AI Troubleshooting: Fix SME Operations Like Telcos Do

A 10-minute network outage can wipe out thousands of customer interactions for a telecom operator. For a Ghanaian SME, the “outage” often looks smaller—stock records that don’t match, deliveries that keep missing time windows, a WhatsApp inbox that never clears, or a cashier who can’t reconcile the day. Different industries, same pain: unreliable operations.

That’s why the AI Telco Troubleshooting Challenge matters beyond telecoms. GSMA and partners have launched a global competition asking teams to build large language models (LLMs) that can do root cause analysis (RCA) of network faults—accurately, efficiently, and with clear reasoning. The practical lesson for SMEs is straightforward: if AI can diagnose complex faults across massive networks, AI can also help your business diagnose why “things keep going wrong”—and what to fix first.

This post is part of our “Sɛnea AI Reboa Adwumadie ne Dwumadie Wɔ Ghana” series, focused on how AI speeds up work, reduces operating costs, and improves performance for businesses in Ghana. I’ll translate the telco approach into a playbook you can apply in retail, distribution, services, hospitality, and light manufacturing.

The telco insight: reliability comes from faster root cause analysis

Telecom operators don’t win by hoping faults don’t happen. They win by detecting issues early, diagnosing the real cause, and fixing it quickly—because downtime is expensive and customer trust is fragile.

The AI Telco Troubleshooting Challenge is built around that reality. It asks innovators to create AI systems that can:

• Generalise to new faults (not just repeat what they’ve seen before)
• Run as small models at the edge (lightweight AI that works closer to where data is generated)
• Provide explainability & reasoning (clear steps: “Here’s what I saw, here’s what it means, here’s what to do”)

For SMEs, this maps cleanly to day-to-day operations:

• Generalise to new faults → handle new customer complaints, new supplier delays, new fraud patterns
• Small models at the edge → run AI on modest devices or low-cost setups without heavy infrastructure
• Explainability → owners need decisions they can trust, not mysterious outputs

Here’s a sentence worth keeping: Reliability isn’t the absence of problems; it’s the speed and accuracy of your troubleshooting.

SMEs in Ghana already have “fault logs”—they just aren’t using them

Most SMEs think they don’t have the data needed for AI. I disagree. Many already collect useful “logs,” but they’re scattered across notebooks, WhatsApp, Excel sheets, POS systems, and mobile money statements.

What counts as SME “fault data”?

You don’t need fancy sensors to start. Common sources include:

• Sales/POS records (time, item, price, discount, cashier)
• Inventory movements (stock-in, stock-out, wastage, returns)
• Delivery notes and rider logs (pickup time, drop-off time, location)
• Customer service chats and call notes (WhatsApp, Instagram, phone)
• Supplier invoices and lead times
• Mobile money/bank transaction histories
• Staff shift rosters and attendance

Those are your equivalent of a telco’s network alarms. When something goes wrong—late delivery, missing stock, repeat refunds—your records can point to a pattern.

Why “root cause” beats “quick fixes”

Many operational problems repeat because SMEs treat symptoms:

• Customer complains about late delivery → you blame the rider
• Cash shortage at close of day → you blame the cashier
• Stockouts → you blame the supplier

Sometimes that’s true. Often it’s not. Root cause analysis asks: What’s the smallest set of causes that explains the repeated failures?

In practice, RCA for SMEs usually reveals issues like:

• Reorder levels are guesses, not rules
• Delivery routes aren’t planned; they’re improvised
• Staff handovers aren’t documented
• Promotions are launched without stock planning
• Returns/refunds aren’t categorised, so the same defect repeats

How LLM-style troubleshooting maps to SME operations

Telco RCA is complex, but the structure is familiar: a chain of events leads to failure. You can apply the same logic to SME processes.

Step 1: Define the “fault” in business terms

Be specific. “Sales are down” is too broad. Better examples:

• “We had 18 late deliveries last week, mainly after 4pm.”
• “We stocked out of our top 10 SKUs twice in December.”
• “Refunds rose from 2 per week to 9 per week.”

Your AI assistant (or internal analyst) needs a crisp problem statement.

Step 2: Gather the last 30–90 days of evidence

Telcos don’t troubleshoot from memory. SMEs shouldn’t either.

Create a simple “incident pack”:

• Date/time of incidents
• People involved (shift/cashier/rider)
• Products/SKUs affected
• Location/route or branch
• Customer complaint category
• Resolution and time-to-resolve

Even if this starts in a spreadsheet, it’s enough to begin.

Step 3: Ask AI to propose causes—with reasoning, not vibes

A useful AI workflow isn’t “write me a report.” It’s:

• identify patterns
• propose likely causes
• suggest tests
• rank fixes by impact and effort

If an AI tool can’t explain why it recommends something, treat it like gossip.

