There is a model of the AI modernization story that goes: construct the platform, then determine the use instances. Ankur Jain would let you know that is backwards — and that the majority organizations are studying that the arduous means.
Ankur is Chief Cloud and Information Modernization Officer at Acxiom, the linked knowledge and expertise basis that helps world manufacturers resolve buyer id throughout channels, enrich buyer profiles with greater than 10,000 attributes, and ship outcomes throughout buyer acquisition, retention and personalization.
Ankur leads each product engineering and client-facing options engineering — which means he’s accountable not only for what Acxiom builds, however for a way these capabilities get embedded contained in the environments the place shoppers truly function.
After becoming a member of the corporate lower than two years in the past, Ankur led the modernization of Acxiom’s core infrastructure, knowledge pipelines, legacy structure and underlying tech-stack. Right this moment, Acxiom is actively constructing agentic workflows that automate the total advertising and marketing worth chain.
Why the Basis Has to Come First
Aly McGue: A whole lot of organizations need to transfer to agentic AI however are nonetheless operating core workloads on legacy infrastructure. What’s the threat of attempting to construct intelligence on prime of a basis that wasn’t designed for it?
Ankur Jain: The chance is that you just hit a ceiling virtually instantly. After I joined Acxiom, each merchandise and shopper options have been hosted principally on-premises. When your merchandise and options are constrained to an information middle, they’ve restricted scalability. Efficiency was less than par for the real-time use instances shoppers have been asking for. After which there was a variety of legacy tech — the stack wanted a refresh, a reimagining of what cloud-native structure might appear to be.
What we additionally noticed was a variety of guide pipelines, a variety of knowledge redundancy, copies of the identical knowledge in a number of locations. The method itself was not very environment friendly. Any group attempting to construct agentic capabilities on a fragmented or legacy basis goes to spend extra time managing infrastructure than constructing merchandise.
For us, the strategic imaginative and prescient comes down to 2 north stars: knowledge modernization and agentic advertising and marketing. They’re sequential, not parallel. You can’t construct an agentic advertising and marketing ecosystem on a legacy basis.
How a knowledge warehouse migration shifted the main focus from upkeep to enterprise outcomes
Aly: You moved from on-premises Hadoop to Databricks. What did that shift make attainable that wasn’t attainable earlier than?
Ankur: By way of efficiency, we’ve seen enchancment throughout the board, throughout various kinds of workloads and various kinds of pipelines, virtually 80 to 90 p.c quicker run occasions. Workloads that used to take 50+ hours, generally 90+ hours — and I am speaking hours, so actually days, generally as much as per week — are actually getting finished inside 2-3 hours. Those self same workloads, in 2-3 hours.
It has additionally freed up our individuals. In some instances we’ve been capable of unlock a number of full-time roles to focus extra on value-added outcomes relatively than managing infrastructure. The primary factor it enabled was for the engineering group to focus extra on enterprise outcomes relatively than worrying concerning the infrastructure beneath. Which may sound like a gentle win, however when your engineers are spending their time constructing merchandise and delivering shopper options relatively than retaining the lights on, it adjustments what you may even try.
What the Agentic Advertising and marketing Worth Chain Really Appears Like
Aly: The place are you seeing agentic AI reshape precise advertising and marketing workflows immediately, and the place does that imaginative and prescient prolong?
Ankur: Acxiom’s core operation may be very data-centric. We usher in advertising and marketing knowledge from a number of platforms — CRM, e-commerce, Adobe Analytics, Google Analytics — and assist manufacturers construct a holistic buyer view, enrich it, and ship outcomes. Historically, that required a group of knowledge engineers and knowledge architects who would mannequin the whole lot and construct pipelines manually. ETL is at all times the longest pole within the tent, and it could take months.
By means of AI, that total cycle compresses. Code technology via prompts, automated testing of outputs, accelerated CI/CD pipelines. On the advertising and marketing aspect, producing totally different variations of an advert used to take inventive businesses months. Now you may analyze adverts at scale via machine studying, feed these outcomes into an AI engine and generate extremely custom-made variations in minutes.
The place we’ve seen the most important actual shift is on execution. Take viewers planning — a marketer passes a immediate describing a marketing campaign goal and goal profile, and the agent builds the viewers segments with pattern personas utilizing Acxiom knowledge, surfaces totally different demographic and behavioral dimensions and lets the marketer refine from there. What used to take effort from a number of individuals with different talent units and a variety of lead time is now finished agentically in minutes. We now have demonstrated the identical sample for media shopping for: an agent queries out there stock, evaluates it, makes a shopping for determination and prompts the audiences throughout channels.
The aim is to attach all the pipeline — from viewers design via media shopping for, activation and efficiency analytics — into an agentic framework. That entire AI for BI functionality that Databricks is constructing via the Genie and agentic ecosystem is precisely the place advertising and marketing workloads like ours are heading. It might probably all be put to work end-to-end.
