On this interview, we converse with Prashant Kondle, a digital transformation knowledgeable with a cross-industry observe document in healthcare, aerospace, and monetary companies. Prashant shares how compliance can drive, not hinder, AI innovation, why most digital transformations fail to deal with actual operational issues, and what startups typically overlook when constructing AI merchandise. From provide chain resiliency to explainable AI, his insights supply a grounded, systems-level view of what it takes to steer transformation at scale.
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How do you stability innovation with compliance when introducing AI-driven options in extremely regulated industries like healthcare, aerospace, and finance?
In extremely regulated markets, like healthcare, aerospace, and monetary companies, innovation is a disciplined course of — following the foundations isn’t an afterthought; it’s constructed into each step. Each idea is examined for compliance from the start to the tip. That’s why I contemplate following the foundations a elementary design precept, incorporating requirements like HIPAA, ITAR, or GLBA into the event course of to forestall rework and delay downstream.
Conformance is ongoing, and in these industries, it provides you a strategic edge. As a result of everyone seems to be following the identical guidelines, your capacity to maneuver rapidly and react is predicated on how properly compliance is embedded in your innovation pipeline. The extra subtle and built-in your processes are, the faster and extra confidently you possibly can carry AI options to market that meet each enterprise wants and regulatory necessities.
I notice that in strictly regulated domains, information privateness, defending mental property, and locking down programs are of utmost significance. Establishments transfer cautiously and like to maintain important programs protected to restrict threat. That’s the reason each single AI answer must be clear, verifiable, and justifiable at each stage, not merely for compliance with guidelines however to construct confidence in inner stakeholders and finish customers as properly.
That is backed by cross-functional cooperation, involving authorized, compliance, and subject material specialists at the beginning of the event course of to flag potential pink flags and align with evolving requirements. My strategy to innovation is risk-based, beginning with lower-risk, higher-impact use circumstances and increasing responsibly because the system proves to be dependable.
I contemplate compliance not as a limitation, however as a strategy to allow accountable innovation. It permits us to create AI options which are superior, safe, capable of adapt, and sustainable over the long term.
What are the largest misconceptions corporations have about digital transformation, and the way do you information them by means of the fact of implementing AI and automation?
One of many best misconceptions is that digital transformation is only a tech refresh delivering effectivity. Many suppose that including AI or automation software program will someway magically yield a greater return on funding. If truth be told, transformation is a change in how a corporation works, and that takes adjustments in course of, use of information, and decision-making. I’ve written extensively about this in publications like Forbes, IEEE, and tutorial journals. My level is that know-how, by itself, not tackling the actual issues in operations, normally doesn’t result in actual change.
A second fantasy is that AI is only a matter of setting one thing up and turning it on. In industries like healthcare, aerospace, and finance, AI has to suit into totally different modes of working, compliance, and other people interplay. In my management concepts, I stress the significance of clear, knowledgeable, and explainable A.I., which engenders belief because it addresses actual issues — this has knowledgeable my work throughout totally different industries.
A vital but neglected fallacy is blind first-principles considering. In my IEEE paper, I warning towards making an attempt to recreate programs from scratch with no regard for legacy infrastructure, cultural resistance, and human dynamics. It sometimes finally ends up re-creating the identical inefficiencies utilizing new instruments. Change packages falter after they neglect that course of flaws are not often, if ever, simply technical — they’re human and organizational. That’s why I’m a believer in incremental, feedback-driven transformation grounded in working actuality, not hypothetical reinvention.
To help organizations in tackling challenges, I view transformation as a course of to develop expertise, not an episodic undertaking. I like to recommend starting small, devoted trials and making certain each effort hyperlinks to particular outcomes earlier than scaling up. Solely when they’re all aligned and ready do I allow them to scale. I additionally interact leaders to shift the notion that transformation is reserved for the IT group alone. True change happens when enterprise house owners, compliance teams, and operators equally personal the answer.
