On this compelling interview, we sit down with Samarth Wadhwa, a forward-thinking product chief at NetApp who’s redefining the way forward for enterprise know-how on the intersection of AI, DevOps, and cloud operations. From launching AISA, a Generative AI-powered assist assistant, to pioneering Textual content-to-SQL capabilities that democratize knowledge entry, Samarth’s work exemplifies how AI can drive actual, measurable impression throughout the group. With deep expertise in each startup and Fortune 50 environments, he brings a uncommon mix of technical acuity and strategic readability, exhibiting find out how to scale innovation with out sacrificing operational effectivity. His insights provide a blueprint for a way at present’s enterprises can flip AI from a buzzword right into a business-critical benefit.
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How do you strategy taking a product from ideation to mass adoption, and what are the important thing challenges at every stage?
When taking a product from ideation to mass adoption, my strategy is grounded in deeply understanding the downside area earlier than dashing to options. I begin by spending time with prospects, gross sales groups, and assist to uncover actual ache factors, what’s damaged, what’s inefficient, and what customers try to do however can’t. That early section is all about sample recognition: are we seeing the identical friction throughout totally different buyer segments? If sure, that’s when I begin validating the chance with qualitative and quantitative indicators.
As soon as I have a transparent downside definition, I transfer into answer design. At NetApp, for instance, we developed an API-first integration platform from scratch. The key was not simply constructing for at present’s wants however designing for extensibility, anticipating how prospects will evolve, and ensuring we don’t field ourselves in. We labored intently with engineering to break the imaginative and prescient into milestones, iterating quick and aligning our inside groups round worth supply, not simply characteristic output. That’s the place good PRDs and roadmap readability matter.
Bringing the product to market is one other crucial section. It’s not sufficient to construct a nice product; you have to make it simple for others to undertake it. That includes enabling gross sales, refining messaging, and sometimes simplifying packaging. At Harness, as an example, we constructed GitOps as a Service from 0-to-1 and labored hand-in-hand with advertising, buyer success, and even analysts to craft the proper narrative. We additionally carried out a month-to-month launch cadence, which helped us construct buyer belief and ship quicker.
The ultimate leg, scaling and mass adoption, is the place a lot of firms stumble. You have to operationalize suggestions loops and obsess over metrics. How are customers interacting with the product? What’s the time to worth? At NetApp, we launched a Generative AI assistant, AISA, and we tracked how a lot it lowered time-to-resolution and how it impacted CSAT scores. That knowledge helped us not solely iterate but in addition show ROI internally and to prospects. And albeit, among the finest product concepts come from watching how prospects misuse or lengthen your product; that’s a sign they need extra.
At each stage, there are challenges: getting alignment early on, balancing scope and velocity throughout construct, standing out in a noisy market throughout launch, and evolving shortly sufficient throughout scale. However when you’re listening intently to your customers and staying centered on enterprise outcomes, these challenges turn out to be alternatives to deepen product-market match. That’s the mindset I deliver to each product I’ve constructed.
What are the largest alternatives and dangers for enterprises as AI and automation proceed to reshape industries?
AI and automation are essentially altering how enterprises function, and I see each immense alternative and a few severe dangers in that transformation. On the alternative facet, AI is creating actual worth in locations that have been traditionally bottlenecks—assist, analytics, and decision-making, to identify a couple of. For instance, at NetApp, I led the growth of AISA, a generative AI-powered assist assistant that considerably lowered time-to-resolution for buyer queries. That’s not only a assist win, it’s a income enabler, as a result of faster decision builds belief and improves retention. We additionally labored on a Textual content-to-SQL answer, which primarily democratized entry to knowledge by permitting non-technical customers to generate complicated queries utilizing plain English. That type of functionality transforms how enterprise customers make choices.
However the largest alternative, in my view, is round effectivity at scale. Automation removes repetitive overhead and frees up groups to concentrate on innovation. At Harness, we did this with GitOps and Steady Supply automation, serving to firms streamline their software program supply course of. When completed proper, AI and automation turn out to be strategic levers, not simply price savers, however worth creators.
