AI Is Easy to Try but Hard to Put to Work

You may already be using AI to draft emails, summarise documents, create marketing ideas, or answer customer questions. The first experiment is usually straightforward. The harder part comes later: connecting AI to your actual business processes, checking its answers, protecting customer information, and making sure your team uses it consistently.

That implementation gap is becoming important as large technology companies compete to help businesses deploy AI. Google Cloud is working with Accenture on a dedicated group that will place trained engineers with enterprises to build applications using Google’s Gemini Enterprise platform. The arrangement is part of a wider industry push involving “forward-deployed engineers”, or specialists who work directly with businesses to apply AI to real workflows. Source

TL;DR: The next AI challenge is not finding a chatbot. It is redesigning work around reliable, connected processes. Malaysian SMEs can apply the same thinking by starting with one measurable workflow, assigning ownership, and building proper checks before expanding.

Google plans to train up to 1,000 Accenture forward-deployed engineers to help enterprises create custom AI applications on Gemini Enterprise. Source Although your company may not need a large consulting team, the principle is useful: AI works best when someone understands both the technology and the daily business process.

What This Means

“AI deployment” means putting an AI capability into a real business workflow, rather than leaving it as an isolated experiment. For example, a chatbot that answers questions in a demonstration is not yet a deployed customer-service system. Deployment means connecting it to approved product information, defining when it must hand a case to a human, recording conversations, and reviewing whether the answers are accurate.

Forward-deployed engineers sit close to the business users. They study how work is done, identify repetitive steps, connect systems, configure the AI, and improve the result after staff start using it. The job combines technical knowledge with an understanding of operations, compliance, customer expectations, and staff behaviour.

This matters because a general-purpose AI tool does not automatically understand your pricing rules, delivery areas, stock status, approval limits, service standards, or preferred language. It may produce fluent text while still giving an answer that is unsuitable for your business. A useful system needs boundaries, approved information, and a clear escalation path.

The important question is not “Which AI tool should you buy?” It is “Which business process should become more consistent, faster, or easier to manage?”

The wider industry is responding to this problem. Google Cloud has also announced a $750 million partner ecosystem commitment involving consultancies such as Capgemini, Cognizant, and Deloitte, according to the source article. Source Accenture has separately introduced initiatives involving Microsoft, ServiceNow, and SAP. Source These developments show that implementation, integration, and process design are becoming central parts of enterprise AI adoption.

How This Applies to Malaysian SMEs

1. Customer enquiries and sales follow-up

Suppose you operate a renovation company, wholesaler, clinic, training centre, or online retailer. Your team may receive enquiries through WhatsApp, Facebook, email, and phone calls. A basic AI assistant can help classify messages, identify the customer’s request, prepare a reply, and remind a salesperson to follow up. However, it should use your approved service information and pass unusual cases to a human.

For a Malaysian business, this may include handling Bahasa Malaysia, English, and informal mixed-language messages. You should define which questions AI may answer directly, such as operating hours or service coverage, and which require staff approval, such as discounts, refunds, guarantees, or medical guidance. The objective is not simply to reply more quickly. It is to reduce missed leads while keeping the customer experience dependable.

2. Quotations, purchase orders, and administration

Many SMEs lose time copying information between enquiry forms, spreadsheets, accounting software, and documents. AI can extract item descriptions from supplier quotations, compare them with a purchase request, draft a quotation, or flag missing information. It should not be allowed to approve every document automatically. Set approval rules for unusual quantities, new suppliers, changes in payment terms, or requests outside normal business limits.

A practical starting point is to map the process from incoming request to final approval. Mark where staff retype information, wait for confirmation, or search through old files. Those points often offer better opportunities than adding AI to a task that is already simple and well controlled.

3. Stock, delivery, and service operations

If you distribute products or manage field services, staff may spend much of the day answering “Is it available?”, “When will it arrive?”, or “Which technician is assigned?” questions. A connected AI workflow can retrieve information from your approved records and prepare updates for customers or staff. The quality of the result depends on your underlying data. If stock records are late or inconsistent, AI will simply communicate outdated information more efficiently.

For this reason, deployment should include data-cleaning rules. Decide who updates stock status, when a delivery is marked complete, and how cancelled jobs are recorded. Start with one product category, branch, or service team. Review the results before extending the workflow across the company.

4. Internal knowledge and staff onboarding

Important knowledge often sits in the owner’s memory or in scattered documents. An internal assistant could help staff find standard operating procedures, product specifications, safety steps, or customer-service guidelines. This is especially useful when new employees need answers quickly or when the owner is frequently interrupted.

Keep the source documents current and label sensitive material clearly. Staff should know that an AI-generated answer is a starting point, not permission to ignore company policy. You should also maintain a simple method for reporting incorrect answers so the system can be improved.

A Simple Deployment Framework

Use the following sequence before introducing AI into a business process:

Stage	What you do	Evidence to track
1. Choose	Select one repetitive workflow with a clear owner.	Current completion time and error points
2. Define	Write the permitted actions, prohibited actions, and escalation rules.	Approved checklist and sample cases
3. Prepare	Organise the documents and records the AI will use.	Data owner and update schedule
4. Test	Run real examples and review incorrect or incomplete outputs.	Accuracy review and exception log
5. Pilot	Let a small group use it while a staff member checks results.	Response time, rework, and user feedback
6. Improve	Fix weak instructions, missing data, and unclear handovers.	Monthly review record

The numbers in your review should come from your own operations. Avoid claiming success because the AI produced attractive text. Track practical indicators such as unanswered enquiries, time spent preparing quotations, repeated data entry, customer complaints, and cases requiring correction.

Practical Takeaways

• Choose one workflow that occurs frequently and has a clear business owner.
• Write down the desired outcome before selecting an AI tool.
• Separate low-risk tasks from decisions requiring human approval.
• Use approved company information instead of allowing the system to rely on random sources.
• Keep customer, employee, and supplier data protected through suitable access controls.
• Test AI with real Malaysian names, addresses, product terms, Bahasa Malaysia, and mixed-language messages where relevant.
• Record incorrect answers and use them to improve the workflow.
• Train staff on when to trust the system, when to verify it, and when to escalate.
• Review whether the process actually reduces rework or delays before expanding it.

The Bigger Picture

The Google Cloud and Accenture arrangement points to a long-term change in how AI is sold and used. Businesses are moving beyond standalone assistants towards systems that operate inside sales, operations, finance, service, and knowledge workflows. Large enterprises may rely on specialist engineers and consulting teams. SMEs can apply the same discipline on a smaller scale by combining process knowledge from their staff with practical automation support.

This also means your advantage will not come from simply having access to the same AI model as another company. Competitors can use similar tools. The difference will be the quality of your customer information, the clarity of your procedures, the discipline of your approvals, and how well your team improves the workflow over time.

Start with a narrow problem that you understand well. Make the process visible, connect only the information it needs, and keep a person responsible for the result. Once the workflow is reliable, you can decide whether the next opportunity is customer follow-up, document handling, stock updates, internal support, or another operational task.

For a Malaysian SME, that is the sensible lesson from the current AI deployment race: do not chase every new tool. Build one dependable use case, learn from it, and expand only when it genuinely helps your people serve customers and run the business better.

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