, you're sitting on a recruiting side hustle. most recruiters charge 15-25% of base salary per placement. that's $20-40K per hire. you can do the same thing with your own connections and a claude subscription.
 
here's what I'm building right now to support AirOps 3x headcount growth this year and hopefully pick up some fat checks :)
 
𝘀𝘁𝗲𝗽 𝟭: 𝗲𝘅𝗽𝗼𝗿𝘁 𝘆𝗼𝘂𝗿 𝗹𝗶𝗻𝗸𝗲𝗱𝗶𝗻 𝗱𝗮𝘁𝗮
 
go to settings, data privacy, get a copy of your data. linkedin gives you a full dump. every connection, their title, company, location. takes about 2 or 3 hours to process. load it into a SQLite database so you can actually query it without spending 9000 tokens on looking for a contact
 
𝘀𝘁𝗲𝗽 𝟮: 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘆𝗼𝘂𝗿 𝘁𝗮𝗿𝗴𝗲𝘁 𝗿𝗼𝗹𝗲𝘀
 
i scraped 31 job descriptions from our Ashby careers page and broke each one into structured criteria. required skills, seniority level, relevant companies, tech stack, years of experience. this becomes your matching rubric.
 
𝘀𝘁𝗲𝗽 𝟯: 𝘁𝘄𝗼-𝘁𝗶𝗲𝗿 𝗺𝗮𝘁𝗰𝗵𝗶𝗻𝗴
 
tier 1 is keyword matching. titles, companies, skills. fast and free. filters 10K connections down to a few hundred plausible candidates per role.
 
tier 2 is where it gets good. i use exa to pull enriched linkedin data on tier 1 matches, then feed those profiles into claude code to score against each role. work history, tenure patterns, location, tech stack overlap. things a keyword match misses.
 
𝘀𝘁𝗲𝗽 𝟰: 𝗲𝗻𝗿𝗶𝗰𝗵 𝗮𝗻𝗱 𝗱𝗿𝗮𝗳𝘁 𝗼𝘂𝘁𝗿𝗲𝗮𝗰𝗵
 
top-scoring candidates get deeper enrichment through leadmagic. the system drafts personalized linkedin DMs based on why that specific person fits. not "i saw your profile" messages. actual context about their career and how it maps to the role.
 
took an hour to build. these people already know you. a warm DM from a real connection converts at a completely different rate than a cold InMail from a recruiter. 
 
the system tells me "Maya is a 47/50 match for Senior AI Engineer because she built RAG pipelines at Notion and has 7 years in ML." i review the context and hit send.

If you're hooked up to your apollo API key you could then use the peoplefinder endpoint to expand on this process if you're having luck with it
 
any seller with 5K+ connections and a $20/month claude subscription is sitting on $2-6K commissions per placement in warm leads they already have.

P.S. screenshot is fake for educational purposes :)

𝗛𝗼𝘄 𝘁𝗼 𝗕𝗹𝗮𝗰𝗸-𝗣𝗶𝗹𝗹 𝗬𝗼𝘂𝗿 𝗦𝗮𝗹𝗲𝘀 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 𝗔𝗯𝗼𝘂𝘁 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲
 
An SDR I shared some tips with messaged me after starting a new role and said they've been totally blackpilled about the status quo after implementing Claude Code and Cursor workflows for the GTM team at their previous company. 

They can't unsee the efficiency gains, and they can't unsee the insanity of all the manual processes around them.

They'd asked their new manager if they could hook up the Apollo API to Cursor to automate some of their longer-form workflows, specifically around lead list building. They were told no without much explanation.
 
I get why. If somebody with six months of sales experience says "let me hook up the most sensitive part of the GTM stack to an AI tool with a dubious level of security," "Fuck No" is a reasonable first reaction. There are real security concerns. But you're shooting yourself in the foot by hand-waving about security when the mitigations are really quite simple. Fix it, move on, and start utilizing the efficiency gains.
 
If you're relating to this, that frustration is a signal. You're early. And there's still time to save yourself from the impending white collar job collapse. 
 
They went on to ask how to socialize this stuff within their new team. Here's what I told them:
 
The play - treat institutional change like a sales cycle.
 
