gettyBusinesses investing in AI may assume that the most advanced model will always deliver the greatest value. But for routine or narrowly defined tasks, added sophistication can increase costs, slow response times and complicate deployment without meaningfully improving outcomes. In some cases, a “good enough” AI solution may offer a better balance of performance, speed, cost and operational efficiency. Here, members of Forbes Technology Council share AI use cases where a streamlined, targeted approach may be the wiser business decision.Running Local AI For Faster DecisionsWe’ve become obsessed with model size when businesses actually measure time to decision. An AI model that’s “good enough” and runs locally can outperform a more powerful cloud model simply because it removes latency, protects data and keeps operations moving. - Tiffany Chang, KneronAutomating Routine Document ProcessingRoutine document processing is one area where “good enough” AI can outperform the most powerful model. Businesses rarely need the most advanced model to extract relevant content. A smaller, specialized model can handle narrow tasks faster, at lower cost and with more predictable results. The best enterprise AI meets business needs and delivers measurable value without unnecessary complexity. - Eranda Maldeniya, Enterprise AnalyticsForbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?Triaging Operational Risk With Explainable AIOperating risk triage is a case where “good enough” beats “most powerful.” Businesses need transparency, consistency and trust more than marginal AI gains. AI recommendations should be tied to business triggers, confidence thresholds and verification protocols. Explainable, trusted models that activate the right controls create more value than complex models that are difficult to govern, validate or trust. - Claudio Laterreur, Chronos Smart ManufacturingHandling Routine IT Help Desk RequestsAn IT help desk does not always need the most powerful AI. A simpler model can understand common requests, find approved instructions and route tickets to the right team. Unclear or serious issues go to a technician. The business gains faster service, lower costs and fewer routing mistakes without paying for capabilities it rarely needs. Common, low-risk requests can be handled automatically. Unclear or serious problems can be passed to an expert tech or a more capable AI model. - Sriram Bhamidipati, Nous InfosystemsClassifying High-Volume Contact Center RequestsIn the contact center space, voice and chatbots use small, domain-specific models to classify high-volume contacts like order status and returns. I see business value depending as much on a well-designed intent taxonomy and clear confidence thresholds as on model sophistication. When cost savings is a key goal and the AI target is highly predictable contacts, these “good enough” models are the right answer. - Seema Farheen, Sam’s ClubProcessing Repetitive Healthcare TasksIn healthcare, prior authorizations, claims sorting and risk coding can be helped with AI. These are repetitive, high-volume tasks, not reasoning problems. A small language model tuned on your data will outperform a frontier generalist here, at a fraction of the cost, and allow you to own your intelligence layer. - Fawad Butt, Penguin AiGenerating Reproducible Security EvidenceAnything that produces evidence is most important. In security work, I would rather have a smaller model whose output I can reproduce and defend six months later than a frontier model that reasons beautifully and answers differently every run. Capability you cannot reproduce is not a finding. It is an opinion with a confidence score. - J Nathaniel Ader, Qtonic Quantum Corp.Triaging Routine Security AlertsOne use case is SOC alert triage. Most alerts are noise needing correlation, not surgery. We just saw what being the most powerful without guardrails costs: two frontier models, tested and unconstrained, breached another company’s production systems chasing a benchmark answer. Keep it scoped, keep it logged and save raw power for what’s actually novel. - Beth Miller, MimecastAccelerating M&A Due DiligenceIn M&A due diligence, speed often creates more value than perfection. A lightweight AI model with the right business context can surface material risks fast enough to inform a deal, while a more powerful model may deliver incrementally better analysis only after the decision window has closed. - Praful Saklani, PramataGenerating Consistent Interview QuestionsIn the case of generating interview questions to validate a candidate’s skills and experience, a “good enough” AI model works better than a “most powerful” one. It offers useful, consistent output inside a defined workflow, with cost and behavior that you can control, and, over time, the AI use case is refined through iteration. - Dimitri Boylan, AvaturePowering Internal Knowledge SearchesInternal knowledge search—like pulling answers from your own documents, wikis or Highspot—is important. A lean “good enough” model wins here over the most powerful one. It’s fast and cheap at scale, and most queries are routine lookups, not hard reasoning. Speed and cost matter more than a marginally smarter answer, and a predictable model is easier to trust and guardrail. Save the frontier model for the genuinely hard cases. - Sakshi Jain, Amazon Web ServicesAutomating Verifiable Compliance RemediationAutomated compliance remediation on enterprise databases is important. For repetitive, safety-critical work like CIS Benchmark hardening, a smaller model wrapped in deterministic verification beats a frontier one. Predictability, low latency and cost-per-action matter more than raw reasoning. A “good enough” model you can fully verify earns trust and gets deployed; a brilliant one you can’t verify never does. - Devendra Rajput, AccentureCategorizing Support Requests By UrgencyI would use a smaller model to tag support conversations by issue and urgency once it meets the accuracy threshold. The task uses a fixed label set, so a frontier model adds cost and response time without changing the decision. Send uncertain cases to a stronger model or human reviewer. - Kayode Faturoti, BreetDrafting First-Pass Supplier RFPsOne use case is drafting first-pass supplier RFPs. A decent model stitching 80% of boilerplate clauses saves procurement days of copy-paste. The value isn’t perfect legalese; it’s handing a mostly-right draft to a veteran negotiator who sharpens the final 20% with market instinct. An overconfident AI hallucinating pricing benchmarks or fake certifications turns a time-saver into a contractual grenade. “Good enough” knows it’s a junior assistant, not signing authority. - Eshaan Jain, Mphasis SilverlineHandling Routine Tasks In Agentic WorkflowsIn agentic workflows, most tasks are high-volume and repetitive, like routing, tagging, summarizing and first-pass QA. Smaller models handle those faster, cheaper and far more predictably. Predictability is what makes governance possible. We reserve frontier models for the judgment calls. The most powerful model is rarely the most governable one. - Ali Alkhafaji, APPLYForecasting Perishable Food DemandIn food distribution, AI demand forecasting for perishable products is where “good enough” wins. A lightweight model retrained daily on sales, weather and delivery route data catches most of the signal a much larger frontier model would but runs instantly across thousands of products. Speed and quick refresh cycles matter more than marginal accuracy when spoilage risk and truck loading decisions happen in real time. - Steven Singer, Julius Silvert, Inc.Managing Cloud Autoscaling Within Safe LimitsA bounded AI model for cloud autoscaling can be more valuable than the most powerful model. It only needs to make fast, explainable decisions within safe thresholds. Low latency, predictable behavior and easy rollback matter more than slightly better model intelligence because one unstable scaling decision can trigger traffic spikes and disrupt production. - Sibasis Padhi, Walmart Inc.