SaaS CRO shares the workflows that are augmenting SDRs — but not replacing them
Discover how a SaaS CRO uses AI to augment SDRs, improve forecasting, and streamline revenue operations while keeping trust, coaching, and customer relationships human-led.
AI Integration: Introducing AI in SDR workflows significantly enhances account prioritization and customer outreach efficiency.
Human Element: Despite AI's advances, relationship management and trust remain vital components of successful revenue strategies.
Proactive Management: AI provides real-time insights and predictive triggers, enabling teams to act proactively instead of reactively.
Process Discipline: Data quality and consistent CRM updates are crucial for effective AI implementation in revenue operations.
Change Management: Successful AI adoption requires addressing team expectations and improving workflows to support human judgment.
Mukul Kaushik is CRO at a SaaS company called Trackier, and he also leads revenue for their products, Apptrove and Affnook.
We sat down with Mukul to learn how he's augmenting his SDRs with AI. Here's what he told us.
Expanding into new markets
Hi everyone, I’m Mukul Kaushik. I'm CRO at Trackier, and I lead revenue for our products, Apptrove and Affnook.
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We operate in performance marketing, mobile measurement, and affiliate technology, serving advertisers, ad networks, agencies, gaming companies, and app marketers globally. We’re currently in a strong growth and expansion stage, focused heavily on scaling internationally across regions like India, Southeast Asia, MENA, Europe, and the Americas. Our revenue model is primarily SaaS-based, with subscription and enterprise contracts depending on customer scale and usage.
The revenue organization covers sales, partnerships, customer success, and strategic growth initiatives. We operate with a mix of inbound, outbound, partner-led, and event-driven GTM strategies, especially as our industry relies heavily on relationships and ecosystems.
Our current focus is on combining traditional enterprise sales with AI-driven workflows for SDR functions, reporting, forecasting, and customer intelligence. At the same time, we’re maintaining a consultative, relationship-focused approach to growth.
My journey has focused on the performance marketing and mobile attribution space — working with advertisers, affiliates, gaming companies, and app marketers across different markets. I started in core sales and partnerships. Over time, I moved into building revenue teams, scaling global relationships, and shaping products based on customer needs.
A few years ago, revenue leaders based most decisions on experience, instinct, and significant manual effort. Now AI is changing the way we work almost every day — from prospecting and personalization to analytics, forecasting, and customer communication.
My biggest realization during this AI wave was how much faster small, focused teams can move when they use AI effectively. It’s no longer just about having a bigger sales team; it’s about having better systems, faster insights, and acting quickly.
However, I still believe revenue leadership ultimately centers on people. Relationships, trust, understanding customer pain points, and long-term partnerships still matter the most. AI can make us more efficient, but the human element builds lasting businesses.
How AI impacts outbound sales and revenue operations
We introduced AI into our outbound sales and revenue operations workflows last year. Previously, much of our process was manual — SDRs spent significant time researching accounts, drafting outreach, and updating Salesforce. They also prepared reports, and leadership reviews relied heavily on manually compiled data.
After integrating AI across multiple layers of the revenue process, it now helps SDRs prioritize accounts, identify intent signals, personalize outreach, and automate parts of follow-ups. Simultaneously, we automated much of the reporting within Salesforce, which provided cleaner, real-time visibility into deal movement and team activity.
Here's our AI-powered revenue workflow:
AI identifies and prioritizes high-intent accounts based on engagement signals and ICP matching.
It then helps SDRs personalize outreach and automate follow-ups.
Once leads enter the pipeline, Salesforce AI automates reporting and tracks deal activity in real time.
We receive predictive triggers when a deal slows down, engagement drops, or an account shows stronger buying intent.
Here are the tools we use:
Salesforce AI for forecasting
11x.ai for outreach
Sales Navigator for account indicators
Apollo for data enrichment
Predictive triggers and forecasting have had the biggest impact. Instead of waiting until the end of the quarter to realize a deal was slowing down, we now get early signals when engagement drops, response patterns change, or a deal starts showing risk indicators. Similarly, AI helps identify accounts that are more likely to convert based on historical behavior and activity patterns.