Don't walk into your manager's office and pitch a revolution. That's how you get a polite "no" and a reputation as the weird one. I learned this the hard way when a past CEO told me, "Ben, you have good ideas, but you don't know how to leverage political capital." He was right.
 
Instead, multithread. Sell bottoms-up. Drop an article in the team Slack. Share something in a meeting. Get two or three peers curious. Let them start asking their own questions.
 
If you do this, your manager starts getting the same questions from multiple people. It's no longer one person asking about weird shit, it's a pattern. It makes its way to the director meeting, and now the conversation is happening at a level where things actually change.
 
If you zoom out, the cost of building software is going to zero. Distribution, security, compliance, customer service, all the enterprise readiness stuff, that's what's becoming the real challenge. The playing field is being leveled. 

A lot of people are being lulled into a false sense of security while their competition gets leaner. Smaller teams are producing the sales output of much larger ones across pipeline management, outbound activity, and post-sales support. They're building a compounding velocity advantage and they are going to use it to destroy you.
 
If you're a sales director, a RevOps leader, and you haven't used Claude Code or Cursor, you need to go familiarize yourself. Google it. It's not hard to find.
 
I write about this stuff regularly. If you need talking points to socialize these ideas, just send them one of my posts :)

BPO, AI & SAAS SALES LEADER | ENTERPRISE TECH, CONTACT CENTER & CX SOLUTIONS | DIRECTOR / VP BUSINESS DEVELOPMENT

Sales and business development leader with 20+ years selling BPO, SaaS, and enterprise technology solutions into mid-market and enterprise organizations.

I help companies reduce operating cost, increase CSAT, and scale customer operations using a combination of outsourced delivery, AI-driven automation, and SaaS CX platforms.

Closed and expanded multimillion-dollar deals across voice, chat, email, back-office, and digital channels. Experienced selling SaaS subscriptions, enterprise platforms, and onshore, nearshore, and offshore delivery models across North America and EMEA.

Deep understanding of contact center and SaaS economics. Cost per contact. ACV. ARR. Churn. Attrition. Staffing risk. Compliance. SLAs. QA. CSAT.

𝐖𝐇𝐀𝐓 𝐈 𝐃𝐎 𝐁𝐄𝐒𝐓
• Enterprise BPO, SaaS, and technology sales
• AI voice and chat automation
• Conversational AI and virtual agents
• Agent assist and QA automation
• Digital CX and contact center platforms
• GTM strategy and pipeline generation
• Account expansion and strategic partnerships

𝐈𝐍𝐃𝐔𝐒𝐓𝐑𝐈𝐄𝐒 𝐈 𝐖𝐎𝐑𝐊 𝐖𝐈𝐓𝐇
Retail | E-commerce | Technology | Travel | Healthcare | Fintech | Telecom | Energy

𝐌𝐘 𝐄𝐃𝐆𝐄
I combine BPO delivery knowledge with enterprise SaaS and AI sales experience. I know where outsourcing works, where software works, and where hybrid models win. That perspective helps leaders modernize support without breaking operations or trust.

𝗦𝗣𝗘𝗖𝗜𝗔𝗟𝗜𝗧𝗜𝗘𝗦 - Enterprise Sales | SaaS Sales | AI Sales | Contact Center Outsourcing | CX Technology Sales | Go-To-Market Strategy | Pipeline Generation | New Logo Acquisition | Strategic Account Management | Account Expansion | Complex Deal Negotiation | Contract Negotiations | Revenue Growth | ARR & ACV Growth | Strategic Partnerships | Contact Center | Call Center Operations | Global Delivery Models | Onshore | Nearshore | Offshore | Workforce Management | Quality Assurance | SLA & Compliance | Conversational AI | Virtual Agents | Voice Bots | Agent Assist | QA Automation | Digital CX Platforms | Multichannel Support | Voice | Chat | Email | Back Office | Automation | RPA | Customer Experience | Customer Retention | Lead Generation | Inside Sales | Channel Partnerships

𝗖𝗢𝗡𝗧𝗔𝗖𝗧 - rondutta@hotmail.com | 832-497-4800

Ron Dutta

Valentine's Day hits different this year.

