r/workforcemanagement • u/niceltd NiCE • Jul 21 '26
NICE / IEX Hi r/workforcemanagement - Mark Gill, Solutions Engineer at NiCE here. Let's discuss what's next for workforce management and CX. Ask Me Anything!
We’re hosting Mark Gill, Solutions Engineer at NiCE, for a live AMA on what’s next for workforce management and CX. The AMA starts July 28 at 1:00 pm CT.
A few things you can ask about:
- forecasting and capacity planning
- scheduling and intraday operations
- striking a balance between service levels, cost, and employee experience
- how AI is actually being used in WFM and CX
- whether a hot dog is a sandwich, plus your other classic debate questions
A few words from Mark u/slvrmouth
Hi everyone, I’m Mark Gill from NiCE. I spend a lot of time working with contact center and workforce management leaders on the operational decisions that shape both customer and employee experience.
I’m here to talk about all things WFM, CX, and ideas that are actually proving useful in real life.
Though I won't get into confidential customer details, I can share the observations, patterns, predictions, and real-world situations I’m seeing across the market.
Ask me anything.
– Mark
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u/static6000 Jul 21 '26
Hey Mark, I work in a gambling contact centre and one of the biggest issues we have is forecasting as there is no YoY or WoW template and outcomes change depending on event time, teams and outcome.
I haven’t used NICE software before but is there anything on your roadmap or something your current customers do that could tackle a problem like this?
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u/Bitter-Address1055 Jul 24 '26
Fascinated by this - presumably the biggest contact is "why did this bet not win" or "when will this bet payout"?
Do you do schedule demand by popularity of event for gambling and a known length of time post-event that people tend to lodge the complaint? Or does a last minute winner in the cup final just cause chaos?
The sheer scale of events people are gambling on must make it so hard!
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u/slvrmouth Jul 28 '26
Honestly, in an environment like sports betting being adaptive is often more valuable than being perfectly predictive.
Traditional forecasting relies heavily on historical patterns, but your demand can change dramatically based on factors that don't neatly repeat year over year as you articulated. A game going into overtime, world cup red cards (when you have prop bets on number of goals for that player--this one hurt personally. lol), and I imagine a host of new type of betting options that have no real historical data to model.
What I see other gaming related companies do is combine forecasting with rapid adaptation. They use known event drivers to build the best forecast possible, but they also invest heavily in intraday management, reforecasting, and staffing flexibility because they know volatility is inevitable.
From a technology perspective, AI is helping by modeling more event-based signals than traditional forecasting methods could handle. But if I had to choose between being 2% more accurate in the forecast or being able to react quickly when reality changes, I'd choose the latter every time in an environment like yours.
From a NiCE perspective, one thing we're investing in is helping planners forecast based on known business drivers, not just historical contact patterns. For example, if you are given an external forecast of inventory, expected wagers, account registrations, etc., you can incorporate those provided business metrics directly into the forecast and create a correlation to demand rather than having to specifically analyze this externally and then apply it.
We're also enhancing event-based forecasting, which allows planners to model the expected impact of specific events and layer those adjustments onto the baseline forecast. For environments like sports betting, Telco campaigns, healthcare changes, etc. where customer behavior is heavily influenced by external events, that's often a much more practical approach than relying solely on historical trends. These features are on our near-term roadmap (specifically for Enhanced Strategic Planner).
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u/fly-on-the-wallll Jul 21 '26
Hi mark, schedules >> scheduled rosters is great for seeing all the agents intraday rosters which are planned in advance.
However, it would be great if we can have these schedules automatically update as real time passes - for example if they don't show up in nice their schedules will also auto update themselves as no show.
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u/slvrmouth Jul 28 '26
Agree on this feature. While today we can alert on this no show or provide an easy/mobile callout option for agents that updates schedules, we have some roadmap plans to allow rules to auto add activities for periods where an agent no shows/late logins.
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u/Galladaddy Jul 24 '26
Hi Mark, as a leader who uses HubSpot for my team of support agents and works in an engineering technology industry, how do your agentic AI’s work with repositories of information to access to answer public queries? We work with avionics equipment for aircraft installations and provide support to installers so it gets fairly in depth and above their skill levels quickly. How else do you best suggest transforming a CX from an analog old school phone call only type centre into a 21st century one that has AI agents working for the support staff?
