From 14% Efficiency to 24/7 Service: The New Automotive Customer Model

A large-scale field study by MIT and Stanford researchers (Brynjolfsson, Li & Raymond, 2023) analysed more than 5,000 customer service agents using generative AI tools.

The findings:

• 14% increase in productivity

• More queries handled per hour

• Faster handling times

• Improved resolution rates

• Higher customer satisfaction

This wasn’t theoretical.

It was measured in real operating environments.

You can read the full paper here

(Direct source: National Bureau of Economic Research)

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What 14% Productivity Actually Means

When translated into business terms, a 14% productivity gain becomes far more tangible.

Labour Efficiency

If a customer service function costs $1M annually:

→ A 14% improvement represents $140,000 in efficiency value

In practice, businesses don’t typically remove roles. They use this efficiency to:

• absorb growth

• reduce overtime

• avoid additional hires

Capacity Expansion

This is where AI customer service has the greatest impact.

A team handling 10,000 enquiries per month can now handle 11,400.

That is not just efficiency. It is expanded operational capacity at zero incremental cost.

Time Savings at Scale

AI reduces handling time and improves response consistency.

Across high-volume environments, this results in:

• faster resolution times

• improved service levels

• significant time savings across the workforce

The Most Important Insight From the Study

One of the most important findings is often overlooked:

AI disproportionately improves the performance of less experienced staff.

It effectively:

• accelerates learning

• embeds best practice

• reduces variability across teams

This creates a more consistent, scalable customer service operation.

Why This Matters Across Industries

In any customer service environment, the same issues exist:

• missed enquiries

• delayed responses

• inconsistent service quality

• casual staff

The problem is rarely demand. The problem is the ability to handle that demand effectively.

Applying This to Automotive

In automotive dealerships, these challenges are amplified.

Service departments deal with:

• high inbound call volumes

• limited staff capacity

• peak-time congestion

• missed booking opportunities

What This Unlocks (Across Both Sides of the Network)

For OEMs

• Greater consistency across dealer networks

• Reduced variability between high- and low-performing sites

• Faster onboarding across multiple locations

• Ability to scale “best practice” service delivery

For Dealerships

• Faster ramp-up of new service advisors and admin staff

• Reduced dependency on a small number of high performers

• Increased throughput without increasing headcount

• Less pressure on front-of-house teams handling repetitive enquiries

A 14% increase in capacity directly translates into:

• more bookings captured

• faster response times

• improved customer experience

Example:

A dealership managing 100 bookings per day:

→ +14% capacity = 114 bookings

If the average service value is $300–$400:

→ Over $1M in additional annual revenue potential

Importantly, this is not new demand.

It is existing demand that is currently being missed.

Where Ask Harry Fits

Ask Harry applies AI customer service specifically to the automotive environment.

It enables dealerships to:

• handle service bookings 24/7

• integrate directly with Dealer Management Systems

• reduce pressure on service advisors

Rather than replacing staff, it absorbs repetitive interactions, allowing teams to focus on higher-value work.

“In automotive, dealerships are currently missing up to 14% of customer demand and operational capacity, not due to a lack of enquiries, but because calls go unanswered, bookings aren’t captured, and teams are stretched across too many manual processes.” Kate Pullinger COO Contact Harald

The Businesses Seeing Results

The organisations seeing the greatest impact from AI customer service are not focused on cost-cutting.

They are focused on:

• capturing more demand

• scaling without increasing headcount

• improving consistency across interactions

• delivering faster, more reliable customer experiences

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Final Thought

The MIT and Stanford study validated that AI improves productivity.

But the real value lies beyond efficiency.

It lies in what that efficiency enables:

• more capacity

• more revenue

• better customer outcomes

In automotive, and across all customer service environments, the implication is clear:

AI is not just improving performance. It is unlocking demand that businesses are currently unable to capture.