I need new friends. Talented people in the CX space who need their next role. BPO vendor managers. Customer experience leaders. People who deliver results.

If that's you, or someone you know, let's talk.

I'm not a recruiter. I'm someone who's been helped before and believes in paying it forward.

Here's how I help:

* Connect you with people in my network who are hiring
* Review your resume and share what worked for me
* Point you toward companies I know are growing
* Expect nothing back

Last week, a friend asked why I spend hours helping strangers find jobs.

The answer is simple. The CX community thrives when we help each other. Most people take. Few people give.

You know the type. They reach out when they need something. You help them. Then silence. They disappear until the next ask.

I'm building something different. A network where people show up for each other. Where helping someone doesn't require a return favor. Where talent meets opportunity through real connections.

I've helped 3 people land roles in the past year. Three of them now help others the same way. That's how movements start.

Drop a comment if you're searching or know someone who is. Share this with one person who needs to see it.

The CX world needs more givers.

Be one.

#jobsearch #payingitforward #careergrowth

Helping Brands Scale & Deliver Seamless Customer Experience ► Enterprise CX Sales & Business Development ⦿ BPO | Contact Centers | Automation & Outsourcing 🔸 Cost Down ➤ Scale Up ➤ Results Delivered ✅

You sent me a connection request.

I accepted.

Then you went quiet.

I wished you a happy birthday.

Nothing.

I congratulated you on your new role.

Nothing.

No sales pitch. No ask. Just a human acknowledging another human.

Here is what silence actually communicates.

You are collecting, not connecting.

A network built on numbers is not a network. It is a contacts list no one wants to be on.

The person you ignored today remembers.

People always do.

Tables turn faster than you think. That same person could be the hiring manager, the client, or the investor you need tomorrow.

If someone never came at you with a sales pitch, what stops you from responding to a simple congratulations or birthday wish?

#networking #relationships #leadbyexample

Traditional BPO charged per seat or per hour.

New leaders charge per successful resolution.

This changes everything.

Intercom, Sierra, and Decagon pioneered this shift.

BPO providers now own outcomes, not activities.

Companies pay for results, not effort.

Here's why this matters.

Per-seat pricing incentivized headcount. 
More agents meant more revenue.

Outcome-based pricing incentivizes efficiency. 
Better resolution means more revenue.

The alignment of incentives transforms the relationship.

Your BPO partner becomes invested in your success.

Not just task execution.

One e-commerce company switched to outcome-based pricing.

Their BPO provider deployed predictive analytics.

Flagged patterns 72 hours before customers complained.

Proactive outreach initiated. 
Issues were resolved before the calls happened.

Result?

Complaints dropped 15%. 
Satisfaction rose 20%. 
Average handle time decreased 28%.

The provider earned more revenue through better outcomes.

The company paid for value, not volume.

Both won.

But here's what this requires.

Transparent metrics. 
Real-time data access. 
Shared success criteria.

If your BPO partner can't access behavioral data, social listening, and predictive insights, outcome-based pricing doesn't work.

The shift from transactional to strategic partnerships depends on data infrastructure.

Are you ready to pay for outcomes instead of hours?

#bpo #cx #cxtransformation

𝗜 𝘄𝗮𝘁𝗰𝗵𝗲𝗱 𝗮 𝗕𝗣𝗢 𝗰𝗮𝗹𝗹 𝗰𝗲𝗻𝘁𝗲𝗿 𝗽𝗿𝗲𝘃𝗲𝗻𝘁 𝟴𝟰𝟳 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗰𝗼𝗺𝗽𝗹𝗮𝗶𝗻𝘁𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱.

Not solve them. 
Prevent them.

Here's how.

They deployed predictive analytics across their entire operation.

AI analyzed every customer interaction. 
Browsing behavior. 
Purchase history. 
Support tickets. 
Social media sentiment.

The system flagged patterns 72 hours before customers even thought about complaining.

A customer browsing refund policies three times in one week? 
Predictive alert triggered. 
Proactive outreach initiated. 
Issue resolved before the call happened.

The results?

Complaints dropped 15%. 
Satisfaction scores jumped 20%. 
Average handle time decreased 28%.

But here's what most BPO leaders miss.

This isn't about buying AI tools.