Now, on the danger facet, one of many largest challenges is over-automation with out understanding context. Simply because you possibly can automate one thing doesn’t imply you need to. Poorly carried out AI can degrade consumer expertise or lead to crucial errors, particularly in regulated industries. There’s additionally the danger of bias in AI fashions, and enterprises want robust governance frameworks to handle that responsibly. I’ve seen that firsthand whereas integrating LLMs and RAG pipelines into enterprise platforms; knowledge privateness, mannequin hallucination, and explainability are actual considerations.
One other danger is inside resistance. AI and automation usually suggest job displacement, and until leaders are clear and intentional about reskilling and communication, it can set off cultural backlash. The key is not simply deploying AI for productiveness, however doing it in a means that augments human potential as a substitute of changing it.
In the end, I believe the winners on this area will be the enterprises that deal with AI not as a characteristic, however as a basis. That means constructing with belief, aligning use instances with measurable outcomes, and embedding AI deeply into each product and course of. The potential is huge, however solely if approached with each ambition and accountability.
As a Product Chief at NetApp, how do you align multidisciplinary groups to drive complicated initiatives ahead successfully?
Driving complicated initiatives ahead actually comes all the way down to readability, context, and steady communication. At NetApp, I’ve led a number of high-impact initiatives, just like the growth of our cloud-native iPaaS platform and AISA, our Generative AI assist assistant—and none of these would have been profitable with out tight alignment throughout engineering, gross sales, assist, and advertising.
The very first thing I concentrate on is establishing a shared understanding of why we’re constructing one thing. It’s simple for groups to get siloed into execution mode, however when you take the time to attach the dots between buyer ache factors, enterprise targets, and the product imaginative and prescient, alignment turns into a lot simpler. I usually kick off initiatives with a narrative that explains the chance, the stakes, and the client impression, not only a characteristic listing. That narrative helps each staff, from builders to buyer success, perceive their function in delivering worth.
From there, it’s about breaking the imaginative and prescient down into actionable milestones and creating the precise collaboration rhythms. I rely closely on agile practices, however with a robust emphasis on cross-functional checkpoints. For example, in the course of the rollout of AISA, we held weekly rage classes with engineering, UX, and buyer assist, so we might iterate shortly based mostly on actual consumer suggestions. I additionally established a New Product Introduction guidelines throughout CloudOps to make sure consistency in supply and readiness throughout capabilities, gross sales enablement, assist documentation, go-to-market, and every part.
After all, misalignment can nonetheless creep in, particularly in international groups with competing priorities. That’s the place I discover transparency and knowledge are key. I overtly share buyer insights, adoption metrics, and utilization patterns with all stakeholders, so the decision-making is grounded in details, not opinions. And I ensure that to have fun progress visibly, highlighting staff wins, recognizing contributions throughout disciplines—as a result of momentum is as a lot emotional as it’s operational.
In the end, aligning multidisciplinary groups is about making everybody really feel invested within the final result. If folks perceive the aim, see the progress, and really feel possession, they present up otherwise, and that’s what drives complicated initiatives throughout the end line.
How do you see the fields of CloudOps and DevOps evolving within the subsequent 5 years, and what expertise will probably be most crucial for professionals?
CloudOps and DevOps are each evolving quickly, and I imagine the following 5 years will deliver a shift from infrastructure-centric operations to intelligence-driven, autonomous programs. We’re already seeing that development with the adoption of GitOps, infrastructure-as-code, and policy-as-code turning into desk stakes-but what’s coming subsequent is much more transformative.
CloudOps, specifically, is transferring towards larger abstraction. As multi-cloud environments turn out to be extra complicated, enterprises gained’t wish to handle particular person providers or distributors—they’ll need unified operational layers which might be API-first, policy-driven, and AI-augmented. That’s what we centered on at NetApp when constructing our iPaaS platform and AI-powered assist programs. It’s about giving groups a technique to handle complexity with out getting buried in it.
DevOps can also be evolving—from pipelines and automation scripts to platform engineering and developer expertise. We noticed that at Harness with GitOps as a Service, the place prospects wished not simply tooling, however end-to-end, opinionated workflows that would scale securely throughout massive organizations. The way forward for DevOps lies in constructing reusable, scalable platforms that empower builders with out making ops groups a bottleneck.