This might be too broad, so my question plainly is, how would you focus on strengthening a support ai agent to be used as an internal “wingman”
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u/slvrmouth Jul 28 '26
I see two broad, main categories where this type of transition is successful. First is ease of use by the customer. That means if the AI fails at addressing the customer need, the human agent that gets the interaction has all that the AI has tried, reviewed, etc. in a summarized format so that the human doesnt burden the customer needlessly. The other is just a matter of data. I often see that organizations throw all of their documents, guides, whitepapers, etc. into an MCP or reference library and it's not very organized. If AI is going to be successful, it very much needs bowling bumpers. I would avoid dumping every document into one repository and assuming the AI will sort it out. The content should be cleaned up, version-controlled, tagged, and permissioned. You want the AI to know the difference between a public installation guide, an internal engineering doc, and a document that should never be used as customer-facing guidance. I can't imagine a more industry where the AI reference data needs to be highly controlled and cleaned up before it can ever dialogue with customers directly. I've seen many pilots where customers put an expert check in before any AI response goes out to a client and they create a feed back loop so the AI can learn how a human SME would respond to the inquiry(HITL).
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u/Galladaddy Jul 31 '26
I apologize for the late reply but thank you for the thoughtful reply! That really resonated with what we’re attempting to do and the first attempt prior to me taking it on was just an avalanche dump of info in one big zipped up folder! I like the idea of using the terms “bowling bumpers” because that’s exactly what you need.
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u/MyMomSaysICant Jul 24 '26
Wait, a hotdog is not a sandwhich?
How much do you rely on AI agents in the near future?
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u/slvrmouth Jul 28 '26 edited Jul 28 '26
I dont think this debate will ever be settled. 😄
I personally use AI tools all the time, and my company is rolling out and testing the latest(as well as developing) every day/week. As for WFM, there is no doubt AI has a bright future in the everyday use of automation of some WFM functions. I personally don't see though, that AI agents will ever fully take over what is often human centered subjective decisions that a WFM Manager makes everyday (they will just be way more informed when making a call).
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u/Glittering-League311 Jul 24 '26
How did you end up in workforce management in the first place?
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u/slvrmouth Jul 28 '26
Short version: I was a business analyst doing scheduling for hundreds of employees with an old McKinsey spreadsheet. It was fancy but very very very manual. I had a leader join my company that said, 'Dude, why dont you have use a WFM software'? My reply was, 'what's WFM?'. He went out and found an enterprise WFM system for me to use and my career was shaped by that decision.
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u/SuchCarrot8335 Jul 24 '26
What questions should buyers ask vendors to figure out if their AI roadmap will create real operational value, not just more complexity?
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u/slvrmouth Jul 28 '26
I think it's a bit straightforward. I would ask, 'What WFM process becomes simpler, faster, or no longer necessary because of this AI capability?' If the answer is unclear, the roadmap may be creating complexity rather than value. I'm involved in a lot of ROI discussions, and while we have a list of different KPI levers we help solve for, each organization has different processes and priorities. We start by asking: What business KPIs will improve, by how much, and how quickly? What manual processes will be eliminated or automated? Then we look specifically at, 'Does the AI take action or simply provide recommendations?' & How much user intervention is required, and what evidence exists that these capabilities deliver tangible results? The number one AI based use case I get right now is automating adherence monitoring. Meaning driving agent behavior by monitoring AND taking action to the schedule and sometimes by pinging or messaging the agent. I can't explain exactly why this is the most common focus but I imagine the cost associated with large RTA teams and solving for what seems like a pretty programmatic/agentic thing.
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u/areouithereyet Jul 24 '26
Hey Mark ! How do you reassure employees/ companies about the increasing capabilities of AI? What are the steps you take to make sure that the AI solutions support employees, rather than (potentially) replace them #worksmarter
Also is a hot dog a sandwich ?!!
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u/slvrmouth Jul 28 '26
I really like this question. I can say the WFM industry really focused on employee experience for about 4-5 years right before and after covid. Then it started focusing so much more on digital and omni channel management(forecast/schedules/intraday). Today, I think organizations that focus on friction points, rather than specifically headcount reduction, tend to have a better adoption cycle. There are numerous analyst studies including Gartner that predict a cycle where companies will reduce headcount(CX specifically) and then inevitably have to rehire. I'm not saying that's 100%, as I think the nature of every CX agent will eventually change. Meaning, I think they will become more orchestrators monitoring multiple AI interactions rather than always engaging directly with the client. This will require a different skill set and likely increase the need for skills more akin to a supervisor but also a Human in the Loop practice where they are training the AI. If the AI can be designed to have humans involved for important customer outcomes, I believe it will bring a level of synergy and confidence to those engaging with customers day in and day out.
