There is a model of the AI modernization story that goes: construct the platform, then determine the use circumstances. Ankur Jain would inform you that is backwards — and that almost all organizations are studying that the onerous method.
Ankur is Chief Cloud and Information Modernization Officer at Acxiom, the linked knowledge and know-how 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 — that means he’s accountable not only for what Acxiom builds, however for the 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. In the present day, Acxiom is actively constructing agentic workflows that automate the complete advertising worth chain.
Why the Basis Has to Come First
Aly McGue: Loads of organizations wish to transfer to agentic AI however are nonetheless operating core workloads on legacy infrastructure. What’s the danger of making an attempt to construct intelligence on high 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 a knowledge middle, they’ve restricted scalability. Efficiency was lower than par for the real-time use circumstances shoppers have been asking for. After which there was plenty of legacy tech — the stack wanted a refresh, a reimagining of what cloud-native structure might appear to be.
What we additionally noticed was plenty of handbook pipelines, plenty of knowledge redundancy, copies of the identical knowledge in a number of locations. The method itself was not very environment friendly. Any group making an attempt 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. They’re sequential, not parallel. You can’t construct an agentic advertising ecosystem on a legacy basis.
How a knowledge warehouse migration shifted the main target from upkeep to enterprise outcomes
Aly: You moved from on-premises Hadoop to Databricks. What did that shift make doable that wasn’t doable earlier than?
Ankur: When it comes to efficiency, now we have seen enchancment throughout the board, throughout several types of workloads and several types of pipelines, virtually 80 to 90 p.c sooner run instances. Workloads that used to take 50+ hours, typically 90+ hours — and I am speaking hours, so actually days, typically as much as every week — are actually getting completed inside 2-3 hours. Those self same workloads, in 2-3 hours.
It has additionally freed up our individuals. In some circumstances now we have been capable of unencumber a number of full-time roles to focus extra on value-added outcomes slightly than managing infrastructure. The primary factor it enabled was for the engineering crew to focus extra on enterprise outcomes slightly than worrying concerning the infrastructure beneath. That may sound like a smooth win, however when your engineers are spending their time constructing merchandise and delivering shopper options slightly than maintaining the lights on, it modifications what you possibly can even try.
What the Agentic Advertising Worth Chain Really Appears Like
Aly: The place are you seeing agentic AI reshape precise advertising workflows right this moment, and the place does that imaginative and prescient prolong?
Ankur: Acxiom’s core operation could be very data-centric. We herald advertising 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 crew of information engineers and knowledge architects who would mannequin all the things and construct pipelines manually. ETL is all the time the longest pole within the tent, and it might take months.
Via AI, that total cycle compresses. Code technology via prompts, automated testing of outputs, accelerated CI/CD pipelines. On the advertising aspect, producing completely different variations of an advert used to take inventive companies months. Now you possibly can analyze advertisements at scale via machine studying, feed these outcomes into an AI engine and generate extremely custom-made variations in minutes.
The place now we have seen the largest 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 completely different demographic and behavioral dimensions and lets the marketer refine from there. What used to take effort from a number of individuals with diversified talent units and plenty of lead time is now completed agentically in minutes. We’ve demonstrated the identical sample for media shopping for: an agent queries accessible stock, evaluates it, makes a shopping for choice and prompts the audiences throughout channels.
The objective is to attach all the pipeline — from viewers design via media shopping for, activation and efficiency analytics — into an agentic framework. That complete AI for BI functionality that Databricks is constructing via the Genie and agentic ecosystem is precisely the place advertising 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 best way 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 apply, meaning AI-generated content material by no means goes straight right into a dwell marketing campaign. It routes via an approval workflow the place authorized evaluations 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 choice level that carries regulatory or model danger. The objective is to not sluggish issues down. It’s to ensure pace doesn’t come at the price of belief — for the shopper, the model or Acxiom.
Embedding AI into advertising 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 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 slightly than manually orchestrated.
For shoppers, the largest change is transparency. Historically, plenty 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 may be delivered collaboratively, contained in the platforms shoppers already use, with full visibility into how choices 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 operate that comes not solely from throughout the group, however from our shoppers straight. They’re asking us: how will you make it cheaper? How will you make it extra performant? How will you make it sooner? If you wish to reply these questions actually, it’s a must to herald AI.
Proprietary Information because the Aggressive Moat
Aly: Your knowledge property are core to what Acxiom sells. How is the best way you ship that knowledge to shoppers evolving, and what does that unlock?
Ankur: Acxiom helps shoppers benefit 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’d enter right into a contract and ship the information. That was the outdated method.
Now we’re embedding our knowledge in an agentic style, both in our personal platforms or straight within the shopper’s surroundings. We accomplice with main martech platforms the place our knowledge property are natively accessible. 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 creating 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 beneficial asset. Information privateness performs an important position whereas dealing with and processing this knowledge. Manufacturers wish to train larger management and are continually in-housing the advertising capabilities. The expectation is shifting for companies to work inside manufacturers’ platforms and governance frameworks. The companies 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: If you happen to 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 the muse is strong. There’s plenty of AI buzz, which is not a buzz anymore; it is actuality. However what makes or breaks the entire AI initiative is the muse that it wants to sit down on. In our case, shifting from on-premises to the cloud was not solely an ambition. Maintaining the longer term in thoughts made it a necessity in order that we might be an actual participant within the AI journey. Strong knowledge basis, cloud-native structure, knowledge governance and safety — these are the important thing substances. Any group that skips that step goes to search out out ultimately that it wasn’t optionally available.
The sample at Acxiom is a helpful body for any govt evaluating the place to place their power. Modernizing the muse and pursuing agentic AI aren’t two separate packages competing for price range 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 modifications what sort of firm Acxiom is. That form of repositioning does not occur by bolting AI onto an on-premises stack. It requires the muse to return first.
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