By way of expertise, I believe adaptability will probably be extra vital than any particular software. That mentioned, professionals ought to put money into three key areas: First, a strong grasp of cloud-native technologies-Kubernetes, Terraform, and CI/CD frameworks. Second, a robust basis in automation, together with GitOps ideas and observability tooling. And third, and perhaps most significantly, knowledge literacy and AI integration expertise. Whether or not it’s working with LLMs, constructing telemetry pipelines, or optimizing fashions for operations, Al is turning into embedded within the DevOps workflow.
Gentle expertise can also’t be ignored. The flexibility to collaborate throughout groups, perceive product and buyer context, and talk trade-offs will differentiate nice engineers from good ones. Because the strains blur between DevOps, CloudOps, and AlOps, it’ll be the professionals who can transfer fluidly throughout domains—and suppose in programs, not silos—who will lead the following wave of innovation.
What methods have you ever discovered best in scaling operational effectivity whereas sustaining innovation inside a company?
One of many largest misconceptions I’ve seen is that operational effectivity and innovation are at odds with one another. In actuality, when you’re considerate, they will reinforce each other. At each NetApp and Harness, I’ve discovered that the best technique is to design programs and processes that cut back friction groups can focus extra on fixing actual issues and fewer on navigating inside complexity.
For instance, at Harness, we carried out a structured month-to-month launch cycle throughout our Steady Supply groups. That shift alone considerably improved velocity and predictability, which freed up engineering time that had beforehand been consumed by context switching and fireplace drills. By streamlining launch administration, we created extra room for experimentation as a result of groups weren’t consistently reacting-they might plan, prototype, and iterate.
At NetApp, we took an identical strategy with our CloudOps initiatives. I launched a New Product Introduction guidelines that aligned engineering, product, gross sales, and assist on what a “complete” product supply appeared like. That standardization didn’t gradual us down—it accelerated go-to-market as a result of there have been fewer surprises late within the course of. Everybody knew what was anticipated and will focus their power on delivering worth moderately than cleansing up misalignment.
One other vital lever is automation. I’m a giant believer in automating something repeatable however not value-differentiating. Whether or not it’s establishing infrastructure with Terraform or streamlining assist workflows utilizing Al instruments like AISA, the aim is to eradicate toil so groups can spend extra time on strategic work. And automation doesn’t simply enhance efficiency-it improves morale, as a result of folks really feel like they’re doing significant work, not simply chasing tickets.
Lastly, I believe a tradition of data-driven prioritization is essential. Innovation usually fails not as a result of the concepts are unhealthy, however as a result of the timing or focus is off. At each firms, I helped implement frameworks to guage options based mostly on ROl, buyer impression, and energy, so we might place good bets with out dropping momentum on core initiatives. That steadiness is what retains the engine working whereas nonetheless pushing boundaries.
In the long run, the true technique is ensuring operational enhancements don’t really feel like constraints, however enablers. When groups see that effectivity offers them extra freedom to innovate, not less-that’s when the flywheel begins to show.
How is Al reworking product administration, and do you see a future the place Al-driven insights substitute key decision-making processes?
Al is essentially reshaping product management-both in how we construct merchandise and the way we make choices about them. We’re already seeing a shift from intuition-driven roadmaps to insight-driven ones, the place choices are more and more grounded in consumer habits, telemetry, and predictive analytics. At NetApp, for instance, after we constructed AISA, our Al-powered assist assistant, we didn’t simply apply Al to the client expertise—we additionally used knowledge from it to tell future product choices. Issues like probably the most incessantly requested queries, time-to-resolution metrics, and escalation patterns helped us prioritize backlog gadgets and design higher self-service flows.
However past analytics, Al is turning into a collaborator. With the rise of LLMs and instruments like Textual content-to-SQL, which we’re constructing to empower non-technical customers, the function of the PM is evolving. We’re now not the one bridge between enterprise and engineering; Al can now translate pure language into technical outputs. That forces us, as product managers, to maneuver up the worth chain to focus extra on framing the precise issues and orchestrating throughout capabilities, moderately than simply translating necessities.
