Product Leader at AI Governance Startup Shares the Agentic Workflows he’s Implementing to Streamline Delivery
AI Impact: AI is transforming the role of CX leaders from operational control to strategic orchestration and engagement.
Onboarding Innovations: AI-powered onboarding offers personalized, proactive guidance, improving customer activation and reducing churn.
Evolving Roles: Project management will shift towards AI-augmented workflows, emphasizing strategic design over task management.
Justin Chappell is a CX specialist at OneTrust and CSX Innovations, who is using AI to overhaul everything from customer experience to delivery leadership.
We sat down with Justin to get a sense of what he's changing and how it's impacting his businesses. Here's what he had to say.
Digital-first motion that accelerates time to value
I’m the Head of Digital Strategy, CX, and Operations at OneTrust. I oversee global customer experience strategy with a focus on Digital Customer Experience (DCX), Community, and Scaled Customer Success.
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I’m also the founder and CXO of CSX Innovations LLC, where I provide advisory services across Customer Success, Customer Experience, and Professional Services, as well as career coaching and mentorship for CS and CX professionals.
When it comes to project delivery, I specialize in building scalable, digital-first motions that accelerate time-to-value for customers. This includes leading initiatives such as AI-powered onboarding, proactive engagement models, and self-service hubs through our community platform — all while ensuring projects are executed with clear milestones, cross-functional alignment, and measurable impact on retention, expansion, and customer satisfaction.
How AI is changing the CX leader’s role
AI is changing my role from operational oversight and executional control toward being a strategic architect and enabler of AI-driven customer experiences.
Instead of making sure projects run, I'm ensuring that AI systems and people together deliver scalable impact that is measured in retention, expansion, and satisfaction.
And I'm spending less time on repetitive customer enablement, since AI-driven onboarding, chatbots, and contextual assistants now handle common training, FAQs, and setup guidance. That’s helped us reduce the time we spend coordinating repetitive enablement sessions.
We’re also spending less time firefighting, since AI-powered predictive models — including health scores, sentiment analysis, and predictive churn — surface risks proactively. That minimizes the time we spend reacting to escalations or late signals.
Why project managers are leaving the waterfall behind
Traditionally, project delivery relied heavily on rigid, waterfall-style methods with big upfront planning, detailed Gantt charts, and long reporting cycles. That model slowed responsiveness, especially in CX where priorities change quickly based on customer signals.
Moving to a lighter-weight approach allows us to scale and pivot quickly. And it accelerates time-to-value for customers by providing faster rollouts and quicker iteration.
We can scale our delivery capacity without adding headcount and keep teams focused on outcomes like retention, expansion, and satisfaction instead of process overhead.
Rethinking delivery rituals in an AI-powered workflow
This shift in making AI an extension of the team means I spend less time producing artifacts and more time on orchestration, decision-making, and coaching.
Delivery of traditional rituals is shorter, more dynamic, and more data-driven. The human side of setting vision, aligning stakeholders, and building trust remains central, but the mechanics are increasingly being handled by AI.
AI doesn't just automate — it reshapes work
The most surprising thing I’ve learned while adopting AI in delivery workflows is that AI doesn’t just automate; it changes how work gets defined in the first place.
I expected time savings, but what stood out was how AI reshaped decision-making, prioritization, and even customer expectations.
And because of that, my job evolved from managing delivery to ensuring that AI-driven systems deliver value at scale, while people focus on empathy, strategy, and innovation.
Agentic workflows for scaled delivery
I am actively experimenting with agentic AI workflows and orchestration platforms to streamline delivery. The focus has been on two core areas: AI-powered onboarding and scaled engagement orchestration.
We’ve prioritized these because they directly reduce time-to-value for customers, free up CSM capacity, and provide predictable impact on retention and expansion.
Agentic workflows in orchestration
We’ve been piloting to allow AI to not just automate tasks, but coordinate across the tech stack, making delivery workflows adaptive rather than rigid.
