2026 Benefits Strategy: Use AI to Control Costs

Mercer is projecting health plan costs will rise 6.7% in 2026, pushing the average employer-sponsored health insurance cost to about $18,500 per employee. If you’re heading into open enrollment debriefs right now (mid-December is when most teams finally have clean numbers), that forecast should change how you plan benefits for next year.

Most companies respond to rising benefits costs the same way: increase employee cost-sharing, tighten eligibility, or quietly trim coverage. It’s understandable—and it’s also how you end up with angry employees, higher regrettable attrition, and managers dealing with “soft quits” that show up months later.

There’s a better way to approach this. AI in HR and workforce management can help you make benefits decisions that are both financially responsible and employee-aware: predict where costs will spike, spot which populations are at risk, and personalize programs so you’re not funding benefits nobody uses.

Benefits in 2026 won’t be won by who spends the most—it’ll be won by who targets spending with the least waste.

The 2026 benefits problem: costs up, expectations up

Answer first: In 2026, employers will face a cost squeeze driven by medical inflation and prescription drugs—while employees simultaneously demand benefits that fit real life (especially parents and caregivers).

The source article points to two big forces HR leaders can’t ignore:

• A healthcare affordability crunch for both employers and employees
• Prescription drug spending growth, including expensive GLP-1 weight-loss medications

Add the policy and labor dynamics that are brewing into 2026:

• More state-level activity around fertility coverage
• Continued tension around return-to-office mandates and flexibility
• Limited likelihood of meaningful federal paid leave expansion, meaning employers will keep carrying the load

Here’s the uncomfortable truth: you can’t “communicate” your way out of a benefits strategy that doesn’t match your workforce reality. But you can use AI-powered workforce analytics to understand that reality with more precision than surveys alone.

Where AI fits in benefits planning

Answer first: AI improves benefits planning by turning fragmented people and claims-adjacent data into practical decisions—who needs what, what’s being wasted, and what will cost more next quarter.

In practice, this often means:

• Forecasting utilization and cost drivers (by population, location, plan design)
• Identifying benefits “blind spots” (high need + low access)
• Personalizing navigation and communications so employees actually use what you already pay for

AI doesn’t replace your broker, consultant, or benefits team. It replaces the guesswork.

Prediction 1: Wellness benefits will get more proactive—and more measured

Answer first: Employers will shift wellness from “perk programs” to outcomes-based models, and AI helps measure outcomes without turning HR into an actuarial department.

The article predicts more employer coalitions and outcomes-based arrangements with health plans, PBMs, and providers. Translation: employers are going to demand proof.

That’s a good thing. For years, wellness programs have been funded on vibes:

• lots of vendors
• lots of portals
• not much behavior change

What “outcomes-based wellness” looks like in 2026

Expect more benefits leaders to ask:

• Did this program reduce diabetes risk scores?
• Did MSK support reduce surgeries?
• Did mental health access reduce leave duration?

How AI helps you run wellness like a business

AI can support proactive wellness in three practical ways:

1. Risk segmentation without stereotyping

   • Cluster employees by benefit needs signals (utilization patterns, leave events, job strain indicators) rather than demographics alone.

2. Program targeting

   • If only 9% of your workforce engages with a wellness app, AI can help identify which roles, shifts, or locations have barriers—and which channels actually work.

3. Early-warning indicators

   • Combine HRIS, absence, EAP engagement trends, and workload data to detect burnout risk earlier than your annual engagement survey.

A stance I’ll defend: if you can’t measure outcomes, it’s not a strategy—it’s a sponsorship.

Prediction 2: Fertility and parental benefits move to center stage

Answer first: Fertility and parental benefits will expand because state mandates and talent expectations are converging—and AI can help deliver these benefits fairly across a diverse workforce.

The article calls out a big catalyst: California’s mandate for large group insurance plans to cover certain fertility diagnoses and treatments. When a state like California moves, multi-state employers often standardize to reduce complexity and perceived inequity.

But here’s what HR teams underestimate: fertility and parental benefits don’t fail because they’re expensive. They fail because employees can’t navigate them.

The real friction: navigation, eligibility, and timing

Working parents and prospective parents typically face:

• confusing eligibility windows
• conflicting info between carrier, vendor, and HR
• missed deadlines during high-stress life events

How AI can improve parental benefits without adding headcount

Use AI as a benefits “concierge layer” that can:

• Answer policy questions consistently (within guardrails and HR-approved content)
• Provide personalized checklists (leave, childcare, lactation support, flexible work policies)
• Route complex cases to human specialists before something goes wrong

If you want one metric to track: time-to-resolution for benefits questions during parental leave events. Lower it, and your experience improves immediately.

Prediction 3: The ‘she-cession’ continues unless flexibility is real

Answer first: Return-to-office policies implemented without caregiver support will keep pushing women out of mid-to-senior roles, and AI can quantify the attrition risk before it hits your org chart.

The article cites a stark figure: half a million college-educated women at director and VP levels left the workforce in 2025 due to RTO mandates implemented without adequate care and support.