Now, will Al substitute key decision-making? I don’t suppose so, at the least not fully. What I do see is a shift towards AI-augmented decision-making. The most effective PMs would be the ones who ask higher questions of the info, perceive the context behind the numbers, and apply judgment the place the fashions fall brief. For instance, Al would possibly inform you that customers are dropping off after step three in a workflow, but it surely gained’t inform you why that step feels irritating or misaligned with consumer expectations. That also requires human empathy and qualitative perception.
That mentioned, I do suppose Al will more and more personal choices in well-bounded, data-rich environments pricing optimization, A/B check evaluations, or incident response. And that’s a very good factor. It frees up psychological bandwidth for strategic considering, consumer empathy, and long-term imaginative and prescient areas the place human product leaders add probably the most worth.
So in my opinion, the way forward for product administration isn’t about being changed by AI, it’s about turning into simpler by studying find out how to lead alongside it.
Based mostly in your expertise with Fortune 50 firms, what frequent errors do massive enterprises make when adopting new applied sciences?
One of the crucial frequent errors I’ve seen Fortune 50 firms make when adopting new applied sciences is leaping straight into implementation with out clearly defining the issue they’re attempting to resolve. There’s usually a rush to “check the box” on adopting the newest trend-whether it’s Al, DevOps, or cloud-native architectures-without a robust alignment between enterprise targets and technical technique. I’ve been in conversations the place the main focus was extra on deploying Kubernetes or integrating a brand new LLM moderately than asking, “How does this move the needle for our customers or our teams?”
One other huge pitfall is underestimating the complexity of change administration. Expertise adoption isn’t nearly putting in new tools-it’s about evolving tradition, processes, and other people. At NetApp and Harness, after we launched automation or AI-based options, we all the time paired them with enablement plans, cross-functional alignment, and stakeholder buy-in. In massive enterprises, particularly when you don’t deliver groups alongside for the journey-engineering, operations, safety, even finance-you find yourself with shelfware or shadow IT. I’ve seen that occur greater than as soon as.
A 3rd mistake will not be investing early in scalability and governance. Enterprises usually begin with profitable pilots, however fail to plan for what occurs when that pilot must serve a whole bunch or hundreds of customers. I’ve labored with firms the place preliminary success was shortly adopted by operational bottlenecks as a result of there weren’t correct controls, observability, or cross-cloud insurance policies in place. That’s why, after I led initiatives just like the iPaaS platform at NetApp, we designed from day one with API-first and enterprise scalability in thoughts.
And at last, there’s usually a disconnect between procurement and product groups. The folks shopping for the tech aren’t all the time those implementing or utilizing it, which might result in misaligned expectations. Bridging that hole by together with engineering, product, and assist within the analysis course of is one thing l’ve persistently advocated for, particularly when working with massive shoppers throughout industries.
In brief, adopting new know-how in a big enterprise isn’t only a technical initiative-it’s an organizational shift. Success comes when the precise issues are being solved, the precise groups are concerned, and the trail to scale is obvious from the beginning.
With automation growing, how do you make sure that human creativity and demanding considering stay central to enterprise decision-making?
Automation is unimaginable at eliminating inefficiencies, however when you’re not cautious, it will probably additionally result in determination fatigue or a false sense of confidence in machine-generated outputs. For me, the hot button is being intentional about the place automation provides worth and the place human judgment continues to be important.
At NetApp, after we constructed AISA, our generative AI-powered assist assistant, we designed it to deal with routine and repeatable duties, issues like answering incessantly requested assist questions or retrieving documentation. However we have been very clear about drawing the road: when it got here to nuanced buyer points, product roadmap prioritization, or interpretation of ambiguous knowledge, we all the time introduced people again into the loop. We didn’t need the AI to interchange considering; we wished it to create area for considering.
A method I guarantee creativity stays central is by designing workflows that floor insights, not solutions. For instance, with our Textual content-to-SQL software, the purpose isn’t to make choices for analysts; it’s to take away technical boundaries to allow them to ask higher questions. By decreasing friction in exploration, you improve creativity and demanding considering as a result of folks can iterate quicker, see extra angles, and dig deeper into “why,” not simply “what.”
