The difference between the type of problems that are solved by traditional algorithm versus AI/ML: Part1

Traditional algorithms are superior when:

The problem has clear, explicit rules that can be precisely defined
Example: Sorting algorithms like Merge Sort, Quick Sort, or Bubble Sort.
These problems have strict rules (e.g., "place smaller elements before larger ones") and can be solved efficiently with deterministic logic.
Example: Mathematical calculations: Simple Interest = P*R*T, Volatge = I*R, etc.

Complete accuracy is required with no room for probabilistic outcomes
Example: Encryption/decryption : Cryptographic algorithms like RSA encryption or SHA-256 hashing.
Security demands precise, repeatable outcomes — there's no tolerance for approximation or probabilistic errors.

The solution path is well-understood and can be explicitly programmed
Example: Pathfinding in a grid-based game using A* or Dijkstra’s algorithm.
The optimal path can be deterministically computed using known algorithms without needing to learn from data.
Example: Path-finding with known constraints: Like GPS navigation with defined roads

The problem space is relatively small or constrained
Example: Sudoku solvers using backtracking algorithms.
Because the space of possibilities is finite and relatively small, exhaustive or optimized deterministic techniques perform well.

Explainability and transparency are critical requirements
Example: Tax calculation software using fixed rules from tax laws.
Auditors and users need to see the exact steps, logic, and formulas used, which rule-based algorithms can clearly provide.

Few case studies where traditional algorithms are best suited:

1. Database queries: Finding exact matches in structured data
   Category:
   The problem has clear, explicit rules that can be precisely defined
   Complete accuracy is required with no room for probabilistic outcomes
   Why:
   When querying a database (e.g., SELECT * FROM users WHERE id = 123), you're expecting an exact, deterministic match. Any fuzziness would be incorrect in this context.

2. Data validation: Checking if inputs meet specific criteria
   Category:
   The problem has clear, explicit rules that can be precisely defined
   Explainability and transparency are critical requirements
   Why:
   Validations such as "age must be greater than 18" or "email must contain '@'" are rule-based and must be clearly understood and traceable.

3. Scheduling algorithms: For deterministic resource allocation
   Category:
   The solution path is well-understood and can be explicitly programmed
   The problem space is relatively small or constrained
   Why:
   Scheduling problems like round-robin CPU scheduling or FCFS (First-Come, First-Served) can be handled with fixed, logical steps and are often constrained by system parameters.

Few examples where what might initially seem like good AI/ML use cases are actually better handled by traditional algorithms:

1. Basic Financial Calculations
   Initial Impression: "We should use AI to calculate loan repayments and interest for our customers."
   Reality: Traditional algorithms are superior because:
   Financial calculations follow precise mathematical formulas
   Results must be exact and consistent across all users
   Regulatory compliance requires transparent, auditable calculations
   A traditional algorithm implementing the compound interest formula will be faster, cheaper, more accurate, and more explainable than an ML model.

2. Calendar Scheduling
   Initial Impression: "We should build an AI scheduling assistant."
   Reality: For basic scheduling needs, constraint-based algorithms are superior:
   Finding available time slots is a constraint satisfaction problem
   Rules for scheduling (working hours, buffer time) are explicitly definable
   Results need to be deterministic and predictable
   The solution space is finite and manageable
   Traditional scheduling algorithms can efficiently find optimal solutions without the overhead of ML.

3. Route Planning for Delivery Services
   Initial Impression: "We need AI to optimize our delivery routes."
   Reality: This is often a classic traveling salesman problem that can be solved with:
   Graph-based algorithms like Dijkstra's or A*
   Vehicle routing algorithms with time windows
   Integer programming approaches
   Heuristic algorithms like genetic algorithms or simulated annealing
   These traditional optimization techniques provide optimal or near-optimal solutions with complete reliability.

4. Basic Document Search
   Initial Impression: "We need ML to search through our document repository."
   Reality: For exact match or keyword-based search, traditional information retrieval algorithms work better:
   Inverted index structures are highly efficient
   Boolean search logic is precise and predictable
   BM25 or TF-IDF ranking algorithms work reliably for keyword relevance
   Traditional search is faster and requires less computational resources
   These approaches provide immediate, deterministic results without the complexity of neural search models.

5. Payroll Processing
   Initial Impression: "AI could help modernize our payroll system."
   Reality: Payroll calculations must be precise and follow explicit tax codes and company policies:
   Tax withholding has explicit formulas defined by law
   Benefit deductions follow contractual rules
   Overtime calculations are based on specific labor regulations
   Results must be auditable and exactly reproducible
   Traditional algorithms guarantee compliance and accuracy that ML approaches cannot match for this use case.
   These examples highlight situations where the problem structure, need for precision, and well-defined rules make traditional algorithmic approaches superior to AI/ML solutions.