Whether your organization agrees with that framing or not, the workforce planning problem is obvious: losing experienced leaders isn’t just a DEI issue; it’s a capacity issue.

Use AI to connect benefits, flexibility, and retention

Most companies track turnover. Fewer connect turnover to policy triggers.

AI-driven workforce analytics can help you model questions like:

• Which departments saw retention drop after schedule changes?
• Which populations increased leave usage after RTO?
• Where are internal mobility pipelines thinning?

Practical step: build a “policy impact dashboard”

You don’t need invasive data. Start with what you already have:

• role level, location, manager org
• schedule policy (remote/hybrid/on-site)
• leave events and absence patterns
• regrettable attrition

Then look for discontinuities—sharp changes tied to a date or mandate. That’s where benefits and flexibility need to be redesigned together.

Prediction 4: Paid family leave stays employer-led

Answer first: Don’t wait for federal policy to save your paid leave strategy; in 2026 it’s on employers, and AI can make leave management more consistent, compliant, and humane.

When paid leave remains patchwork, HR teams end up building a custom experience per state, per employee type, sometimes per manager. That’s where mistakes happen:

• inconsistent approvals
• unclear pay calculations
• poor handoffs between HR, payroll, and managers

Where AI supports paid leave operations

AI helps most in the “middle” of the process:

• Intake triage: classify leave type, required documentation, deadlines
• Workflow automation: reminders, checklists, manager tasks, payroll triggers
• Consistency checks: flag exceptions (e.g., two employees in the same state treated differently)

This matters because paid leave is one of the most emotionally loaded benefits you administer. Errors don’t just create tickets; they create distrust.

Prediction 5: Employers will reduce coverage (explicitly or quietly)

Answer first: As premiums spike, many employers will shift costs to employees or reduce coverage—but AI can help you do it transparently and with less harm.

The article predicts a reality many benefits leaders are already modeling: spiking premiums will lead employers to provide less coverage or pass costs to workers.

Sometimes you have to adjust plan design. The question is whether you do it bluntly or intelligently.

Use AI to reduce “bad cuts” and protect high-value coverage

Smarter cost control means understanding:

• which plan features prevent larger downstream costs
• which employee groups will be disproportionately affected
• where navigation and steerage can reduce spend without reducing coverage

Examples of AI-supported moves that often beat across-the-board cuts:

• Identifying unnecessary out-of-network utilization and improving steerage
• Detecting pharmacy spend anomalies and improving prior authorization support
• Improving primary care access for specific sites/regions where ER use is high

A stance I’ll take: if your only lever is higher deductibles, your benefits strategy is under-instrumented.

A simple 30-60-90 plan for AI-powered benefits in 2026

Answer first: Start small: prioritize one cost driver and one employee experience pain point, then scale what proves value.

First 30 days: get the data usable

• Inventory systems: HRIS, payroll, benefits admin, leave, engagement, EAP utilization (aggregated)
• Define 5–7 metrics you’ll actually act on (e.g., cost per employee, high-cost claimant concentration, leave duration, benefits ticket volume)
• Set privacy boundaries and governance (who can see what, at what level)

Next 60 days: pick two “use cases,” not a platform

Choose one from each bucket:

Cost control use cases

• pharmacy cost trend monitoring
• provider steerage and navigation improvements
• wellness outcomes measurement

Experience / retention use cases

• parental benefits concierge
• leave management automation
• targeted communications by life stage and role context

By 90 days: prove value with a pilot

• Run a pilot in one business unit or location
• Measure before/after on 2–3 metrics (not 12)
• Document operational time saved (tickets avoided, cycle time reduced)

If you can’t define success in one sentence, the pilot will drift.

People also ask: what HR leaders are deciding right now

Should we cover GLP-1 medications in 2026?

Answer first: Decide based on outcomes and guardrails, not headlines—then use analytics to monitor utilization, adherence, and downstream cost impacts.

Are fertility benefits only for tech companies?

Answer first: No. Fertility and family-building support is increasingly a mainstream expectation, especially in competitive labor markets and states with expanding mandates.

Can AI personalize benefits without creeping employees out?

Answer first: Yes—if you use transparent, consent-based design, keep personalization at an appropriate level (role/life event), and avoid sensitive inference.

What to do next (and the question to bring to your next benefits meeting)

The 2026 predictions are clear: wellness will be judged on outcomes, parents will expect more support, flexibility will keep reshaping retention, and cost pressure will force plan changes. The only way to manage all of that without burning out your HR team is to build a benefits strategy that runs on data.

If you’re already investing in AI in HR—recruiting, performance analytics, workforce planning—benefits can’t be the holdout. Benefits is where cost, trust, and retention collide.

Next step: identify your biggest benefits cost driver for 2026 and your most painful employee benefits friction point, then map where AI can reduce waste or confusion.

One question worth ending on: If costs rise again in 2027, will your benefits strategy be smarter—or just smaller?