It's about shifting from reactive firefighting to proactive problem-solving.

Your contact center is sitting on mountains of data. 
Customer behavior patterns. 
Interaction histories. 
Sentiment trends.

Most of it goes unused.

The BPO providers winning right now treat data as their most valuable asset.

They invest in: 
Real-time analytics platforms 
AI models that learn from every interaction 
Social listening tools that catch issues before escalation 
Behavioral data integration across all touchpoints

The shift from vendor to strategic partner happens when you stop answering phones and start preventing problems.

Your customers don't want better reactive support.

They want you to know what they need before they ask.

What's stopping your team from going proactive?

#predictiveanalytics #bpo #ai

For two years, I sat in QBRs where the CSAT scores were green.

We celebrated. We reported up. We told the story of a program that was working.

Then someone pulled the retention data.

Customers were rating interactions 4 out of 5.

And not coming back.

I did not understand it at first. The scores said they were happy. The behavior said something else entirely.

Here is what I got wrong.

I was measuring how customers felt at the end of one conversation. I was calling that loyalty. Those are not the same thing.

A customer can feel good about a return interaction and still decide the brand is not worth the effort next time. A customer can rate a support call a 4 and quietly move their spend somewhere else. CSAT captures a moment. Loyalty is a pattern.

The thing that changed how I saw everything was a single Forrester data point.

Trust-driven customers generate 2.6 times more revenue than satisfied ones.

Not 10% more. 2.6 times more.

I had been optimizing for satisfaction when the money was in trust. And trust is built on completely different signals. Whether the brand did what it promised. Whether the policy felt fair. Whether getting a problem fixed required a fight.

None of that appeared in our post-interaction survey.

I rebuilt the measurement framework after that. It was uncomfortable to present because the new numbers were not as green.

But they were honest.

If your CSAT scores are green and your retention is flat, what are your scores actually measuring?

#csat #customercare #contactcenter

𝗙𝗶𝗿𝘀𝘁 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 𝗥𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮 𝗯𝗿𝗼𝗸𝗲𝗻 𝗺𝗲𝘁𝗿𝗶𝗰.

Companies measure it. Agents game it. Customers suffer.

Here's how it gets gamed:

• Agent marks ticket resolved before confirming with customer
• Customer calls back the next day with the same issue, counts as a new contact
• Complex problems get band-aid fixes to hit FCR targets

The result? Your dashboard looks green. Your customers are frustrated.

Better metrics to track:

• Customer effort score after resolution
• Repeat contact rate within 7 days
• Resolution quality audits on closed tickets

Stop measuring what's easy to track. Start measuring what your customers experience.

What metrics are you using to measure CX quality?

#firstcallresolution #cx #customerservice

Every AI deployment business case says the same thing.

Less volume. Fewer contacts. Lower cost.

Here is what CX Today found when they asked the people actually running these deployments.

Transactions will increase as a function of AI. Not decrease.

That came from a BPO industry roundtable published in late 2025. Multiple operators. 
Same observation.

Here is why.

When AI improves personalization, customers engage more. When resolution quality goes up, trust goes up. When trust goes up, customers contact you for things they previously gave up on.

IVR was supposed to reduce volume. It increased it.

Chatbots were supposed to reduce volume. They increased it.

Every time the industry made service more accessible, customers used it more.

The contact centers building capacity based on a volume-reduction assumption are going to be structurally underprepared for an environment where AI drives more engagement, not less.

The assumption in your AI business case is that customers will need you less.

What if the opposite is true?

#ai #cx #customerengagement

𝗔𝗜 𝗻𝗼𝘄 𝗵𝗮𝗻𝗱𝗹𝗲𝘀 𝘂𝗽 𝘁𝗼 𝟲𝟬% 𝗼𝗳 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗾𝘂𝗲𝗿𝗶𝗲𝘀 𝗶𝗻 𝗕𝗣𝗢 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀.

Response times dropped 40%.

First contact resolution improved.

So why are customer satisfaction scores flat in so many organizations?

Because deflection is not resolution.

Customers do not care how fast they get an answer. They care whether the answer solves their problem. When AI routes someone five times before reaching a human, speed becomes an insult.