How governance accelerates agentic workflows
Aly: Acxiom operates in extremely regulated industries, and deploying brokers requires a excessive degree of belief. How does that form the way in which you design governance into agentic workflows?
Ankur: The information we deal with spans PII, so each agentic workflow we construct begins with privateness as an architectural precept.
In follow, which means AI-generated content material by no means goes straight right into a stay marketing campaign. It routes via an approval workflow the place authorized critiques inventive and messaging earlier than something reaches a buyer. The brokers function inside outlined boundaries, with safety and privateness controls baked into the pipeline, and people keep within the loop at each determination level that carries regulatory or model threat. The aim is to not gradual issues down. It’s to verify velocity doesn’t come at the price of belief — for the client, the model or Acxiom.
Embedding AI into advertising and marketing merchandise and workflows
Aly: What does it imply for Acxiom’s merchandise to be AI-native, and the way does that change what shoppers truly expertise?
Ankur: AI-native means intelligence is embedded throughout all the advertising and marketing worth chain: ingesting first-party knowledge, resolving buyer id, enriching profiles with Acxiom’s knowledge property, constructing viewers segments, planning media buys, activating campaigns throughout channels and feeding efficiency analytics again into the subsequent cycle. Every of these steps can now be AI-driven relatively than manually orchestrated.
For shoppers, the most important change is transparency. Historically, a variety of what we offered operated as a black field. Manufacturers despatched knowledge in, outcomes got here again, and the logic in between was opaque. Now those self same capabilities might be delivered collaboratively, contained in the platforms shoppers already use, with full visibility into how selections are being made. That’s what shoppers are asking for: meet them the place they’re, function of their surroundings and make the method clear.
And it’s a forcing perform that comes not solely from throughout the group, however from our shoppers straight. They’re asking us: how are you going to make it more cost effective? How are you going to make it extra performant? How are you going to make it quicker? If you wish to reply these questions truthfully, you need to usher in AI.
Proprietary Information because the Aggressive Moat
Aly: Your knowledge property are core to what Acxiom sells. How is the way in which you ship that knowledge to shoppers evolving, and what does that unlock?
Ankur: Acxiom helps shoppers profit from their buyer knowledge. We assist them put it to work and monetize it. We offer knowledge property that manufacturers in any other case wouldn’t have, throughout automotive, retail, healthcare and pharmaceutical. Traditionally, delivering that knowledge was via conventional means — via SFTP. A model would request enrichment, we might enter right into a contract and ship the recordsdata. That was the outdated means.
Now we’re embedding our knowledge in an agentic vogue, both in our personal platforms or straight within the shopper’s surroundings. We companion with main martech platforms the place our knowledge property are natively out there. If a shopper is constructing their very own AI platform, we are able to combine agentically to allow them to make a name to our property and serve them up straight. We’re additionally growing clear room options in partnership with Databricks, the place shoppers can combine with Acxiom knowledge in a privacy-safe method inside their very own ecosystem.
The manufacturers we work with perceive that first-party knowledge is their Most worthy asset. Information privateness performs a vital function whereas dealing with and processing this knowledge. Manufacturers need to train larger management and are continually in-housing the advertising and marketing capabilities. The expectation is shifting for businesses to work inside manufacturers’ platforms and governance frameworks. The businesses that may function and ship outcomes natively into that surroundings will probably be indispensable.
Deal with It as a Basis Downside, Not a Instruments Downside
Aly: For those who have been chatting with a C-suite peer simply starting to scale their AI efforts, what is the one factor you’d need them to listen to?
Ankur: Be certain that the inspiration is stable. There’s a variety of AI buzz, which is not a buzz anymore; it is actuality. However what makes or breaks the entire AI initiative is the inspiration that it wants to take a seat on. In our case, shifting from on-premises to the cloud was not solely an ambition. Protecting the longer term in thoughts made it a necessity in order that we might be an actual participant within the AI journey. Stable knowledge basis, cloud-native structure, knowledge governance and safety — these are the important thing components. Any group that skips that step goes to search out out ultimately that it wasn’t non-compulsory.
The sample at Acxiom is a helpful body for any govt evaluating the place to place their vitality. Modernizing the inspiration and pursuing agentic AI aren’t two separate packages competing for funds and a focus. They’re the identical guess, made in sequence. Get the info layer proper, show worth via targeted pilots, then embed your differentiated capabilities the place shoppers really want them.
The shift Ankur describes — from delivering knowledge via file transfers to embedding intelligence natively inside shopper environments — is not simply an architectural improve. It adjustments what sort of firm Acxiom is. That sort of repositioning does not occur by bolting AI onto an on-premises stack. It requires the inspiration to come back first.
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