Final however not least, I assist corporations reinterpret digital transformation as a strategic, iterative course of — one which yields adaptive, data-driven working fashions. That’s the message I’ve persistently delivered by means of each my written output and direct implementation in goal industries. Ultimately, Digital is one side of transformation, true transformation occurs when folks, course of & considering remodel.
Along with your expertise in course of optimization, what’s a standard inefficiency in enterprise operations that AI can remedy however is commonly neglected?
A serious inefficiency lies in organizations’ tendency to optimize present duties with out questioning their necessity. Many enterprises look to AI for fast wins — automating repetitive processes or accelerating resolution cycles — however they not often ask: Ought to this job exist in any respect? AI’s actual potential isn’t simply in making processes quicker, however in difficult the operational logic behind them.
In my expertise, transformation occurs when AI isn’t just used for execution, but additionally for prognosis and reflection. For instance, utilizing unsupervised studying and course of mining, we will floor patterns that reveal redundant approvals, outdated checks, or legacy duties that persist just because “that’s the way it’s always been done.” These inefficiencies are sometimes blind spots to essentially the most concerned groups.
This connects instantly to a different blind spot: the casual or “shadow” course of — the best way work really will get executed by means of backchannel communication, spreadsheets, and ad-hoc workarounds outdoors the formal system. AI, particularly when skilled on unstructured information like electronic mail logs, chat threads, and workflow metadata, can illuminate these casual channels, exposing the precise bottlenecks and enablers of operational stream.
Most change packages by no means deal with these latent inefficiencies as a result of they aren’t seen in system-of-record information. Nonetheless, as soon as revealed, AI permits us to revamp processes from empirical habits, as an alternative of theoretical course of maps. I’ve seen this strategy shrink cycle occasions, remove pointless handoffs, and decrease exception dealing with.
In brief, AI shouldn’t simply automate what we already do — it ought to assist us rethink what’s price doing within the first place. That’s the place the deepest, longest-lasting effectivity good points are available.
How do you strategy the problem of scaling B2B SaaS merchandise whereas making certain they continue to be adaptable to evolving regulatory necessities?
Regulatory adaptability doesn’t start after you’ve discovered product-market match — it begins from day one. One of the crucial frequent pitfalls in SaaS scaling is treating compliance as a bolt-on as soon as traction arrives. By then, it’s typically too late — structure should be refactored, workflows reworked, and UX redesigned underneath stress. As a substitute, I imagine regulatory consciousness should be baked in from the beginning, with product structure, information governance, and auditability as foundational capabilities, not afterthoughts afterward.
Second, UI/UX ought to replicate the regulatory consciousness of the product. A compliant system isn’t just safe on the again finish, it reminds the consumer of their accountability. By displaying unambiguous workflows, contextual prompts, permission-aware interfaces, and embedded coverage transparency, the product should prod the customers in the direction of compliant habits. This reduces the friction, threat issue and builds a tradition of belief with out overwhelming the consumer.
Third, true scalability underneath regulation relies on configurability. Totally different prospects — startups or corporations, healthcare or protection, want totally different regulatory obligations. An rigid compliance layer is a blocker. I construct programs with modular controls, versatile role-based entry, information retention insurance policies that may be configured, and policy-driven workflows, in order that the platform can scale with out code-level customization.
Collectively, this strategy — early compliance design, user-level regulatory path, and system-level configurability — permits SaaS merchandise to scale {industry} and geographically whereas being audit-ready. It’s not nearly fulfilling immediately’s necessities — it’s about constructing a system that may evolve to fulfill tomorrow’s.
Having labored throughout a number of industries, what classes from one sector have you ever efficiently utilized to a different in your strategy to product growth?
