What Is an AI BDR, How Does Intent Data Work, and Should You Trust a G2 Rating? A Procurement Manager's FAQ

Every few months, a sales manager drops a tool link in my inbox. "Can we get this?" From the outside, AI SDR platforms look like a button you press that books meetings. The reality is messier—and more useful once you understand it. I'm the procurement manager at a 120-person B2B SaaS company. I've managed our sales tech budget for four years, tracked every invoice, and evaluated a dozen tools. These are the questions our team keeps asking, answered the way I wish vendors would answer them. Consider this a pre-pitch briefing: here's what matters before the demo starts.

1. What does an AI BDR actually do in outbound sales automation?

An AI BDR automates the workflow a human SDR runs daily: building prospect lists, verifying emails, writing personalized icebreakers, sending follow-ups, and logging activity into the CRM. Not just email blasting, I should add. The platforms worth considering—Artisan's Ava included—chain that entire loop together, so a rep wakes up to a queue of conversations already moving.

The part that surprised me is how much of it is data work. Email verification, contact enrichment, dedupe. You can't automate outreach that lands if the addresses bounce. That's why I asked about the data source first. A 300M-contact database with regular cleansing beats any AI copywriting engine. The two-way HubSpot/Salesforce sync—not the templates—is what convinced our ops team. It meant no more exporting CSVs and cleaning them up in spreadsheets.

The word "automation" hides the real cost question, though. Automating a broken process just breaks things faster.

2. Is a sales intelligence platform worth the cost?

A sales intelligence platform is a contact database with layers on top: firmographic filters, intent signals, enrichment, and verification. The short version: yes, it's worth it—if you're already paying for data tools separately. That's the TCO trick.

Before we consolidated, we ran a list vendor, an enrichment tool, and a verification service. Three invoices, three renewals, three contracts to fight about. Moving to one platform put everything on a single line item. Why does that matter? Because when renewal season comes, you negotiate once, not three times. Funny thing: the consolidated price was slightly higher than one of the three tools by itself. Compared to the sum of all three? We cut the stack cost by roughly a third.

The mistake is buying one on top of your existing stack. I see teams double-paying for overlapping data. Run a 50-account overlap test against your current database before you sign. The results are eye-opening. It's tempting to think the biggest dataset wins. But accuracy and verification beat raw size when you're paying for both.

3. How does intent data actually work?

Intent data tracks behavioral signals suggesting a company is researching a problem. People at the account searching for specific keywords, reading certain content, visiting pricing pages, or engaging with review sites—observed anonymously, tied to an IP, and mapped to a company.

Mechanically, it's a cooperative data network. When the engagement score crosses a threshold, the platform flags the account as "in-market." Sounds simple. The nuance is that intent is directional, not a conclusion. It tells you who's looking, not who's buying. (As of 2025, the signal quality was still noisy in our tests.)

So treat high intent as a cue for outreach, not a script for a close. We filter intent data by persona fit first. Buyer intent matters less if the company doesn't match our ICP. The main value of intent data isn't the signal itself—it's prioritization. That's what earns it a budget line. Also, check how the vendor bills it. Some charge per credit, others per profile, others flat-rate. Same data can cost 2x depending on the meter.

4. What is a LinkedIn Sales Navigator scraper, and when should a B2B sales team use it?

A Sales Navigator scraper extracts prospect profiles from LinkedIn Sales Navigator search results—names, titles, companies, sometimes guessed email patterns. Teams use it to build lists faster than LinkedIn's native export limits allow.

When should you use one? Honestly, rarely—at least not as a core strategy. We tested one on a 500-prospect campaign. Data quality was rough, deliverability was worse, and we dropped it after a quarter. The tool wasn't the problem; the list quality was. We burned more time verifying contacts than we saved building them.

It has one narrow use case: fast list validation. If you need to map decision-makers at 50 target accounts in a day and you have a verification layer downstream, scraping can shortcut the research phase.

The better default, in my opinion, is a platform with a maintained database—where deliverability and cleanliness are features, not afterthoughts. With Artisan, we stopped needing the scraper altogether. Ask any vendor: "Where does your data come from?" The answer tells you more than the demo.

5. What should you trust about an AI sales agent's G2 rating?

Start here: per FTC advertising guidelines (ftc.gov), testimonials have to be genuine, and claims need substantiation. So the reviews behind a G2 score are real experiences. But real experiences aren't automatically representative ones.

Three things I check before trusting any rating:

• Review count. A 4.9 with 34 reviews tells you less than a 4.4 with 600.
• Middle reviews. The four-star and three-star reviews show the pattern. Five-star reviews just confirm the product can work.
• Category fit. A rating in "AI Sales Development" can't be compared with one in "Sales Engagement." Different metrics.

For Artisan's rating—and I'd tell you to check the G2 page yourself rather than trust my memory—what mattered to me was the review distribution. Most reviewers came from mid-market sales teams, close to our segment. That's the signal I trust. G2 alone never decides a purchase for us. It's a screen, not a verdict.

6. Why buy an AI SDR instead of hiring a human SDR?

I went back and forth on this one for two months. Hire or automate? When I modeled our costs, a single SDR landed near $82,000 fully loaded: base, variable comp, benefits, tools, onboarding time. The AI SDR quotes we collected from seven vendors earlier this year ranged from roughly $800 to $4,000 per month, depending on contact volume. That spread matters: the low end quoted basic sequences only; actual use put us in the mid-range. The math doesn't just lean toward AI—it sprints.

But here's the part no demo slide shows: the AI doesn't replace judgment. Your best SDRs read context, build relationships, and handle the nuanced conversations an automated sequence can't. The winning setup is hybrid. The AI handles volume, data hygiene, and the first two touches. Humans pick up the conversation the moment it gets interesting.

The savings only materialize if you reinvest the freed capacity into actual talking. Skip that step, and you're just paying less to get less.

7. What hidden costs come with adding an AI SDR?

Nobody puts this in the proposal. The real cost of an AI SDR lands in four places:

Setup and integration. Connecting the AI to your CRM, cleaning existing data, mapping fields. We spent close to two weeks untangling duplicates before our first sequence ran. (circa Q3 2025—modern integrations are faster now.)

Sequence management. Someone has to monitor replies and catch misfires. Budget a few hours a week from your sales ops person.

Data quality review. The underlying contact database is strong at onboarding, but your ICP shifts. Monthly list reviews are non-negotiable.

Overage fees. Some platforms charge per contact sequence or per booked meeting. We nearly hit a $2,400 overage in November because I missed a credit limit buried in the contract.

Add it up: real TCO lands roughly 40% over the base subscription. Still cheaper than a hire—but not as cheap as slide one implies. Plan for it, and it's a great tool. Ignore it, and the invoice starts the conversation. That's the advice I give every sales manager before they forward me another link.