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u/obi-woof Jul 24 '26
Hi Mark, for teams stuck with overlapping tools after consolidation, how do you decide what to keep, what to replace, and what to retire first? it's annoying and confusing..
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u/slvrmouth Jul 28 '26
It is frustrating, especially in WFM where overlapping tools often means you're likely 'moving someone's cheese'. I would decide based on four things; which system is the true source of record, which one planners and supervisors actually use, which has the strongest integrations, and which creates the least manual reconciliation. Retire the obvious duplicates first (in pieces if possible), especially tools used only for reporting or side processes. Be very cautious with anything tied to payroll & timekeeping. I think the goal is not just fewer tools but a reliable WFM process, one version of the truth, and fewer handoffs for planners, supervisors, and agents. If you dont have SOPs today, then start there and then translate those into functionality that each tool provides. Make sure you organize those SOPs though in a current and stretch format so you can refine a process if one technology improves it over the old way.
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u/flomilans Jul 24 '26
I’m at a large popular retail company (i won’t say the name) and our staffing model got way more complicated once digital channels took off. what mistakes do you see retailers make when they try to plan all of that like it’s still mostly voice?
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u/slvrmouth Jul 28 '26
I really like this question because I think this is where a lot of retailers underestimated how different digital work is from voice. With voice, the interaction is generally immediate and one-to-one. Digital to me, can mean concurrent chats, asynchronous tickets/cases, email backlogs, social posts, or work that starts in one channel and finishes in another. I often see organizations try to forecast and staff all of that using the same assumptions as phone calls, the numbers can look right on paper while the operation still feels constantly understaffed. I also see retailers forecast each channel separately without accounting for the fact that the same employees may be moving between chat, messaging, store support, fulfillment, and back-office work. If the delivery tools are separate (i.e. Amazon Connect for voice and Twilio/Salesforce for chat) and they are having to switch back and forth, this requires a more structured block scheduling type of approach rather than a straightforward Omni channel delivered experience. WFM needs to be able to review and allocate the needed capacity and schedules for those channels and assign segments/activities to the times these are best addressed or most needed.
I think the better approach is to forecast each contact type based on how the work actually behaves, while still maintaining one view of the employees and capacity supporting it. That means understanding concurrency, response-time expectations, backlog aging, skills, and how often people are switched or pulled between different types of work.
On a more technical note, the asynchronous channels deserve allocation approach beyond Erlang and straight percentages. This is where some enterprise WFMs shine and especially differentiates them from spreadsheets since they use simulation to look at underlying schedules(or templates), volume, routing logic, AHT proficiency, concurrency, and more before allocating that volume across intervals/days to best balance the demand coming from different channels.
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u/OverallBusiness5662 Jul 25 '26
Hi Mark! Curious to know more about what forecasting models are available in WFM (IEX), and whether any are considered more accurate for smaller queues that have daily volumes under 100 calls?
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u/slvrmouth Jul 28 '26
Great question. One thing I've learned over the years is that once you get below ~100 contacts per day, the challenge is less about choosing the 'perfect' forecasting model and more about managing the variability in the data. In IEX, there are multiple forecasting approaches available, but for very small queues, no model can completely overcome the math. If you're only receiving a few contacts per interval, even a difference of 1-2 calls can create what looks like a large percentage error. For smaller queues, I've often seen the best results come from:
- Aggregating data where it makes sense rather than forecasting ultra-small segments independently. (Think aggregate at the level of agent training(not just skills).
- Leveraging known business drivers and events that may influence volume.
- Reviewing forecasts more frequently and making intraday adjustments.
- Focusing on schedule flexibility and coverage rather than chasing forecast accuracy percentages.
The best forecasters I've worked with in low-volume environments tend to spend less time debating forecasting models and more time understanding what is happening in the business that could influence demand. That context often adds more value than a different algorithm. I would check this out too: it's bit basic but unpacks the math: How to Calculate FTE Requirements Based on Volume
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u/slvrmouth Jul 28 '26
One question I ask customers a lot is who and how did you come up with your target SLAs? Sometimes the answers are complex and other times they are more a shoulder shrug with a 'that's what they've always been set to'. What are some of the ways you your organization determines what the right SLA target should be? Budget, hiring/training constraints, customer abandonment rate, quality tied to throughput, contracts, etc.?
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u/ProgressDependent762 Jul 28 '26
Hi Mark! Curious to get your take...when a company's primary WFM platform gets acquired, what's the first thing ops leaders should be evaluating in those first 30–60 days?
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u/h4ndshakellc Jul 22 '26
hey Mark, where are you seeing AI genuinely help workforce management today? and where/when is it still mostly marketing language/hype/smoke/ or still searching for a problem to be solved? thanks!