One of the crucial formative classes was from my time in healthcare, the place I acquired a security- and privacy-first mindset. In healthcare programs, defending affected person information isn’t only a technical requirement — it’s an moral and regulatory requirement. This background taught me to architect programs with zero-trust rules, granular entry controls, and built-in auditability from the very starting. I’ve carried that rigor instantly into my work in aerospace and protection, the place defending delicate information, safeguarding IP, and making certain traceability throughout the product lifecycle are simply as important. The result’s merchandise that aren’t simply compliant — they’re resilient underneath scrutiny and trusted in high-stakes environments.
I gained from monetary companies the significance of operational self-discipline and process-driven scale. I led a compliance-driven, high-volume operational group the place success was primarily based on predictability, clear handoffs, and metrics-driven execution. I later utilized the identical rigor to rework retail workflows, creating programs that might deal with variable demand, regional range, and complicated provider networks, with consistency and accountability. That cross-fertilization enabled us to construct retail platforms that scaled often and operated in a variety of regulatory and geographical circumstances.
Throughout each area, I’ve discovered that success lies in translating deep classes — whether or not about safety, course of, or human habits — into new contexts. That’s what permits me to construct merchandise that aren’t solely modern but additionally grounded, scalable, and execution-ready.
What position does AI play in bettering provide chain resiliency, and what are among the most enjoyable functions you’ve seen or labored on on this house?
AI is the catalyst that transforms provide chains from reactive, rule-based programs into adaptive, self-healing networks. As a substitute of counting on historic forecasts, AI supplies real-time visibility, anomaly detection, and predictive intelligence to operational information, so groups can forecast disruptions, quantify threat, and set off automated corrective motion earlier than points propagate downstream.
One of the crucial highly effective functions I’ve seen is the usage of AI-powered digital twins. These programs create a stay, digital duplicate of the end-to-end provide community, enabling groups to simulate situations—whether or not it’s a port shutdown, provider delay, or sudden demand surge—and consider remediation methods in seconds. This not solely accelerates disaster response however feeds a steady studying loop, making the availability chain smarter over time.
I touched upon this idea in my Forbes article, “The Cognitive Supply Chain: Building Self-Healing Networks Through Digital Transformation,” the place I defined how digital twins and AI-powered orchestration ranges can determine the early warning indicators of disruption, suggest the optimum reroutes, and even make low-risk choices on their very own. The result is a provide chain that not simply withstands volatility however learns from it, adapts accordingly, and emerges stronger.
You’ve authored analysis on AI functions in billing workflows and cybersecurity frameworks. What rising AI tendencies do you imagine could have the largest influence on enterprise course of automation within the subsequent 5 years?
One of many largest tendencies is the motion away from job automation and in the direction of decision-driven orchestration. In my very own work on billing workflows, I demonstrated how AI can transcend the automation of particular person duties, e.g., validating claims or extracting information, to intelligently handle exceptions, value-based work sequencing, and workflows’ real-time adaptation. It is a shift from automation for effectivity to automation as a dynamic operational intelligence.
Second, AI is quickly surfacing in its capacity to watch compliance with processes, requirements, and regimes of regulation, operationally in addition to from a governance viewpoint. As a substitute of counting on both post-hoc audit or rules-based compliance checks, AI is ready to monitor habits in real-time now, detect violations of prescribed process, and flag up dangers early on. This has important penalties for sectors akin to protection, healthcare, and finance, the place energetic compliance monitoring will grow to be paramount as regulation outpaces programs.
Third, I see AI taking part in a transformational position in creating dynamic, unstructured repositories of data derived from paperwork, dialogue, system logs, and SME exchanges. These our bodies of data can energy context-dependent coaching, onboarding, and resolution assist for employees. As a substitute of dry guides or inflexible SOPs, teams might be offered AI-curated, in-the-trenches steering that displays how issues get executed, decreasing ramp-up time and investing difficult-to-document experience.
Collectively, these tendencies portend a future through which AI isn’t merely automating work — it’s watching how work will get executed, ensuring it meets altering requirements, and amplifying organizational understanding alongside the best way.
