The Ortus Club · Moderator Reference
Moderating at
The Future of AI at Scale
Roundtable
AI-Powered Service in 2026: Staying Ahead of the Curve — scaling service without scaling headcount through sharper workflow design, cleaner data and real accountability across the client lifecycle.
TUE · 2 JUNE 2026
SMITH & WOLLENSKY · BGC
18 CONFIRMED ATTENDEES
SPONSOR · monday.com
00
Run of Show
Discussion window: 6:45 – 7:45 PMTopic & Framing
AI-Powered Service in 2026: Staying Ahead of the Curve
Client expectations are not just rising — they are accelerating. Real-time visibility, faster outcomes and consistent experiences are now the baseline. As delivery complexity grows, so do the cracks: misalignment between sales promises and operational reality, handovers that quietly lose momentum, performance that drifts before anyone notices. AI is moving fast from pilot to production — the honest question is where it is reducing friction, and where it is just adding another layer to manage.
- Scale vs pilot — what separates AI that scales into core service operations from initiatives stuck in pilot mode?
- Trust & control — how are organizations ensuring trust, control and quality as AI becomes embedded in customer-facing and internal workflows?
- Proving value — where is AI delivering the most tangible operational impact today, and how are leaders measuring and proving it?
Agenda
- 6:00 PMArrival of guests
- 6:30 PMWelcome address by The Ortus Club
- 6:40 PMShort address by monday.com
- 6:45 PMDiscussion instigated by the moderator and continued by the group
- 7:45 PMDiscussion brought to a close
01
Attendees
Confirmed list · ✦ pre-session responses received01
Rienzi Ramirez
Chief Financial Officer, Philippines
[24]7.ai
02
Jonathan Tam
Head, APAC People Services · Interim MD, PH Shared Service Center
Food Panda
03
Ryam Ganjehi
SVP Operations & Country Leader
Pinnacle Intelligence
04
Marc Dimen
Senior Director — Global PMO
Concentrix
05
Tiffany Lyn Aynera Reyes
Director of Program Management
Enshored
06
Sipin Sidharthan
Head of Operations
Eastvantage
07
Ricky Lee
Chief Technology Officer
Enshored
08
Nur Laminero
Integrator & Head of Angcars
Angkas
09
Leny Han
Director, Customer Success / Service Delivery
SupportNinja
10
Arnab Ray
Sr Vice President, APAC — Engage
TTEC
11
Johannes Elamparo
VP / Global Head, Operational Risk Management
Transcom
12
Jerico Bautista
AVP, Global Operations Data Analytics & AI Solutions
Straive
13
Martina Bugarin
Vice President — Country Operations
IMS People Possible
14
Vasanth Kumar
AVP — Technical Services Group (TSG)
HGS
15
Marjorie Delos Santos-Pimentel
Global Vice President of Operations
Support Services Group
16
Paul Rezaba
Director of Operations
GoVirtual365
17
Eric Ebia
Director for Operations
Infinit-O
18
Johanna Reyes
Head of RPO Operations
Emapta
Pre-session respondents not on the confirmed attendee list
✦
Ivy Rose Capco
Pre-session respondent — not on confirmed list
Conduent
✦
Andrew Nitafan
Pre-session respondent — not on confirmed list
Carenet Health
✦
Dinah Hernandez
Pre-session respondent — not on confirmed list
XMC INC.
01–10
Moderator Discussion Cards
Card Back ▾ moderator notes · Responses ▾ pre-session votesCard 01Opening FrameSet Tone
Moderator Prompt
We are past the question of whether AI works in service operations. This room already agrees on two things: the value lives in the workflow, and the winners over the next few years will be the ones who go AI-native. Yet the same room says implementations break down because we bolt AI onto processes we never redesigned. So tonight is not about the models. It is about the operating model — the data, the handoffs, the ownership and the trust that decide whether AI scales past the pilot.
Card Back — Use & Bridge
Moderator Notes
Objective: Move the room from generic AI commentary into concrete service-operations reality — this is a table of CX, BPO and shared-services operators, not a table of data scientists.
The productive tension to exploit: 11 of 15 say today’s value is internal workflow automation, and 9 of 15 say the #1 breakdown is applying AI without redesigning the process. Same belief, opposite outcome. That contradiction is the whole evening.