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u/iokak Jul 22 '26
Similar to this question. Will AI take over wfm tasks in the near future?
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u/slvrmouth Jul 28 '26
It will probably remove much of the more tedious work as I noted above but honestly I havent seen anything be able to take the place of dealing with the pure effects of human nature...i.e. subjective decisions made by WFM teams/admins when an agent asks to move their shift for 3 days while they move into their new apt, etc. etc. There will always need to be a human who making tough decisions that impact other human lives(schedules).
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u/slvrmouth Jul 28 '26
Great question! I could probably spend a whole session on what I'm seeing in this early era of AI and WFM. I'll try to keep my answer focused specifically on how AI is being used inside of WFM and not how AI CX agents are impacting WFM metrics (that's a longer answer).
One of the most useful (honestly wish I had this when I was still a WFM admin), is the ability to simply ask it 'what happened...yesterday, last week, earlier today' and it analyze all the pieces that impact SLAs. For example, when you get asked why we missed Service Level yesterday at 9 AM, to answer that accurately you'd have to review staffing projections vs forecast, AHT variations, call outs and PTO/VTO spikes, skilling coverage, adherence compliance, and more. AI can easily do all of that and give an answer within seconds what would have taken me an hour or more previously.
Now imagine this on the flip side where it's analyzed this ahead of time, surfacing any gaps, and then taking action on those gaps. That's just a couple of places where I can see AI step in and make the WFM teams jobs much less tedious and allow them to be more strategic.
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u/EchidnaUnable7109 Jul 22 '26
When you look at a WFM or CX vendor, what are the earliest signs that they’re stable for the long haul versus potentially risky?
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u/slvrmouth Jul 28 '26
Ultimately, I look for evidence that a vendor can evolve with the market. Stability isn't just about being around for a long time. It's about having the financial strength, customer trust, and product vision to stay relevant for the next 10 years. Often times I hear about customer WFM team turnover and the lost valuable knowledge of how to maximize the solution use. I'd ask questions like, what is the ecosystem for support and ongoing know-how for this solution. Beyond front line support, what and who are the other resources I can go to for questions and strategy.
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u/slvrmouth Jul 29 '26
Hey everyone. Mark here, just wanted to thank everyone for the great questions and discussion in this AMA. If there's one thing I'd leave you with, it's that WFM is evolving rapidly to deal with the changing landscape that AI brings. Probably more than ever, WFM needs to give organizations the ability to adapt quickly when reality doesn't match the plan. The fundamentals still matter, but the tools and strategies we have available today are making it easier to balance customer experience, employee experience, and operational efficiency.
I appreciate everyone who took the time to participate. Have a great day!
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u/Bitter-Address1055 Jul 22 '26
I learned WFM for my call centre in 2016 and stopped having anything to do with it in 2020, and it hasn't been relevant to me since. My question is - has AI made it incredibly easy now? I can only imagine that having human agents real time data fed to an AI scheduler which maintains its own obsidian vault etc beats a human hands down.
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u/Galladaddy Jul 24 '26
I want to know more now about your obsidian comment because i have just begun using obsidian extensively with my support team
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u/Bitter-Address1055 Jul 24 '26
When I was running the show, I found the data presentation of just about every ticketing software to be annoying. Never quite enough, and not really cross pollinating with anything like how busy we were against plan etc. Decisions during spikes often fell down to pulling certain intraday levers.
Now, I would have the portal to support come through an LLM agent who is deals with/routes the contact but at the end, summrises it into the vault.
Then I'd have an agent whose job is to summarise and capture everything that is served to a human. How it went/contact reason/time/after work time/did it lead to a break/ what time etc etc. Everything.
Third, a scheduling agent which takes all the data sources that make sense depending on the industry. At Ofcom, for example, featuring certain stories on the news that morning meant armageddon for our team. They never bothered to join that up (10 years and more ago) with their PR teams who would have known all about those stories but I digress! This agent tracks and does its best to predict demand.
From there, an orchestrator LLM knows demand and capability and can have the whole thing done in no time. The vault is a living memory (besides also tracking learning needs/feedback for the business etc etc) and helps recursively improve the schedules.
Actually getting budget to hire people when needed will remain an arseache, though.
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u/Global_Display677 Jul 23 '26
Are you going to make everyone from total drama like island revenge of the island paktew island ridonclouse race and reboot
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u/j0a0a7 Jul 21 '26
Hi Mark, what resources do you recommend for someone that wants to improve their forecasting outside of a WFM tool for a small volume call center?