How do you foster a tradition of innovation inside groups whereas making certain alignment with enterprise targets and regulatory constraints?
I imagine innovation is of worth on the subject of fruition. An concept with a proof-of-concept isn’t true innovation. Transformational work that complies with organizational, {industry} & regulatory necessities is modern. My strategy is to provide groups a way of what stays the identical — enterprise targets, regulatory calls for, system constraints — after which push them to discover what might be executed inside that constraint. It’s not about unfettered freedom; it’s about targeted problem-solving.
A powerful tradition of innovation is fostered when groups have a way that their concepts matter. Subsequently, I assist delivery innovation, not large, expansive ideas that by no means ship, however a sequence of helpful, high-impact adjustments individually that every drive change. I inform my groups: don’t be obsessive about that one concept that transforms every part — be obsessive about the ten that ship and stick. That’s how transformation will get executed.
In extremely regulated environments like protection or healthcare, this mindset is very vital. I advocate for taking compliance, authorized, and ops alongside early — to not kill innovation, however to design inside constraints early. It saves waste and accelerates adoption.
We run tiny, low-friction pilots to check new ideas, not only for technical feasibility but additionally for scalability, usability, and auditability. It builds an over-time system the place innovation isn’t episodic — it’s how we function.
In brief, I foster innovation by creating intense focus, clear boundaries, and fast suggestions, and by rewarding concepts that don’t essentially sound appropriate however do have an effect.
In mentoring startups, what frequent pitfalls do you see amongst early-stage tech corporations making an attempt to construct AI-driven merchandise?
One of many largest traps I see is that AI is main early-stage founders to skip the fundamentals, particularly round downside discovery, buyer wants, and product–market match. Groups are making use of AI to research consumer habits or generate product insights, however they’re not speaking to actual prospects practically sufficient. No mannequin will exchange first-hand discovery conversations, tough pilot rollouts, or studying from agonizing consumer suggestions. Startups will find yourself constructing technically spectacular merchandise that don’t remedy significant issues.
Second, there’s sometimes a scarcity of consideration to information high quality and lifecycle. Founders don’t recognize how a lot time is invested in sourcing, cleansing, labeling, and sustaining information updated, and the way vital that’s to the product’s long-term success. They deal with mannequin structure earlier than the enter layer is even decided.
The third situation is in constructing AI in silos, with out contemplating explainability, integration, and the human-in-the-loop case. Usability and belief would grow to be extra important than mannequin accuracy in healthcare, finance, and ops-dense functions. AI programs that customers can’t perceive, management, or act on won’t be used.
My recommendation is at all times: begin with the issue, not the mannequin. Take into consideration your preferrred buyer profile, downside set & how one can scale inside that house. This stays true for any startup.
Should you had limitless sources to deal with one main problem in AI and digital transformation, what downside would you remedy first and why?
One in every of my areas of strongest curiosity is resiliency and transparency within the provide chain — an merchandise excessive on the agenda of practically each manufacturing agency immediately. However resiliency can’t be in-built isolation. A provide chain is a system of interdependent organizations, and its resiliency depends on the weakest hyperlink, not simply essentially the most superior participant.
The fact is that almost all of producing suppliers, particularly small ones, lack the structured information, digital infrastructure, or expertise to be concerned in AI-based ecosystems. That renders end-to-end visibility and coordination practically unattainable. OEMs and enormous enterprises can spend billions on AI, however their upstream and downstream companions are sometimes in silos, using handbook workarounds or legacy instruments.
If I had limitless budgets, I’d put even tiny manufacturing services onto a digital roadmap to allow them to plug into the broader provide chain with light-weight, interoperable AI instruments. This wouldn’t be about dashboards, this is able to be about serving to them construction information, roll out customary procedures, and hook into upstream-downstream workflows in actual time.
Correcting this is able to launch true resiliency, not simply on the organizational degree, however all through provide chain networks. And that’s the shift that really strikes the needle on systemic threat and enterprise continuity.