Bridge: “Let’s start with evidence. Where is AI actually earning its place in your operation today — and where is it still theatre?”
The productive tension to exploit: 11 of 15 say today’s value is internal workflow automation, and 9 of 15 say the #1 breakdown is applying AI without redesigning the process. Same belief, opposite outcome. That contradiction is the whole evening.
Bridge: “Let’s start with evidence. Where is AI actually earning its place in your operation today — and where is it still theatre?”
Pilot vs production · Internal-safe vs customer-facing · Cost-takeout vs top-line · Trust threshold
Card 02EQ1 · Value TodayImpact
Engagement Question
Where is AI actually proving its value in service operations today?
Card Back — Read the Room
Moderator Notes
Signal: Overwhelming — 11 of 15 chose internal workflow automation, not customer-facing AI. Only Tiffany (CX personalization) and Johannes (incident triage) broke from the pack; Ryam and Leny went to predictive.
Is internal automation where the value really is — or just where it is safest to deploy without client or compliance exposure?
Tiffany, Johannes, Ryam, Leny — what made you push past back-office automation into customer-facing or predictive territory?
Is the value assistive, or is AI influencing decisions agents actually act on?
Where automation stops at ‘assist’ · Client-facing risk appetite · Cost vs quality vs growth
Pre-session Responses15 responses
Where is AI actually proving its value in service operations today?
Pre-session vote · 15 responses
AIncident triage & resolution acceleration1
BCustomer experience personalization at scale1
CWorkflow automation across internal service operations11
DPredictive insights for proactive service delivery2
JE
Johannes Elamparo
Transcom
Incident triage & resolution acceleration
TA
Tiffany Lyn Aynera
Enshored
Customer experience personalization at scale
RR
Rienzi Ramirez
[24]7.ai
Workflow automation across internal service operations
JT
Jonathan Tam
Food Panda
Workflow automation across internal service operations
MD
Marc Dimen
Concentrix
Workflow automation across internal service operations
PR
Paul Rezaba
GoVirtual365
Workflow automation across internal service operations
VK
Vasanth Kumar
HGS
Workflow automation across internal service operations
MB
Martina Bugarin
IMS People Possible
Workflow automation across internal service operations
EE
Eric Ebia
Infinit-O
Workflow automation across internal service operations
IC
Ivy Rose Capco
Conduent
Workflow automation across internal service operations
JB
Jerico Bautista
Straive
Workflow automation across internal service operations
AN
Andrew Nitafan
Carenet Health
Workflow automation across internal service operations
DH
Dinah Hernandez
XMC INC.
Workflow automation across internal service operations
RG
Ryam Ganjehi
Pinnacle Intelligence
Predictive insights for proactive service delivery
LH
Leny Han
SupportNinja
Predictive insights for proactive service delivery
Card 03EQ2 · Scaling BlockerFoundations
Engagement Question
What is the biggest reason AI initiatives struggle to scale across the organization?
Card Back — The Fault Line
Moderator Notes
Signal: A near-even split — data foundations (6) vs ownership (5). That divide is the conversation. Only Rienzi flagged proving impact; three pointed at legacy integration.
Data camp (Jonathan, Ryam, Martina, Eric, Tiffany, Jerico): is it data quality, or data access — fragmented across client tenants you don’t fully control?
Ownership camp (Paul, Vasanth, Leny, Andrew, Dinah): who should own a cross-functional AI service workflow — IT, Ops, or the client?
Rienzi — you’re alone on ‘proving impact.’ Has the rest of the room stopped trying to prove ROI, or do they already have the numbers?
Multi-tenant data reality in BPO · RACI gaps across IT / Ops / client · ROI fatigue
Pre-session Responses15 responses
What is the biggest reason AI initiatives struggle to scale across the organization?