The BPOs winning right now are not the ones with the highest automation rates. They are the ones who designed their AI to know its own limits, and hand off gracefully when it reaches them.

Automation without judgment is not efficiency. It is just a faster way to frustrate people.

Where have you seen AI handoffs done well?

#bpo #ai #cx

A customer service manager told me something shocking last week.

"We're not hiring humans anymore. We're managing AI agents."

68% of customer interactions will be handled by agentic AI by 2028.

Not assisted by AI. 
Handled by AI.

This isn't chatbots answering FAQs.

Agentic AI has memory. 
Task awareness. 
The ability to independently take actions with minimal human oversight.

One telco deployed agentic AI for customer support.

The system identifies coaching opportunities for agents. 
Builds customized learning journeys. 
Pushes micro-content over 12-15 weeks.

Result?

Performance improved across agents with similar profiles.

But here's what most companies miss.

Gartner predicts agentic AI will autonomously resolve 80% of common customer service issues by 2029.

30% reduction in operational costs.

Yet 95% of gen AI pilots deliver little to no P&L impact.

Why?

Companies rush implementation without change management. 
They automate before understanding workflows. 
They treat AI as a technology deployment, not an operational transformation.

The shift is from human-assisted AI to AI-assisted humans.

Your contact center strategy needs to change.

CX professionals should focus on complex problem-solving. 
Building deeper, trusted relationships. 
Providing humans-on-the-loop feedback.

89% of customers emphasize the need to combine human connection with AI efficiency.

This isn't about replacing humans.

It's about freeing them to do what AI cannot.

Empathy. 
Judgment. 
Trust-building.

81% predict vendors delivering agentic AI-led CX will gain a competitive edge.

The race is on.

Are you building AI that assists or AI that acts?

#cx #ai #contactcenter

A customer browses your site for 20 minutes.

Adds items to the cart. 
Drives to your store. 
Buys in person.

Your analytics mark it as cart abandonment.

Failed conversion. 
Wasted ad spend.

In reality, it drove the sale.

This attribution blindness is costing retailers $200 billion in misallocated marketing spend.

73% of consumers use multiple channels before buying.

Yet most retailers measure only single-channel conversions.

The result?

Millions invested in recommendation engines. 
Advanced data systems. 
AI-powered personalization.

Customers still see generic experiences.

Here's what most retailers miss.

A shopper discovers a product through AI recommendations online. 
Buys it in-store.

The online interaction gets marked as a failure.

This is the attribution problem killing retail.

The shift needs to happen from measuring touchpoints to measuring journeys.

Retailers winning in 2025 design journeys, not channels.

They measure cross-channel impact. 
Time personalization to customer intent. 
Unify data across every interaction.

H&M's AI chatbot recommends outfits based on style preferences.

McDonald's kiosks increased order size by 20%.

80% of consumers are more likely to buy from brands offering personalized experiences.

But personalization without infrastructure is theater.

You need unified customer profiles. 
Real-time data access. 
Context that travels with the customer across channels.

If your personalization breaks when customers switch from mobile to store, you're losing sales.

If your BPO partner can't see the full customer journey, they can't personalize service.

The future isn't more personalization tools.

It's an integrated personalization strategy.

Where does your attribution break down?

#retailcx #personalization #cx

𝗬𝗼𝘂𝗿 𝗕𝗣𝗢 𝗽𝗮𝗿𝘁𝗻𝗲𝗿 𝘁𝗼𝗹𝗱 𝘆𝗼𝘂 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴'𝘀 𝗳𝗶𝗻𝗲.

Meanwhile, 52% of your customers stopped buying because of bad experiences.

Here's the kicker.

90% of executives believe loyalty is growing. Only 40% of customers agree.

PwC calls this the loyalty illusion.

You're measuring what you want to see. Not what's happening.

Your quarterly surveys arrive 3 months too late. 
The customers already left.

The fix?

Real-time feedback loops. 
Map your full experience supply chain. 
Discovery to advocacy. Every touchpoint.

Price gets them in the door at 69%. 
What happens after the click determines if they stay.

And here's what kills retention.

74% of customers hate repeating themselves across channels.

Your BPO partner running siloed teams? 
You're bleeding revenue.

