For a CMO in financial services, the right AI move this year is a few small, measured pilots with guardrails in place, not a transformation programme. The board wants an AI story, vendors promise results by next quarter, and teams are already experimenting with a dozen tools.
In a category where trust is the product, the leaders who gain most are not the fastest adopters. They are the ones who know what to try first and what to ignore.
The examples below are illustrative scenarios, not client data. Swap in your own numbers and products.
Where to start
Start where the risk is low and the results are easy to measure.
- Start with the work nobody misses. First drafts, resizing, versioning, localisation across languages and markets, and report summaries. These give your team quick wins without putting the brand on the line.
- Example: A credit card team needs one cashback offer adapted into English, Hindi and Tamil across email, SMS and app banners. AI produces the first versions in an afternoon, the team edits them, and compliance reviews the final copy.
- Start with listening. Call-centre transcripts, app reviews, complaints and relationship-manager notes hold how customers actually talk about fees, onboarding and trust. AI is very good at spotting patterns in them, provided the data is anonymised and approved for use.
- Example: Run a month of anonymised call transcripts and app reviews through AI to cluster the complaints. You may find that confusion over joining-fee waivers drives more calls than any product issue, and a rewritten onboarding email can fix that.
- Start with one workflow and a baseline. Pick a process with a clear owner, such as adapting a card or wealth campaign across segments. Record how long it takes, what it costs and how long approvals take today, then compare after the pilot. Without a baseline, “AI saved us time” is an opinion.
- Example: Take the monthly wealth newsletter for HNI clients. Say it takes six working days from brief to send, with three review rounds. Use AI for the first draft and segment variants, then compare days and rounds after two cycles.
- Start by fixing your data. AI is only as good as what you feed it. Customer data, consent records, naming conventions and segment definitions (mass affluent, HNI, UHNI) need to be clean and consistent before you buy anything clever. It is unglamorous work, and it decides whether the rest succeeds.
- Example: If “HNI” means a ₹5 crore portfolio in one system and ₹2 crore in another, any AI-led segmentation will contradict itself. Agree one definition and clean up consent flags before the pilot starts.
What to skip
Five traps are worth avoiding, because each one costs time or trust.
- The tool-of-the-week chase. New tools launch constantly. Choose problems first, then tools. If you cannot state the problem in one sentence, you are not ready to buy.
- Example: A team signs up for separate AI tools for copy, images, video and analytics in one quarter, and nobody can say which problem each one solves. Better: state the problem first (“cut campaign adaptation time”), then pick one tool and test it.
- The AI strategy deck with no owner. A strategy without a budget, a metric and a named person accountable is just a presentation.
- Example: An “AI-first marketing” deck is approved at an offsite, and six months later nothing has a budget or a metric. Better: the head of marketing operations owns one pilot, with a budget and a monthly turnaround target.
- Full automation of customer-facing, regulated communication. Anything that quotes a rate, a return, a fee or an eligibility rule, or makes a claim, needs a human reviewer and compliance sign-off. Speed is not worth a compliance miss or a broken customer relationship.
- Example: An AI-generated ad promises “earn up to 8% returns” with no risk disclaimer, or quotes a rate that changed last week. A human reviewer and compliance sign-off catch it before it goes live.
- Vanity metrics. “We use AI in 40% of workflows” tells you nothing. Measure outcomes: cycle time, cost per output, conversion and customer satisfaction.
- Example: Instead of “AI is used in 40% of workflows”, report “campaign adaptation time fell from five days to two” or “cost per localised asset is down by a third”.
- Outsourcing your brand voice to the model. AI scales whatever point of view you give it. If you do not have one, it produces competent, generic content at speed. A useful test: could a competitor have produced this with the same prompt? If yes, you have made content, not a brand asset.
- Example: Ask AI for a “trusted, modern, customer-first” wealth tagline and two competitors will get near-identical lines. Better: give the model your voice guidelines, words you never use and copy you love, then have a person choose.
Put guardrails in before you scale
Agree the rules with compliance, legal, risk and information security before the pilots begin, not after. At a minimum, decide:
- What customer data may and may not be used in AI tools, and where that data is stored and processed
- Example: Anonymised call transcripts in an approved enterprise tool are allowed. Pasting customer names, account numbers or portfolio details into a public chatbot is not.
- Which content always needs human review, and which needs compliance approval as well
- Example: A festival greeting post needs marketing review. Any post that mentions rates, returns, fees or eligibility also needs compliance approval.
- How you will handle disclosure where AI plays a visible role in customer communication
- Example: A website chat assistant tells customers they are talking to an automated assistant and offers a route to a human.
- Which vendors are approved, and what happens to your workflow if one changes or disappears
- Example: Keep an approved-vendor list, a contract clause barring the vendor from training on your data, and a way to export your prompts and templates.
- Who is accountable when something goes wrong
- Example: Every AI-assisted workflow has a named owner, such as the head of campaigns, who signs off the output and answers for it.
In financial services, trust is the product. These guardrails protect it.
A simple 90-day shape
Three phases keep the effort small enough to learn from and structured enough to scale.
- Days 1 to 30: Pick two or three low-risk workflows, record baselines and agree the guardrails with compliance and risk.
- Example: Choose campaign adaptation, review-note summaries and customer-feedback tagging. Record today’s turnaround and cost for each, and get the data rules signed off by compliance.
- Days 31 to 60: Run the pilots, track results against the baselines and gather honest feedback from the team.
- Example: Run each pilot on two live campaigns, with a fortnightly 30-minute review where the team says what saved time and what needed rework.
- Days 61 to 90: Scale what worked, stop what did not, and decide explicitly where humans stay in the loop.
- Example: Scale campaign adaptation if it cut turnaround without adding compliance rework. Drop feedback tagging if it needed heavy manual correction. Record that all rate-related copy stays human-written.
Questions to ask before you buy any AI tool
- What specific problem does this solve, and how will we know it worked?
- Example: “Cut campaign adaptation time from five days to two with no rise in compliance rework” can be tested. “Be more innovative” cannot.
- What data does it need, where does that data go, and does it leave our environment?
- Example: Ask the vendor in writing whether prompts and uploads are stored, who can see them, and whether they are used to train models.
- Who owns the output, and who reviews it before it reaches a customer?
- Example: The tool drafts, the campaign manager edits, compliance approves, and an audit trail records each step.
- What happens to our workflow if the vendor changes terms or disappears?
- Example: Ask what happens to your saved templates and prompts if the vendor is acquired or shuts down, and whether you can export them.
The bottom line
A good AI playbook is less about the technology than about discipline. Start small, measure honestly, protect trust and keep your point of view. The CMOs who do that will get compounding returns, while the rest end up with a collection of tools.