Pre-session vote · 15 responses
AFragmented or low-quality data foundations6
BLack of clear ownership across IT, Ops & Business5
CDifficulty proving measurable business impact1
DIntegration complexity with legacy systems3
JT
Jonathan Tam
Food Panda
Fragmented or low-quality data foundations
RG
Ryam Ganjehi
Pinnacle Intelligence
Fragmented or low-quality data foundations
MB
Martina Bugarin
IMS People Possible
Fragmented or low-quality data foundations
EE
Eric Ebia
Infinit-O
Fragmented or low-quality data foundations
TA
Tiffany Lyn Aynera
Enshored
Fragmented or low-quality data foundations
JB
Jerico Bautista
Straive
Fragmented or low-quality data foundations
PR
Paul Rezaba
GoVirtual365
Lack of clear ownership across IT, Ops & Business
VK
Vasanth Kumar
HGS
Lack of clear ownership across IT, Ops & Business
LH
Leny Han
SupportNinja
Lack of clear ownership across IT, Ops & Business
AN
Andrew Nitafan
Carenet Health
Lack of clear ownership across IT, Ops & Business
DH
Dinah Hernandez
XMC INC.
Lack of clear ownership across IT, Ops & Business
RR
Rienzi Ramirez
[24]7.ai
Difficulty proving measurable business impact
MD
Marc Dimen
Concentrix
Integration complexity with legacy systems
JE
Johannes Elamparo
Transcom
Integration complexity with legacy systems
IC
Ivy Rose Capco
Conduent
Integration complexity with legacy systems
Card 04EQ3 · Biggest ConcernTrust
Engagement Question
What concerns you most about scaling AI across service operations?
Card Back — The Human Edge
Moderator Notes
Signal: In a people business, the top anxiety is people — employee adoption and trust (7) just edges loss of control / governance (6). Experience consistency and impact measurement barely register.
Employee-trust camp (7): is the fear job displacement, or agents not trusting AI enough to act on its output?
Governance camp (6): whose governance — yours, or the client’s contractual and compliance regime?
Andrew’s own question lands right here: how do you deploy AI without your people hearing ‘you are being replaced’?
Change management as the real bottleneck · Internal vs client governance · Attrition & morale risk
Pre-session Responses15 responses
What concerns you most about scaling AI across service operations?
Pre-session vote · 15 responses
ALoss of control and governance6
BInconsistent customer or employee experience1
CEmployee adoption and trust7
DDifficulty measuring long-term business impact1
RR
Rienzi Ramirez
[24]7.ai
Loss of control and governance
MD
Marc Dimen
Concentrix
Loss of control and governance
MB
Martina Bugarin
IMS People Possible
Loss of control and governance
JE
Johannes Elamparo
Transcom
Loss of control and governance
TA
Tiffany Lyn Aynera
Enshored
Loss of control and governance
JB
Jerico Bautista
Straive
Loss of control and governance
VK
Vasanth Kumar
HGS
Inconsistent customer or employee experience
JT
Jonathan Tam
Food Panda
Employee adoption and trust
PR
Paul Rezaba
GoVirtual365
Employee adoption and trust
RG
Ryam Ganjehi
Pinnacle Intelligence
Employee adoption and trust
EE
Eric Ebia
Infinit-O
Employee adoption and trust
LH
Leny Han
SupportNinja
Employee adoption and trust
AN
Andrew Nitafan
Carenet Health
Employee adoption and trust
DH
Dinah Hernandez
XMC INC.
Employee adoption and trust
IC
Ivy Rose Capco
Conduent
Difficulty measuring long-term business impact
Card 05EQ4 · Breakdown PointProcess
Engagement Question
Where do AI implementations most commonly break down in day-to-day operations?
Card Back — Strongest Consensus
Moderator Notes
Signal: The clearest verdict of the night — 9 of 15 say AI breaks because it is applied to processes nobody redesigned. Nobody chose ‘limited visibility into AI decisions.’
If everyone in the room already knows this, why does it keep happening? Speed-to-deploy pressure? Client SLAs that freeze the process?
Dinah’s question is the sharp edge: which process would fail if AI were switched off tomorrow — and why was it never redesigned?
Misalignment camp (Martina, Tiffany, Ivy, Dinah): is frontline misalignment just a symptom of the same unredesigned-process problem?
Lift-and-shift automation vs reengineering · Who owns process redesign · SLA / contract rigidity
Pre-session Responses15 responses
Where do AI implementations most commonly break down in day-to-day operations?
