When To Use GenAI: A Practical Decision Framework

As generative artificial intelligence (GenAI) capabilities evolve, software architects and developers face critical decisions about when to use GenAI-based solutions versus traditional programming approaches. A systematic, four-dimensional decision framework guides technology selection in application design.

While traditional programming offers faster implementation for straightforward tasks with full transparency, GenAI-based solutions demand significant computational resources and training time but enable sophisticated handling of complex, personalized interactions. A hybrid architectural strategy provides concrete criteria for technology selection that reconcile software engineering limitations and GenAI capabilities.

The 4-Dimensional Decision Framework

Before defaulting to or dismissing GenAI, architects can take a systematic approach to evaluate each feature against four practical dimensions to determine whether GenAI will add value or create unnecessary complexity and costs.

1. Reasoning versus logic. Does the feature require adaptive interpretation of ambiguous inputs or intentions, or does it follow predictable rules? GenAI excels in tasks involving pattern matching across messy inputs where all possibilities can’t be enumerated upfront. Deterministic code is best suited to handle behaviors that can be expressed as explicit rules, such as routing requests based on user permissions and eligibility decisions that follow clear logic trees.
2. Data type. What format are the primary inputs and outputs? Traditional code struggles with unstructured data that resists traditional parsing. GenAI is better suited for unstructured data, while traditional code excels at handling structured data.
3. Scalability profile. How many times per second will this function run, and what is the cost tolerance per execution? GenAI is well-suited to moderate-volume interactions where adaptive decision-making justifies the higher cost per call. Traditional code is the better option for high-throughput operations that must handle tens of thousands of requests per second at fractions of a cent each.
4. Task complexity. How many edge cases and conditional paths exist? GenAI is designed to handle workflows where the path forward depends on unpredictable or ambiguous factors, like customer intent. Using GenAI to manage simple linear tasks with clear success criteria that can be handled by programmable code is excessive and expensive.

Klarna’s GenAI customer service chatbot aligned with these criteria. The “buy now, pay later” payment company determined that customer messages, which contain unstructured text with ambiguous intent and emotional context that require interpretation across 35 languages, warranted the use of GenAI. Structured operations that required calculations, audit trails and regulatory compliance, such as authentication and payment processing, remained in traditional code.

Klarna later refined its approach, using GenAI to handle two-thirds of inquiries, while directing more complex cases that require judgment and empathy to humans. The result demonstrated an effective division of labor. GenAI interprets and routes requests, while deterministic systems handle execution and judgment.

3 Critical Trade-Offs in Practice

Once features are mapped against the four-dimensional framework, balancing three operational trade-offs further refines the decision whether the GenAI approach justifies its costs.

The first trade-off is time-to-market. GenAI accelerates features centered on language interaction, summarization or question answering. Building a hypothetical “Ask our docs” feature requires less development time with GenAI than with traditional approaches.

Traditional programming wins on speed when building crisp, rule-based features like order status tracking. When business rules are clear, features can be implemented, tested and deployed in days without the GenAI overhead of prompt engineering, model selection or accuracy evaluation.

The second trade-off is transparency and explainability. Financial calculations, access control, compliance checks and safety-critical operations demand complete transparency. When auditors ask why a fee was charged or regulators question a claim denial, deterministic code provides traceable logic. GenAI models produce outputs through billions of learned parameters that cannot guarantee reproducible reasoning paths.

Consider a fee calculation service processing 1 million transactions monthly: Deterministic code achieves essentially 100% accuracy for valid inputs. Recent studies on GPT model behavior found its accuracy rate ranging from 30% to 90%, depending on the function. Applying these rates to 1 million monthly transactions would result in over 100,000 errors, which is unacceptable for financial or compliance-critical tasks.

The final trade-off is cost structure. Traditional applications typically run on a central processing unit (CPU) infrastructure with per-request costs measuring fractions of a cent. GenAI systems introduce variable costs depending on deployment models. With external API providers, cost scales with usage through per-token pricing. A feature averaging 1,000 tokens per callcosts $20,000 monthly at $0.002 per 1,000 tokens for 10 million calls, or $100,000 monthly at higher pricing. Self-hosted models’ costs shift to GPU infrastructure, resulting in a higher upfront investment but lower marginal cost per request.

Beyond compute, GenAI introduces additional governance costs. The economics shift when GenAI replaces substantial human labor. Bank of America’s Erica chatbot relies on deterministic natural language processing GenAI to resolve 98% of support interactions autonomously,contributing to a 19% earnings upliftthat far exceeds the GenAI infrastructure costs.

Implementing Hybrid Architecture

Successful production systems use one of three architectural templates to structure the relationship between GenAI and traditional code at the system level.

Template No. 1: GenAI Interprets, Code Executes.

This template is effective when natural user experience is critical, but transactional operations require precision. A customer types, “Can you refund my last order and ship the replacement to my office?” GenAI parses intent and extracts structured elements, such as a refund request, order identifier or delivery address. Traditional services then verify ownership, determine refund eligibility, calculate amounts, call payment and shipping APIs, and update databases.

Template No. 2: GenAI Generates, Code Validates.

When creative or summary output is needed within strict boundaries, this template is appropriate. For example, a support agent uses GenAI to draft customer responses quickly by reviewing ticket history and generating suggested text. Code-based validation ensures that no personally identifiable information (PII) is leaked, that refund amounts match actual records and that responses comply with policy requirements. GenAI provides speed and quality, while deterministic guardrails ensure compliance violations never reach customers.

Template No. 3: GenAI Captures Knowledge, Code Enforces Facts.

Cleveland Clinic uses a GenAI scribe platform to document patient interactions. With patient consent, the system listens to patient calls and drafts clinical notes, which providers review before adding them to medical records. To date, the tool has documented more than 1million patient interactions, saving providers an average of 14 minutes per day. The GenAI-generated notes are then used by revenue cycle systems to reduce billing and coding issues downstream. The GenAI applies contextual knowledge to generate accurate records, while the code uses factual elements to complete traditional functions.

Hybrid Architecture: It’s Not Either/Or, but Yes

Choosing between GenAI and traditional code doesn’t require complex analysis. These frameworks and templates provide a systemic approach to GenAI adoption, but it’s essential that they don’t become obstacles to making clear decisions. A practical checklist helps move analysis to execution.

• Start with the verb. If the feature helps, suggests or explains, consider GenAI. If it calculates, enforces or guarantees outcomes, deterministic code might be a better option.
• Assess the feature’s acceptable error rate and latency. Can the system tolerate occasional inaccuracies? Does it require sub-50 millisecond response times? Identifying these operational constraints helps eliminate unsuitable approaches.
• Determine which outcomes matter most. Common metrics include cost per successful task, time to resolution and user satisfaction, not GenAI model sophistication.

GenAI excels at interpreting ambiguous inputs and generating insights, while traditional code takes ownership of decisions and irreversible actions. This division of responsibility captures GenAI’s strengths without sacrificing the reliability that business operations demand.

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