As a result, the team became much more proactive instead of reactive. Managers spend less time chasing updates and more time coaching teams and helping move deals forward. Forecasting conversations are now more data-driven rather than purely instinct-based.
What AI is unable to replicate in revenue operations
Mukul's Thoughts
A customer may buy because of product capabilities, but we usually build long-term partnerships through credibility, understanding business context, and human connection — something AI cannot fully replicate.
Today, we rely heavily on AI for areas involving pattern recognition, speed, and large-scale data analysis, but we still keep critical strategic decisions human-led.
Things like enterprise negotiations, pricing strategy for key accounts, long-term partnerships, hiring decisions, market positioning, and relationship management still rely heavily on human judgment and experience.
In our industry, especially, trust and relationships matter a lot. A customer may buy because of product capabilities, but we usually build long-term partnerships through credibility, understanding business context, and human connection — something AI cannot fully replicate.
Why autonomous SDRs are not yet possible
Another thing that AI can't handle yet is fully autonomous outbound sales and relationship management.
Initially, we were excited about AI SDRs handling large parts of prospecting and outreach independently. AI improved efficiency, personalization, and account prioritization. But in enterprise and partnership-led sales, especially in industries like performance marketing and gaming, relationships still drive conversions much more than automation alone.
For example, AI can generate highly optimized outreach, but it still struggles to understand deeper business context, timing sensitivity, and internal politics on the client side, or to build genuine trust over long sales cycles. In many cases, AI-generated communication also sounded too similar or overly polished, reducing authenticity.
Why process discipline is essential when using AI
We’ve seen both strong positives and a few learning curves as we integrated AI into our revenue workflows over the last year.
On the positive side, operational efficiency and visibility have been the biggest improvements. AI-assisted SDR workflows helped the team reduce time spent on repetitive research, outbound personalization, and follow-ups, improving overall outreach velocity and response handling. The sales team now spends more time in actual customer conversations instead of administrative work.
We also saw noticeable improvements in forecasting accuracy and pipeline management after automating reporting and introducing predictive signals in Salesforce. Earlier, pipeline reviews were manual and reactive. Now, leadership gets much earlier visibility into deal risks, stalled opportunities, engagement drops, and high-intent accounts. This has improved decision-making speed and made forecasting conversations much more data-backed.
Qualitatively, managers now focus more on coaching and strategy instead of chasing CRM hygiene and manual updates. The overall organization has become more proactive rather than reactive.
At the same time, we faced challenges. We realized early that AI is only as good as the quality of the data and processes behind it. In the beginning, inconsistent CRM updates and fragmented data sometimes created misleading signals or noisy forecasts. We realized that if CRM updates are inconsistent, teams do not maintain stages properly, or activity tracking is incomplete, AI simply amplifies those problems instead of fixing them. We had to improve process discipline before AI outputs became truly reliable.
We also found that AI tools sometimes create an illusion of productivity — more outreach, more reports, more automation — without necessarily improving actual revenue outcomes proportionally. So, we became much more intentional about measuring real business impact instead of just automation metrics.
And finally, we found that over-automation can sometimes make outreach feel robotic.
Why AI adoption requires change management
Adoption only succeeded when people understood that AI supports better execution, rather than replacing human judgment.
I also underestimated the amount of internal change management required. Teams initially saw AI either as a threat or as something that would magically solve everything automatically. In reality, adoption only succeeded when people understood that AI supports better execution, rather than replacing human judgment.
If I had known this earlier, we would have first spent more time improving CRM hygiene, clearly defining workflows, and training teams on how to work alongside AI. This would have avoided a lot of noisy reporting, unrealistic expectations, and early confusion about AI's actual impact.
Why revenue teams are changing
What I've learned is that scaling revenue no longer requires scaling headcount.
Today, a smaller, AI-enabled team can often execute faster and with better visibility than a much larger traditional sales organization. AI can handle much of the research, prioritization, reporting, forecasting, and workflow automation at a scale previously impossible.
Why CROs must keep teams human
The CROs who'll look back at this moment with pride aren't the ones who moved fastest. They're the ones who kept their teams human, were intentional about where AI belonged, and didn't confuse activity with momentum.
You've navigated disruption before. This disruption involves more tools, but the job is the same: build trust, solve real problems, and close.