Pre-session vote · 15 responses
AMisalignment between AI outputs & frontline execution4
BLimited visibility into AI decision-making0
CInefficient handoffs between humans & automated workflows2
DApplying AI without redesigning underlying processes9
MB
Martina Bugarin
IMS People Possible
Misalignment between AI outputs & frontline execution
TA
Tiffany Lyn Aynera
Enshored
Misalignment between AI outputs & frontline execution
IC
Ivy Rose Capco
Conduent
Misalignment between AI outputs & frontline execution
DH
Dinah Hernandez
XMC INC.
Misalignment between AI outputs & frontline execution
VK
Vasanth Kumar
HGS
Inefficient handoffs between humans & automated workflows
LH
Leny Han
SupportNinja
Inefficient handoffs between humans & automated workflows
RR
Rienzi Ramirez
[24]7.ai
Applying AI without redesigning underlying processes
JT
Jonathan Tam
Food Panda
Applying AI without redesigning underlying processes
MD
Marc Dimen
Concentrix
Applying AI without redesigning underlying processes
PR
Paul Rezaba
GoVirtual365
Applying AI without redesigning underlying processes
RG
Ryam Ganjehi
Pinnacle Intelligence
Applying AI without redesigning underlying processes
EE
Eric Ebia
Infinit-O
Applying AI without redesigning underlying processes
JE
Johannes Elamparo
Transcom
Applying AI without redesigning underlying processes
JB
Jerico Bautista
Straive
Applying AI without redesigning underlying processes
AN
Andrew Nitafan
Carenet Health
Applying AI without redesigning underlying processes
Card 06EQ5 · The Differentiator2–3 Years
Engagement Question
What will separate successful AI-powered service organizations from everyone else over the next 2–3 years?
Card Back — Where They Bet
Moderator Notes
Signal: AI-native workflows and processes leads (6); data, governance and adoption tie at 3 each. The room’s thesis is consistent — value today and the future both run through the workflow.
If you believe value and future are both workflow-led, what is actually stopping you getting there now?
The second factor splits three ways — which one (data, governance, adoption) is the binding constraint for your operation?
Tiffany’s question fits here: what did scaling teach you that the pilot never could?
Workflow-native vs workflow-bolted-on · The real second-order constraint · Capability boards underestimate
Pre-session Responses15 responses
What will separate successful AI-powered service organizations from everyone else over the next 2–3 years?
Pre-session vote · 15 responses
ABetter data foundations3
BStronger operational governance3
CAI-native workflows and processes6
DFaster organizational adoption3
JT
Jonathan Tam
Food Panda
Better data foundations
MB
Martina Bugarin
IMS People Possible
Better data foundations
EE
Eric Ebia
Infinit-O
Better data foundations
VK
Vasanth Kumar
HGS
Stronger operational governance
JE
Johannes Elamparo
Transcom
Stronger operational governance
TA
Tiffany Lyn Aynera
Enshored
Stronger operational governance
RR
Rienzi Ramirez
[24]7.ai
AI-native workflows and processes
PR
Paul Rezaba
GoVirtual365
AI-native workflows and processes
RG
Ryam Ganjehi
Pinnacle Intelligence
AI-native workflows and processes
IC
Ivy Rose Capco
Conduent
AI-native workflows and processes
JB
Jerico Bautista
Straive
AI-native workflows and processes
DH
Dinah Hernandez
XMC INC.
AI-native workflows and processes
MD
Marc Dimen
Concentrix
Faster organizational adoption
LH
Leny Han
SupportNinja
Faster organizational adoption
AN
Andrew Nitafan
Carenet Health
Faster organizational adoption
Card 07EQ6 · Open FloorQuestion Bank
Engagement Question
If you could ask all participants one question during the discussion, what would it be?
Card Back — How to Use
Moderator Notes
This is your participant-sourced question bank. Use it to redirect energy, surface proof, or break a lull — and hand the floor back to the person who asked.
The questions cluster into seven themes:
The questions cluster into seven themes:
Proof — Ryam, Marc, Leny want concrete success stories and top-line impact.
Scale friction — Jerico and Tiffany push on hidden cost and the pilot-to-production gap.
Human-centred — Paul and Andrew on protecting people through adoption.
Governance vs speed — Martina’s tension between deploy-fast and responsible-AI.
Hype & timing — Vasanth’s ‘when does the bubble burst’ — your contrarian fuse.