Give your outsourced teams full customer history. 
Zero repetition. 
Complete context.

Or watch your customers walk to competitors who do.

What's your biggest CX blind spot right now?

#cx #bpo #contactcenter

We were at an industry event.

He had been running his operation for 19 years. 8,000 seats across three countries. Solid contracts. Stable revenue.

I asked him what kept him up at night.

He did not hesitate.

"Andreessen Horowitz published something in 2025 about unbundling the BPO. I read it four times."

The a16z thesis was direct. Modern AI is now capable of productizing the work BPOs specialize in. Document processing. Data reconciliation. Knowledge search. Voice interactions that are nearly indistinguishable from human ones. The work that took large manual teams can now be brought back in-house by the client.

He told me what bothered him most was not the technology. It was the timing.

His clients were not asking about this yet. The conversations were still about SLAs and pricing. But he had seen enough industry cycles to know that the conversations clients are not having today are the ones that determine who is still in business in five years.

He had spent the last six months doing something counterintuitive.

Mapping every function his operation performed and asking honestly which ones would still require a human in 36 months. Not defending them. Mapping them.

What was left after that exercise became his investment roadmap.

The functions that required judgment, empathy, compliance expertise, and AI oversight. He was doubling down on those. Building specialist capability, he could price at a premium.

He told me the BPOs that survive this period will not be the ones that automate fastest. They will be the ones who figure out what automation cannot replace and get very good at it before everyone else figures out the same thing.

I think about that conversation every time someone asks me whether BPO has a future.

What would your investment roadmap look like if you mapped your operation the same way he did?

#ai #bpo #contactcenter #outsourcing

95% of customers want to know why AI made the decision it made.

Only 37% of companies currently offer any explanation at all.

That gap is not a technology problem. It is a trust problem.

Customers are not afraid of AI. They are afraid of opaque AI. They want to know why they got denied, why they were routed, and why the offer changed. When the answer is "the system decided," they disengage.

The Zendesk CX Trends 2026 report surveyed over 11,000 respondents across 22 countries. 63% said their demand for AI transparency has increased in the last year alone.

And yet 80% of CX leaders say transparency will be non-negotiable going forward, while only 37% have built any transparency into their current AI systems.

That is not a roadmap problem. That is a prioritization failure.

The companies that win customer trust in the next two years will not be the ones with the fastest AI. They will be the ones whose AI can explain itself.

What would it take for your AI to say "here is why" before a customer has to ask?

#cx #ai #customerservice

"𝗪𝗲 𝗿𝗲𝗱𝘂𝗰𝗲𝗱 𝗰𝗼𝘀𝘁 𝗽𝗲𝗿 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗯𝘆 𝟰𝟬%."

That number is real.

It is also incomplete.

Cost per interaction measures what it costs to handle a contact. It does not measure how many contacts your AI strategy is generating.

Here is the pattern spreading across organizations right now.

AI handles a higher percentage of contacts. Cost per interaction falls. Total contact volume rises because customers who got incomplete or wrong AI responses call back, escalate, or contact through a second channel.

The metric improved. The total cost did not.

McKinsey data from 2025 shows that 57% of customer care leaders expect call volumes to rise over the next two years. They are automating at the same time. The volume is rising because some of that automation is creating downstream contacts it was supposed to prevent.

Then there is AI sprawl.

A 2025 Zapier survey of 500 enterprise leaders found 28% of enterprises now use more than 10 different AI applications. 76% experienced at least one negative outcome from disconnected AI. 46.5% of workers switch between two or more AI tools to complete a single task.

In a contact center, that means agents navigating multiple interfaces during live calls, data failing to transfer between systems, and compliance blind spots forming where tools do not communicate.

The 2026 Contact Center Technology report is direct about it. Total operating costs rise where AI is deployed at scale without consolidation.

The organizations posting real ROI are not the ones that bought the most tools. They are the ones who reduced tool count, cleaned their data, and integrated before they automated.

What does your total cost of AI ownership actually include when you count all of it?

#cx #ai #contactcenter

Many teams chase metrics, tools, and channels.

Few fix root causes like resolution quality, effort, and speed.

Vote in the poll. Curious to see where you land.

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