I’m finding that orchestration platforms are only as strong as the data foundations and cross-functional alignment behind them. We’ve had to double down on master data management and stakeholder buy-in to make the orchestration truly seamless.
Overall, the direction is promising — with AI shifting my role from managing delivery tasks to architecting systems of engagement and outcomes.
Agentic workflows in onboarding
We’re testing agentic AI copilots that dynamically guide customers through configuration steps, surface next-best actions, and flag adoption risks before they escalate. This reduces the need for manual intervention and ensures even long-tail customers get a high-quality experience.
Let’s dive deeper into this.
Digital-first onboarding at scale
When we launched our AI-powered onboarding, we replaced a manual, CSM-driven, one-size-fits-all process with a personalized, adaptive, and proactive experience. Instead of static playbooks, the onboarding journey dynamically adjusts to each customer’s role, goals, and pace.
Here’s the breakdown.
The challenge: Customers in the <$75K ARR segment were underserved because high-touch onboarding wasn’t scalable.
The approach: We introduced AI-driven journeys that surfaced next-best actions, adaptive tooltips, and personalized learning modules.
We monitored product telemetry for adoption signals, which automatically triggered nudges or resources.
Here are the key elements of AI-powered onboarding:
- Personalization at scale
- Proactive guidance and next-best actions
- Conversational and self-service journeys
- Adaptive milestones and progress tracking
- Closed-loop feedback and continuous learning
The outcome:
- Coverage became scalable.
- Time-to-value was cut significantly — customers activated key features faster.
- Churn was reduced.
- Satisfaction increased.
- Support tickets dropped as customers used contextual self-service.
- And CSMs were freed up to focus on enterprise accounts, while still providing a quality onboarding experience to long-tail customers.\
Where AI belongs — and where it doesn’t
Not everything can be taken over by AI. Here’s what’s ripe for automation and AI support:
- Onboarding and education
- Reporting and insights
- Customer outreach
Here’s what still demands a human touch:
- Strategic alignment and stakeholder buy-In
- Complex escalations and relationship management
AI means the end of project management as we know it
Within the next five years, project management as we know it will disappear.
AI will take over 80–90% of the traditional PM tasks like status reporting, scheduling, risk flagging, dependency tracking — even drafting project charters.
Delivery leaders won’t spend their time managing tasks; instead, they’ll curate, orchestrate, and coach.
AI copilots will continuously synthesize cross-functional data, predict bottlenecks before they occur, and automatically recommend next-best actions — essentially serving as the project manager of record.
Within the next five years, project management as we know it will disappear.
Teams will shift from chasing updates to making decisions in real time, guided by AI insights.
The human role will evolve into being strategic designers of outcomes and culture — by setting vision, aligning stakeholders, and bringing empathy and creativity where machines cannot.
In short, project delivery will evolve from being process-driven to AI-augmented and outcome-led, with leaders acting less like administrators and more like architects of adaptive, customer-centric ecosystems.
Learnings from adopting AI as a delivery leader
This shift isn’t theoretical — it’s already happening. And the job of the delivery leader is evolving fast. Here’s what I’ve learned:
- Start with business impact — Anchor your work in measurable outcomes like retention, expansion, and satisfaction.
- Think in systems — Don’t just add tools; design workflows that scale with minimal human coordination.
- Treat AI like a team member, not a tool — Let it handle the repeatable work so people can focus on empathy, strategy, and alignment.
- Start scrappy — Build the system, then iterate based on feedback and telemetry.
Start scrappy — Build the system, then iterate based on feedback and telemetry.
- Invest in cross-functional trust — The success of any workflow or automation depends on the alignment behind it.
- Level up your team — Help them shift from executing tasks to orchestrating outcomes.
Follow along
You can follow Justin’s work on LinkedIn and his website as he continues to design and scale AI-powered delivery systems. And check out the companies he’s doing it for: OneTrust and CSX Innovations.
More expert interviews to come on The Digital Project Manager!