Resilience — Dinah’s ‘what fails if AI is switched off’ — a strong closer.
Hand the question back to its author · Use Vasanth’s and Dinah’s to provoke · Use Proof questions to lift a flat room
Participant Questions11 questions
Questions the room wants to put to each other
RG
Ryam Ganjehi Proof
Pinnacle Intelligence
What AI success stories can you share with the group?
MD
Marc Dimen Proof
Concentrix
Can you give an example of how AI transformed your organization — or a section of it — in the last 12 months?
LH
Leny Han Proof
SupportNinja
How did you leverage AI to impact your top-line numbers?
JB
Jerico Bautista Scale Friction
Straive
Moving from isolated pilots into core, enterprise-scale production — what has been your single biggest unbudgeted operational cost or unexpected friction point, and how did you solve it?
TA
Tiffany Lyn Aynera Scale Friction
Enshored
What is the single most counterintuitive lesson you have learned bridging the gap between a successful AI prototype and enterprise-wide scale?
PR
Paul Rezaba Human-Centred
GoVirtual365
How can organizations balance AI automation with a human-centred customer and employee experience as adoption scales?
AN
Andrew Nitafan Human-Centred
Carenet Health
How has AI helped you without making employees feel it will replace what they do?
MB
Martina Bugarin Governance
IMS People Possible
How should organizations balance the pressure to deploy AI quickly against the growing need for governance, security and responsible-AI practices?
VK
Vasanth Kumar Hype & Timing
HGS
When do you think the AI bubble will burst — or slow down to rationalise?
RR
Rienzi Ramirez Agentic
[24]7.ai
What are the known successful agentic-AI deployments you have seen offshore?
DH
Dinah Hernandez Resilience
XMC INC.
Which end-to-end process in your organization would still fail or degrade if AI were switched off tomorrow — and why has it not been redesigned yet?
Card 08Sponsor Bridgemonday.com
Moderator Prompt
When you redesign a service workflow for AI — not just automate the old one — what does the work-management layer underneath it actually need to do that your current tooling doesn’t?
Card Back — Why It Fits
Moderator Notes
Link: The room’s own data hands monday.com the bridge. Value today = workflow automation; the 2–3-year differentiator = AI-native workflows; the #1 breakdown = processes never redesigned; the live blockers = ownership gaps and human-to-AI handoffs. That is precisely the work-management and service-operations problem space.
Do say: “This is sounding like a workflow-design, ownership and visibility problem as much as an AI problem.”
Let operators describe the gap first — cross-functional ownership, handoff orchestration, real-time visibility across the client lifecycle — then let monday.com map to it.
Avoid: Turning the table into a product pitch. Keep it operator-led.
Do say: “This is sounding like a workflow-design, ownership and visibility problem as much as an AI problem.”
Let operators describe the gap first — cross-functional ownership, handoff orchestration, real-time visibility across the client lifecycle — then let monday.com map to it.
Avoid: Turning the table into a product pitch. Keep it operator-led.
Card 09ContrarianChallenge
Moderator Prompt
What are we collectively overestimating about AI in service operations right now — and what is quietly working that nobody is talking about?
Card Back — Follow-ups
Moderator Notes
Seed: Vasanth already asked when the AI bubble bursts. Light that fuse deliberately once the room is warm — it gives the sceptics permission to be honest.
Where does the slide deck look great but the floor operation still struggles?
If budgets tightened tomorrow, which AI initiative would you cut first — and what does that tell you about its real value?
Which ‘agentic’ claims have you seen survive contact with a live SLA?
Pilot theatre · Sunk-cost initiatives · The unglamorous things that actually work
Card 10Future-StateClose
Closing Line
The next phase of advantage in service operations will not come from better models. It will come from redesigned workflows, clean data across every client tenant, clear ownership of the human-to-AI handoff, and the trust to let AI act — not just advise.
Card Back — Land the Arc
Moderator Notes
Tie the evening together: the room agreed the value is in the workflow and the winners go AI-native — yet the breakdowns come from bolting AI onto processes nobody redesigned, and the deepest fear was always about people, not technology.
Last question round the table: what is the one capability you will invest in first — data, process redesign, governance, or your people’s trust?
Where each leader will spend next · What boards still underestimate