Target Just Named Its First Chief AI Officer. Who Owns AI at Your Dealership?

On August 11, Target announced the first chief AI officer in the company’s history. The same day, Cox Automotive published the first results of its new AI in Auto Retail Tracker, a survey of dealership decision-makers and in-market vehicle shoppers. Read together, the two announcements describe the same moment from opposite ends of retail: the biggest players are assigning AI to a named executive with a mandate and a budget, while most dealerships are still running AI with no owner, no baseline, and no reliable way to say what it is delivering.

Myth vs. data. The myth: buying AI tools is the same as having an AI strategy. The data: 82% of dealers now use AI in some capacity, but only 22% of those users report seeing sales or revenue growth from it so far — and about one in three either are not measuring AI’s impact at all or have no clarity on how they are measuring it (Cox Automotive AI in Auto Retail Tracker, August 2026).

Retail just made AI a named job

Target’s appointment is specific in ways that matter for anyone running a store. Chandhu Nair joins on August 24 as senior vice president and chief AI officer, reporting to chief information and product officer Prat Vemana. He arrives after more than six years at Lowe’s, most recently as senior vice president of stores, data, AI and innovation — a portfolio that pairs frontline operations with the technology behind them. Target announced a second move at the same time, promoting Purvi Shah to senior vice president of user experience, and framed the two roles as one system: intelligence, plus the design work that makes intelligence usable by employees and customers.

The context is a turnaround, not a victory lap. CIO Dive notes the appointment sits inside CEO Michael Fiddelke’s multiyear growth plan, which includes an incremental $2 billion in operational and capital investment in 2026, and follows a quarter in which Target’s net sales rose 6.7% year over year to $25.4 billion. Target has also already gone where AI-first shoppers are: it launched a Target app inside ChatGPT in November 2025.

This is not one company’s quirk. In IBM’s Institute for Business Value survey of 2,000 global CEOs, fielded February through April 2026, 76% of organizations reported having a chief AI officer, up from 26% a year earlier. Definitions and titles vary enough that the exact number deserves some caution, but the direction is hard to argue with: accountability for AI results is being assigned to specific people, rather than absorbed into everyone’s job in general and no one’s in particular.

Nair’s own framing is the part worth pinning to a dealership wall: “The measure of success won’t be how much AI we deploy.” What counts, he told Target’s newsroom, is the difference the technology makes for growth, for customers, and for the people doing the work.

The dealership picture: high adoption, thin accountability

Cox Automotive’s new tracker, which combines survey waves from the first and second quarters of 2026, establishes a baseline for how AI is actually going inside dealerships. The numbers describe an industry that has adopted quickly and instrumented slowly.

The usage pattern helps explain the distance between the 69% who expect revenue growth and the 22% who report seeing it. The three most common dealership applications — automating routine or complex tasks (40%), coordinating customer follow-up (40%), and generating content and creative (38%) — are internal efficiency plays, typically purchased department by department. Useful, but diffuse: when AI is everywhere in small doses, its impact shows up nowhere in particular on a financial statement.

Why AI without an owner stalls

Three mechanics, all visible in the tracker data, tend to compound at stores where nobody owns the AI portfolio.

First, tools arrive through fragmented purchasing. Marketing subscribes to a content generator, the BDC adds an appointment texting tool, service pilots a scheduling assistant, and a vendor bundles a chatbot into the website contract. Each decision is defensible on its own; nobody reconciles the overlap, the handoffs between tools, or the total spend. This is the pattern we described in our piece on the connected-AI execution gap, and it is the organizational opposite of what Target just did.

Second, there is no baseline, so there is no verdict. A store that never recorded its call answer rate, appointment set rate, or after-hours capture rate before deploying AI cannot say whether the tool changed anything. That is how a third of dealers end up unable to measure impact: not because the math is hard, but because nobody was assigned to do it before the contract was signed.

Third, the demand side is not waiting. The share of dealers who know they need to adjust to AI-powered vehicle search but have not started rose from 26% to 32% between survey waves — the gap is widening even as awareness grows. Shoppers keep moving regardless: 63% plan to use AI on their next purchase, and 24% say AI makes them feel more prepared when working with dealerships.

One more tracker finding deserves a careful read. Dealers working with an external AI partner were considerably more likely to say they use AI optimally (66% vs. 46%), to express high confidence in AI outputs (30% vs. 8%), and to report sales and revenue growth from AI (36% vs. 21%). Cox Automotive sells AI-powered products itself, so treat the framing with appropriate skepticism — but the pattern is consistent with the IBM data from outside automotive: results tend to follow when someone, internal or external, is explicitly responsible for producing them.

What owning AI looks like at a dealership

A 12-store group does not need a chief AI officer on the payroll, and a single store certainly does not. What both need is a scaled-down version of the same three things Target just bought: an owner, a scoreboard, and a cadence.

Name one owner. A general manager, operations director, or BDC director with real authority — someone whose job now includes an inventory of every AI touchpoint in the group, from website chat to the service line. The role is not technical. It is editorial: deciding what stays, what goes, and what gets measured.

Build a scoreboard, not a feelings check. Five numbers are enough to start, and the phone channel offers the cleanest ones: call answer rate, after-hours capture rate, appointment set rate from inbound calls, appointment show rate, and recovered missed calls. Record thirty days of baseline before any new tool goes live. The difference between the 22% of dealers who can point to growth and everyone else is rarely the tool itself; it is the before-and-after discipline.

Run a cadence. A monthly AI review with the same standing as a sales meeting, ending in kill, fix, or scale decisions for each tool. Vendors also respond differently to stores that show up with their own numbers — a dynamic we covered in how to judge dealership AI vendors.

Start where measurement is easiest: the phone

If the scoreboard idea feels abstract, start with the channel where every event already has a disposition. Calls are counted, recorded, timestamped, and tied to outcomes in the CRM, which makes the phone the fastest place to establish a genuine baseline — and the revenue leak there is well documented; see our earlier breakdown of what missed calls cost a dealership. Our look at phone-channel AI outcomes walks through what measured results look like once the discipline is in place.

The bottom line

Target’s chief AI officer is not a template for a dealership to copy literally. It is a signal about where operational AI is heading across retail: out of the experiment phase and into owned systems with quarterly numbers attached. The Cox Automotive tracker suggests most dealerships are currently on the other side of that shift — adopted but unmeasured, enthusiastic but unaccountable — at the same time as 63% of their customers are bringing AI to the purchase themselves.

The stores that close the gap will probably not be the ones with the most tools. They will be the ones where a named person can answer, with numbers, the question Nair was hired to answer for Target: what difference is this actually making?

If the phone is where you start measuring, purpose-built platforms can help — Carbuki’s AI voice agents report per-call outcomes such as appointments set and missed calls recovered, so the scoreboard builds itself. Details at carbuki.com.

Sources

• Target Corporate News (Aug. 11, 2026): Target Strengthens AI and UX Capabilities to Power Its Next Chapter of Growth
• CIO Dive, Nicole Laskowski (Aug. 11, 2026): Target taps Lowe’s executive as first chief AI officer
• Cox Automotive via PR Newswire (Aug. 11, 2026): New Cox Automotive AI in Auto Retail Tracker Finds Growing Gap Between Dealers and AI-Powered Car Shoppers
• IBM Institute for Business Value CEO Study (May 2026): CEOs are reshaping C-suite roles for the AI era
• Target Corporate News (Nov. 25, 2025): Meet Your New Shopping Buddy: The Target App in ChatGPT