One other technique is embedding a crucial evaluate into the method. Whether or not it’s a product spec, a roadmap determination, or a GTM technique, I construct in deliberate moments the place we cease and ask:
Does this make sense? Are we fixing the precise downside? What assumptions are we making? Even with AI-powered suggestions, I problem groups to deal with them as beginning factors, not ultimate calls.
Lastly, tradition performs a giant function. In case your staff feels secure to query issues, to supply different concepts, and to discover with out penalty, then creativity thrives-even in a extremely automated surroundings. A number of the finest concepts I’ve seen, like a framework tweak to streamline Steady Supply or a UX change to enhance onboarding-came from engineers, designers, or buyer assist reps who had the area to step again and suppose otherwise.
So, to me, the aim of automation isn’t to interchange human insight-it’s to amplify it. It’s about creating the situations the place folks can concentrate on what really requires judgment, empathy, and creativeness. That’s the place the true worth lies, and that’s what I goal to guard and allow as a product chief.
When you had limitless assets and no constraints, what groundbreaking product or innovation would you construct to redefine the way forward for cloud computing and enterprise know-how?
If I had limitless assets and no constraints, I might construct a totally autonomous enterprise platform-something l’d name a Cognitive CloudOps Cloth. Consider it as a unified, Al-native working layer that sits above all public clouds and enterprise programs, with the intelligence to look at, predict, optimize, and act with out human intervention, however with human oversight.
Proper now, cloud computing continues to be largely reactive and fragmented. Even in superior organizations, groups are coping with dozens of dashboards, siloed metrics, disconnected instruments, and handbook choices that gradual every part down-from efficiency tuning to price optimization to compliance. What I envision is a system that adjustments that entirely-something that’s self-healing, self-scaling, and self-optimizing, all in actual time.
This platform would mix three foundational parts:
First, a context-aware telemetry engine that ingests and correlates knowledge throughout the stack-infra, app, consumer habits, prices, and even exterior elements like climate, market situations, or geopolitical dangers.
Second, a closed-loop Al engine skilled not simply on logs and metrics, however on enterprise outcomes. It wouldn’t simply inform you “CPU is spiking”—it will perceive whether or not that spike is affecting your most worthwhile buyer, and modify assets or workflows accordingly.
And third, a pure language interface that enables anybody within the organization-developer, analyst, or government, to question the system or give it high-level directives. “Optimize my pipeline for cost and latency,” or “simulate impact if we switch cloud regions.” Consider it like ChatGPT meets GitOps meets Website Reliability Engineering, at a planetary scale.
It will blur the strains between observability, governance, price administration, and even strategic planning-bringing all these parts below a single, Al-governed management airplane.
Now, after all, constructing this wouldn’t simply be a technical problem—it will require a rethinking of how enterprises belief, govern, and collaborate with Al programs. But when we might crack that, I imagine it will fully redefine how organizations construct, function, and scale within the cloud.
It wouldn’t simply save time or money-it would essentially shift the function of operations from upkeep to innovation. That’s the type of future I’m enthusiastic about—and it’s the type of downside l’d like to deal with if given the liberty to construct with out limits.
“Al Is Not the Future, It’s Already Here”: Driving Innovation on the Crossroads of Al, DevOps, and Cloud at NetApp
As Director of Product at NetApp, I’m centered on bridging AI innovation with real-world operational impression. One of the crucial transformative initiatives I’ve led is AISA*—our GenAl-powered assist assistant. Constructed utilizing massive language fashions (LLMs), Retrieval-Augmented Technology (RAG), and Azure-hosted vector databases, AISA* now delivers quicker, extra correct resolutions to buyer points. It’s already lower assist response instances by 10% and surfaced over $1M in new alternatives by incomes deeper belief with our prospects.
In parallel, I’ve been exploring how Al can streamline DevOps workflows—from clever incident triage to automated root trigger analysis-bringing larger effectivity to our inside groups. I’m additionally incubating a Textual content-to-SQL idea aimed toward empowering non-technical customers to question knowledge utilizing plain language, making insights extra accessible throughout the enterprise.
For me, the aim is obvious: combine Al meaningfully into on a regular basis cloud and DevOps practices-not as a buzzword, however as a pressure multiplier that improves how we ship, function, and scale our merchandise.
