Driving ROI from your AI investments

First things first. Don’t “Do AI”. And definitely don’t do it for the sake of doing it. Solve a Business Problem.

Most companies don’t need a full-fledged “AI strategy” powerpoint to get started. What they need is a business result — lower costs, more sales, faster delivery, higher margins.

Rather than overcomplicate it by finding a niche use case, designing the concept, running a pilot, and then assessing whether you made money or not (you can still do all that, just not initially!), put AI to work at the simple things - and see it save some money for you quickly. 

AI is not magic. But it is very good at doing a few things faster, cheaper, and more reliably than people. The key is to plug AI into a problem that’s already costing you money or slowing you down — and measure the impact in real terms: revenue, cost, output, time.

Here are five practical ways companies are making money with AI right now. No moonshots, no pilot programs that fizzle. Just tactics that deliver.

1. Automate Cost-Center Workflows — Start with Support and Finance

AI is best at repetitive, high-volume tasks. Think customer service tickets, invoice approvals, or status checks — the kinds of things companies pay humans to do at scale.

What to do:

• Deploy AI chatbots for first-tier customer support (Salesforce, Zendesk, Intercom)

• Use AI-powered accounts payable tools (Tipalti, Airbase) to automate invoice processing and reconciliation.

Why it works:

• Reduces headcount or external vendor costs.

• Improves response speed and accuracy.

• Available 24/7, scales instantly.

What you’ll see:

• Support volume deflection

• AP cycle time cut

• Real cost savings

2. Prioritize Revenue-Driving Work with AI in Sales and Marketing

AI can sharpen your sales and marketing efforts by telling your team where to spend time — and where not to. Companies using AI in sales pipelines see up to 50% higher lead conversion, per McKinsey.

What to do:

• Implement AI lead scoring (HubSpot Predictive Lead Scoring, Salesforce Einstein) to help reps focus only on high-conversion prospects.

• Use tools like Gong or Regie.ai to auto-generate call summaries, insights, and follow-up prompts.

Why it works:

• Cuts time spent on dead-end leads.

• Prepares reps faster, improves close rates.

• Reduces reliance on gut feel — better data, better decisions.

What you’ll see:

• Sales team efficiency up.

• Close rates improve.

• Shorter sales cycles.

3. Generate 10x More Content With the Same Team

Marketing is one of the clearest use cases for AI — not just for ideation, but for serious output at scale.

What to do:

• Use generative tools (Jasper, Writer) to create first drafts for SEO pages, email copy, product descriptions, etc.

• Set up a fast review loop — human editors polish AI drafts instead of writing from scratch.

Why it works:

• Cuts content production time

• Allows small teams to produce enterprise-level volume.

• SEO content and paid campaigns become easier to test at scale.

What you’ll see:

• Higher publishing frequency.

• More organic traffic, faster.

• Lower per-piece content cost.

4. Personalize the Customer Experience — Without Building a Data Science Team

Everyone talks about personalization, but AI makes it actually doable — even for smaller teams. Retailers using AI for personalization see a 6–10% boost in revenue, according to BCG.

What to do:

• Use AI to personalize site content, product recommendations, or pricing (e.g., Dynamic Yield, Mutiny).

• Automate behavioral-triggered emails and upsell flows with tools like Klaviyo or Iterable.

Why it works:

• Higher engagement and conversions.

• Better customer retention and repeat purchase rate.

• You get more out of the traffic and users you already have.

What you’ll see:

• Lift in CTR, AOV, and retention.

• More efficient CAC.

• Direct impact on revenue.

5. Unlock Insights Hidden in Internal Data

Every company has valuable data sitting around — customer emails, transcripts, inventory logs, churn reasons — that no one has time to analyze. AI can turn that pile into revenue-impacting insights.

What to do:

• Use LLMs (even ChatGPT) to summarize support tickets, find product issues, or surface repeated customer pain points.

• Analyze call transcripts for win/loss trends or compliance risk.

• Run sentiment analysis across reviews or chat logs to identify at-risk customers.

Why it works:

• Fast pattern recognition across massive text data sets.

• Surfaces root causes that humans miss.

• Informs product, UX, ops, and sales instantly.

What you’ll see:

• Faster decision-making.

• Fewer customer escalations or issues.

• Operational efficiency improvements.

Final Word: Skip the AI Pilots — Start with Real Problems

Success with AI doesn’t come from testing tools. It comes from fixing something real.

Ask these three questions before deploying anything:

1. What business metric am I trying to move?

2. What’s the manual or slow process today?

3. Can AI help me move that number — this week, not this year?

Don’t benchmark success by how much AI you’ve “adopted.”

Benchmark it by how much time, money, or revenue it helped you unlock.

AI is a means to an end. If it’s not solving a problem you already care about — don’t do it.

If you'd like to brainstorm on driving ROI from your AI investments, drop in a line to vinayvaswani@strivo.nl

Written by AI with prompts from Vinay Vaswani, for Strivo B.V.