Step 4: Test fixes like experiments

One strong telco habit SMEs can copy: change one thing, measure the effect.

Example:

• Fix: introduce a reorder rule for 20 fast-moving items
• Measure: stockout count per week + lost sales incidents
• Time window: 4 weeks

This prevents the common SME trap: many changes at once, no clarity on what worked.

3 practical SME use cases (Ghana-focused)

Below are three areas where AI troubleshooting delivers real operational gains quickly.

1) Logistics and delivery reliability

If you run deliveries in Accra, Kumasi, Takoradi, or across regions, delays aren’t just “traffic.” They’re usually a combination of:

• poor batching (too many drops per rider)
• weak address validation (time lost calling)
• dispatch timing (orders prepared late)
• payment confirmation delays

What AI can do

• Categorise delivery failures (late pickup vs late drop-off vs customer unavailable)
• Detect time-of-day patterns (e.g., failures spike after 4pm Fridays)
• Recommend route batching rules (e.g., max 5 drops per trip in certain zones)
• Draft standard customer messages (confirm landmarks, share ETA)

Snippet-worthy rule: If you don’t classify delivery failures, you can’t fix them—because every delay looks the same.

2) Customer service: turn WhatsApp chaos into a system

Many SMEs sell through WhatsApp and Instagram. The hidden cost isn’t just slow replies—it’s lost follow-up and inconsistent answers that reduce trust.

What AI can do

• Summarise long chats into “issue + status + next step”
• Suggest reply templates consistent with your policies
• Tag conversations (pricing, delivery, complaint, returns)
• Create a daily “unresolved issues” list for your team

The telco parallel is clear: a network operations team needs a queue of incidents with priority and ownership. Your SME inbox is the same.

3) Financial management: reconcile faster, spot leakage earlier

Cash leakage is rarely one dramatic theft. It’s often “small small” issues:

• discounts not recorded
• duplicate refunds
• inconsistent pricing
• stock shrinkage not matched to sales

What AI can do

• Flag outliers (cashier A’s discounts 3× higher than others)
• Explain likely causes (promotions, training gaps, fraud risk)
• Generate checklists for daily close-of-day reconciliation
• Convert mobile money/bank descriptions into clean categories

Even a simple weekly AI-assisted review can reduce losses because problems stop staying invisible for months.

What “small models at the edge” means for SMEs (and why it matters)

One standout category in the telco challenge is Small Models at the Edge—lightweight AI that can run closer to the action.

For Ghanaian SMEs, the benefit is practical:

• Lower costs (less dependence on heavy cloud usage)
• Better privacy (sensitive customer and finance info stays more controlled)
• Faster response (instant support for staff at branch level)

You don’t need to become an AI lab. You need a setup that fits your business reality: intermittent connectivity, lean teams, and tight budgets.

A simple “AI troubleshooting” starter plan for SMEs (2 weeks)

If you want results without turning this into a long IT project, do this.

Week 1: Build your “operations log”

Pick one process to stabilise (delivery, inventory, customer service, or cash control). Then:

1. Define the top 3 recurring failures
2. Collect 30 days of examples
3. Standardise categories (keep it simple—10 tags max)
4. Assign an owner (someone must close the loop)

Week 2: Use AI to diagnose and run 1 experiment

1. Ask AI to summarise patterns and propose 3 likely root causes
2. Choose one fix that’s low-effort, high-impact
3. Track 2 metrics (e.g., late deliveries/week and customer complaints/week)
4. Review results every Friday

The reality? Most SMEs improve quickly when they stop guessing and start logging.

People also ask: common SME questions about AI troubleshooting

“Do I need a lot of data before AI helps?”

No. For troubleshooting, quality beats quantity. Fifty well-documented incidents can be more useful than 50,000 messy records.

“Will AI replace my staff?”

Not in the way most people fear. In SMEs, AI works best as a process assistant: summarising, categorising, drafting, spotting anomalies. Your staff still run operations and make judgment calls.

“What should we never give to an AI tool?”

Don’t paste sensitive customer identity data, passwords, or full bank credentials into random tools. Start with operational patterns and anonymised examples.

Why this telco challenge should change how you run your SME

The GSMA-backed AI Telco Troubleshooting Challenge is about making networks more resilient through AI that can diagnose faults, explain its reasoning, and work efficiently. That’s not a telecom-only story. It’s a blueprint for operational excellence.

If your SME keeps repeating the same problems, it’s not because you’re not working hard. It’s usually because you’re troubleshooting from memory, not evidence. Start collecting “fault logs,” use AI to identify patterns, and test fixes like experiments. That’s how reliability is built.

If you’re following our “Sɛnea AI Reboa Adwumadie ne Dwumadie Wɔ Ghana” series, this is a good next step: choose one operational area and run a 14-day AI troubleshooting sprint. What would change in your business if recurring issues dropped by even 30% before the next quarter?