Beyond the Hype: Finding Real Industry Problems Worth Solving with Software

Talk to any CxO or product manager and the same complaint surfaces again and again. It is surprisingly hard to locate real business pain that can be eased through software. Reports from large analyst firms seldom help. Their documents frequently highlight the absence of a fashionable technology as if that absence itself were the central problem.

A reader often finishes a fifty-page note from an analyst convinced that the world will end tomorrow if quantum ready data fabrics or generative AI pipelines are not adopted at once, yet still cannot explain what actual work will improve or which budget will pay for the change.

Turning vague excitement into paying customers demands a sharper lens.        

Hype and the Mirage of Problem Discovery

Analyst houses have an understandable bias. They sell research that is consumed largely by technology buyers, not domain operators. The easiest path to growth for such research is to crown technologists as domain experts, because technologists are the audience that renews the subscription. The result is a conversation that begins with a tool and then hunts for a pain point. Software builders who believe the conversation at face value risk chasing phantoms.

A genuine industry problem is an observable pattern of waste, risk, or delay that prompts a budget holder to act without evangelism.         

Spotting that pattern requires long hours beside claims processors, pick-and-pack workers, lab technicians, or nurses, not a quick reading of a quadrant chart.

Even when a genuine challenge is visible, one more hurdle remains. The problem must be solvable at acceptable cost through software alone or through software coupled with modest process change. Many high profile pilots fail because the root issue lies in policy, regulation, or incentive misalignment rather than in missing code.

Banking and Financial Services

Nowhere is the gap between hype and day to day work more obvious than in banking. White papers speak of algorithmic credit scoring, hyper personal advisory platforms, or real time risk dashboards. Banks do indeed run experiments in these areas, yet the problems they pay for most readily remain far more prosaic. One is the ballooning operational expense linked to regulatory reporting, especially in regions that treat each branch as a separate legal entity. Another is the tangled workflow that links legacy mainframe ledgers to cloud based analytics. Both swallow budgets year after year, yet neither features glowing terms such as neural or quantum.

The challenge for a software entrepreneur is twofold.

• First, data custodians worry about penalties for leakage, so proofs of concept move at glacial pace.
• Second, the bank often sees the problem as unique to its own architecture even when it is not.

A supplier must show deep respect for compliance rules and, at the same time, build reference cases that demonstrate reproducibility. Without that groundwork, a new platform, no matter how clever, will reach the stage gate and stall.

Retail

Retail commentary tends to celebrate smart shelves, cashier less checkout, and immersive digital twins. Meanwhile, merchandisers struggle with a stubbornly old issue: returns. In apparel, return rates north of thirty percent erode margin and inflate carbon cost. Software is only part of the cure because many returns relate to size confusion or changed preference, not to logistics. However, data shows that even a single percentage point fall in return rate lifts profit materially. A company that can merge computer vision, fabric stretch science, and accurate fit prediction might create clear value, yet the product brief must integrate warehouse process re engineering and supplier contract realignment.

The practical difficulty is that each retailer runs on a different cocktail of ERP, order management, and in house scripts. Integration, not algorithmic novelty, dominates delivery timelines. Teams that underestimate that reality learn a harsh lesson when the initial pilot, which was stitched by hand, has to scale to three hundred stores before the next holiday season.

Manufacturing

Global supply chains are fragile, and recent disruptions have turned analytic spotlight toward predictive maintenance and demand sensing. Yet many plants still rely on USB sticks to move machine logs between isolated networks. The root problem is often network segmentation put in place for safety, combined with equipment that can last thirty years. A cloud native dashboard thrills a visiting consultant but means very little if the maintenance planner cannot legally or physically push sensor data to it.

To uncover monetizable issues, one must walk the floor and notice where planners resort to paper to copy numbers from one system to another. In many cases, a narrow gateway that moves three or four critical tags securely into an existing CMMS will deliver more measurable benefit than a full digital twin. Selling that bridge, however, is much less glamorous than pitching AI for everything, so the opportunity can remain invisible to firms that study only press articles.

Pharmaceuticals

Drug development is awash with high tech terms such as in silico screening and synthetic biology pipelines. The economic sinkholes, though, sit in documentation and quality tracking. Each molecule generates a mountain of electronic lab notebooks, instrumentation files, and protocol amendments that must stay traceable for decades. When auditors arrive, teams spend weeks reconciling file names that differ by a single character.

Creating software that reduces this burden seems straightforward: standardize metadata, enforce reference data, and provide immutable audit logs. The difficulty is culture. Lab scientists value speed and creativity; rigid systems feel like a straightjacket. Startups that succeed here invest serious effort into user experience that mirrors the fluidity of a paper notebook while still capturing the required compliance markers. They also price by study or by candidate molecule, aligning cost with the decision making unit that can approve spend.

Healthcare

Healthcare often appears to be one giant software opportunity, yet providers remain reluctant because patient safety overrides experimentation. Predictive scheduling of operating rooms sounds attractive, but one canceled procedure can cost both revenue and goodwill. In reality, hospitals throw money at bed management and insurance pre authorization, two chores that staff handle through phone calls and fax even today. That is where delay harms patients and budgets simultaneously.

The catch lies in the heterogeneity of systems owned by insurers, providers, and regional authorities. A seamless experience requires bilateral or trilateral integration, which introduces legal discussions about data sharing. A vendor that tries to muscle through with technology alone meets a wall. Progress emerges when the vendor offers contractual templates, guarantees on indemnity, and perhaps even shared savings arrangements. In other words, software is necessary, but the selling motion hinges on risk mitigation.

From Technology First to Problem First

All five domains show a common pattern. Problems ready for monetization sit closest to routine work and are often messy. They involve data that is partial, stakeholders who hold conflicting incentives, and processes cemented by regulation. Discovery therefore demands immersion, not assumption. Field interviews, task shadowing, and small service engagements can surface clear irritants long before a line of code is written. Once an irritant is verified, the next task is to test whether solving it will unlock budget quickly enough. A venture can die of exhaustion waiting for a yes even when the value proposition is solid.

Analyst research retains value, but mostly as a map of language that potential buyers may repeat in meetings. Teams that rely on that map alone to define their product road map confuse vocabulary with need. True discovery is analog, time consuming, and mostly devoid of keynote friendly phrases.

Conclusion

Finding an industry problem worth solving with software is less about predicting the next technology wave and more about witnessing boredom, frustration, or fear on the ground. The obstacles are domain complexity, legacy process, and organizational risk, not lack of artificial intelligence or blockchain.

In banking, the grind is regulatory reporting; in retail, it is returns; in manufacturing, it is air gapped data; in pharmaceuticals, it is documentation sprawl; in healthcare, it is coordination across entities.

Address these, and customers will pay. Ignore them in favor of shiny tools, and the product will gather dust, no matter how many quadrants announce its arrival.

Dr Mahesha BR Pandit, 3rd May 2025