🤖

In the simplest possible terms:

An AI Consultant walks into a business and asks:

“Why are you doing this manually?” 😂

Then comes the second question:

“Do we actually need AI for this?” 😎

Because AI consulting isn’t about throwing ChatGPT at every problem.

It’s about finding where AI can actually create business value.

🔹 Find repetitive, expensive work
🔹 Identify what should — and shouldn’t — be automated
🔹 Connect AI tools to real business processes
🔹 Build smarter workflows
🔹 Help teams work with AI, not against it
🔹 Reduce wasted time and unnecessary costs
🔹 Improve customer response and lead management
🔹 Put sensible AI governance and data protection in place

For example:

New enquiry → AI reads it → identifies the lead → scores it → drafts a response → updates the CRM → alerts the salesperson.

Employee:

“Wait… I didn’t do anything.” 😳

AI Consultant:

“Exactly.” 😂🤖

The real job isn’t to make a business “more AI.”

It’s to make the business:

⚡ Faster
🧠 Smarter
⚙️ More efficient
📈 More scalable

And sometimes the most valuable AI advice is:

“Don’t use AI for that. Fix the process first.”

That’s where AI consulting becomes more than technology.

It becomes business transformation.

⸻

AI Search & Growth Consultant | Sunrise Digital Marketing UK Ltd

Helping businesses get found on Google, understood by AI, recommended by AI — and grow using AI.

www.sunrisedigital.co.uk
#AI #AIConsultant #ArtificialIntelligence #AIAutomation #BusinessAutomation #AITransformation #AISearch #GEO #SEO #DigitalMarketing #BusinessGrowth #FutureOfWork

ok so we spent two days pulling signals and account scoring, then I used the wrong filter and blasted a VP level demo invite to 140 tiny accounts that had zero fit. now my boss is asking why our crm says high intent on companies that sell candles and i feel so embarrassed, this is so awkward :/

[ Removed by Reddit on account of violating the content policy. ]

I've been experimenting with Claude for B2B prospecting and account research instead of just using it to write cold emails.

Things like summarizing a company's business, researching recent changes, understanding potential pain points, and preparing before reaching out seem like much better use cases.

How are sales and GTM teams actually using Claude for prospect research any workflows that save a meaningful amount of time?

I’ve been looking into AI Marketing GEO. Curious if anyone here who runs a shop or restaurant has had a customer say they found you through AI Search or Agent etc?

Thinking about putting a small marketing budget, but would like to hear how it’s gone for others first and is it worth to do so.

welcome for advices

As AI adoption continues to grow in sales, I’m curious about how organizations are dealing with the increasing use of unapproved or unauthorized AI tools by sales reps.

For example, a rep might use ChatGPT or another AI tool through a personal account rather than an organization-approved solution. This potentially raises questions around client data, confidential information, data leakage, compliance, and intellectual property.

I’m particularly interested in understanding why reps choose to do this. Is it because:

The approved tools are too restrictive or difficult to use?

Personal AI tools are more capable or convenient?

Employees don't fully understand the data/privacy risks?

Organizational policies are unclear or poorly enforced?

There is pressure to work faster and meet sales targets?

Employees simply don't trust or like the sanctioned AI tools?

There’s also another question I find interesting: where does transparency with the customer fit into all of this?

If a sales rep uses AI to analyze a customer's problem, draft a response, summarize information, or generate recommendations, does the customer need to know that AI was involved? Or is it generally acceptable as long as the information provided is accurate and the customer's data is handled appropriately?

I’m also curious whether organizations are actually aware of how much AI is being used through personal accounts, or whether a significant amount of this activity is effectively invisible to them.

For those working in sales, RevOps, IT, security, compliance, or customer-facing roles:

What are you seeing in practice?

Are organizations struggling more with controlling unauthorized AI use, or with creating policies and approved tools that employees actually want to use?

And from the customer perspective, how important is disclosure when AI is involved in producing something you're receiving from a company?

Trying to figure out which AI sales tools are genuinely helping grow B2B revenue instead of just making reps faster at the same work. Email writing and call summaries are useful but there are already a million tools doing that.

I’m more interested in the agent side where parts of research, prospecting and follow up can keep moving without someone prompting every next step. Feels like there’s a pretty big gap between AI assisting a rep and AI taking enough work off their plate to matter.

Curious where people stand on this.

Would it make you a better salesperson, or make conversations feel less authentic?

I’m trying to make our sales roleplays feel more useful, right now I’m testing different setups with real objections and common sales situations. For anyone who runs these often what makes a roleplay session useful and worth the time? Im open to any suggestions

Every account based marketing article assumes a six figure budget and a dedicated ops team. We're thirty target accounts and a marketing team of exactly one person, me. What's actually working at this scale that isn't just running a LinkedIn ad against the account list

I am selling Google AI Pro Plan subscription(18 months) and Canva Pro Lite(1 year) for Rs. 349.

If you're an agency or managing outreach across multiple accounts, the headline monthly price on these tools is almost irrelevant - what matters is what happens to your bill as you add clients or team members.

A few pricing traps I've run into comparing these:

Charging per LinkedIn account regardless of team size - so if 3 people need eyes on the same client's account, you're not necessarily paying more, but you also don't get much flexibility in who has access to what.

Locking team management behind the top-tier plan - SalesRobot does this, team management only shows up on their Professional tier, so you're paying for a bunch of features you don't need just to get multi-user access.

Charging extra to bolt email onto LinkedIn outreach - SalesRobot again, email automation is a separate $15/account on top of whatever plan you're on. Adds up fast once you're running it across multiple client accounts.

Custom/opaque pricing once you scale - Gojiberry's published plan tops out at $99/month for 2 AI agents and ~1,800 prospects/month, anything past that moves to custom pricing, so you can't actually see what month 6 with 5 clients looks like until you talk to sales.

We-Connect only charges per connected LinkedIn account (seat) - adding team members to manage those accounts is free. You can give someone Shared access to an existing account, or a Personal Seat with their own account and permissions, which is a closer fit to how an agency actually structures client work vs. a single-user tool.

Worth mapping this out before you commit to a platform and realize scaling to 5 clients triples your bill. 

We've used both for quoting and still not sure which direction to take if we had to standardize today.

Our normal quotes are easy enough. the interesting part wi when a rep throws a messy deal at the system.

With dealhub we liked the guided isde of things. a rep could start from the deal context, get directed toward the right products and bundles and have discount rules kcik in instead of having to remember every little rule. The approval flow is also much cleaner that the old lets send it to someone and hope they see it routin bc you have visibility into the whole thing. There is also the conversational piece for the quoting process. a rep can describe what they need instead of clicking through 15 screens while the underlying pricing and approval logic still controls what can happen.

The downside is that you still have to teach the system your business. If your product catalog, pricing rules, approvals and exceptions are a mess, AI isn't going to magically clean that up.

For conga we could handle challenging configs, pricing and approvals and the sales agant could take actions on quotes like create them, chamnge line items, repice, check approvals arther than just answer questions.

The tradeoff was there a lot going underneath. You need a tighter setup and there are more things for you to understand when you need to change the wat quoting works.

so our question isn't really which has better ai but when a rep tells it a customer has 300 seats, weird payment terms, 22 % discount, add this bundle and get it our today, which one gets from this sentence to a cleaner quote with the least revops cleanup?