AI-Powered Business Analyst Series | Post 17

AI-Powered Business Analyst Series | Post 17

💡 A customer may approve your solution… but will it actually solve their problem?

Let me ask you something.

Have you ever worked on a project where the requirements were clear, the solution was approved, the development was completed, and the project was delivered on time…

…but the customer was still not getting the expected business results?

I believe this is one of the most important challenges a Business Analyst needs to think about.

Because delivering a solution and solving a business problem are two different things.

Imagine a logistics company handling thousands of EDI transactions every day.

Their operations team spends hours investigating failed transactions. The customer requests a new monitoring dashboard.

We analyze the requirements, estimate the effort, build the dashboard, and deliver it successfully.

But what happens next?

If the team still spends the same amount of time investigating failures, have we really solved the problem?

Perhaps the real opportunity was to identify recurring failures, automate error classification, prevent avoidable errors, and help the operations team resolve issues faster.

Now, this is where I see an interesting opportunity for AI in Business Analysis.

Instead of asking AI only to generate requirements or prepare documentation, we can use it to explore questions such as:

🔹 What is causing the highest operational cost?

🔹 Which recurring problems can be prevented or automated?

🔹 What measurable improvement could different solutions deliver?

🔹 Can we achieve the same outcome using our existing platforms?

🔹 How can we measure success after implementation?

Of course, AI-generated suggestions must be validated against actual operational data, technical feasibility, costs, and customer priorities.

The BA must also work with the customer and technical teams to agree on realistic success criteria.

For example, instead of promising that a dashboard will solve everything, we could agree to measure investigation time, repeat failure rates, and manual effort before and after implementation.

This gives everyone a clearer picture of the value delivered.

And there is a benefit for the service provider too.

When we demonstrate measurable business value, we build customer trust, strengthen long-term relationships, and identify opportunities to improve our own services.

My personal takeaway:

A Business Analyst should not stop at asking, “Did we deliver what the customer requested?”

We should also ask, “Did our solution improve the customer's business?”

AI can help us investigate possibilities, challenge assumptions, and identify better options.

But the real value comes when we combine AI with business understanding, domain knowledge, and human judgment.

💬 I would love to hear your thoughts:

When evaluating a project's success, what matters most in your experience?

1️⃣ Delivering the agreed requirements on time and within budget?

2️⃣ Achieving the business outcome the customer actually needs?

Or do you believe both must be measured together?

Let's discuss in the comments.

#BusinessAnalyst #ArtificialIntelligence #BusinessAnalysis #BusinessValue #DigitalTransformation #EDI #B2BIntegration

 

Day 6 — When the Estimate Is Burning: How AI Helps the BA

 Day 6 — When the Estimate Is Burning: How AI Helps the BA

This is a very realistic project situation.

Suppose:

Original estimate: 500 hours

Actual forecast: 750 hours

The worst BA response is:

“We need another 250 hours.”

The customer will immediately ask:
“Why?”

AI can help the BA analyze the variance.

For example:

Original assumption
5 partners
5 mappings
1 communication protocol

Current reality
12 partners
14 mappings
3 protocols
Additional validation rules
Additional security requirements
Additional testing

Now the BA can build:

Original Scope → New Discovery → Impact → Effort → Timeline → Commercial Impact

The conversation becomes fact-based rather than emotional.

hashtag#BusinessAnalyst hashtag#StakeholderManagement hashtag#AI hashtag#Estimation hashtag#CustomerSuccess

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What are main risks facing by bank customers while providing open banking by banks

 Open banking, while promising exciting opportunities, also comes with some potential risks for bank customers. Here are some of the main concerns:

Data Security and Privacy:

• Increased attack surface: Sharing financial data with third-party providers expands the potential attack surface for hackers and fraudsters. Weaknesses in any point of the data-sharing chain can be exploited to steal sensitive information.
• Misuse of data: Customers need to be vigilant about how their data is used by third-party providers. There's a risk that data could be sold for targeted advertising, profiling, or even discriminatory practices.
• Phishing and scams: Malicious actors may exploit open banking systems with sophisticated phishing scams, tricking customers into sharing their data with illegitimate providers.

Financial Fraud and Scams:

• Account takeover: With access to open banking APIs, fraudsters may attempt to take over customer accounts and steal funds. Strong authentication and security measures are crucial to prevent this.
• Unauthorized payments: Third-party providers could potentially initiate unauthorized payments from customer accounts if vulnerabilities are present in the API technology.
• Money laundering and terrorism financing: The increased complexity of transactions through open banking systems could make it easier for criminals to launder money or finance illegal activities.

Competition and Consumer Protection:

• Discrimination and unfair pricing: With access to detailed financial data, third-party providers might offer services or pricing based on discriminatory factors, disadvantaging certain customer groups.
• Lack of awareness and understanding: Customers may not fully understand the risks and implications of sharing their data through open banking, leading to potential harm.
• Uncertainty of regulatory landscape: Open banking regulations are still evolving, and uncertainties may create ambiguity for both customers and providers, potentially impacting consumer protection.

These are just some of the main risks that bank customers face with open banking. It's important for banks and regulators to implement robust security measures, strong data protection practices, and clear consumer education initiatives to mitigate these risks and ensure a safe and beneficial open banking experience for everyone.

Remember, while open banking presents challenges, it also offers enormous potential for improving financial services and empowering customers. By staying informed and taking precautions, you can reap the benefits of open banking while minimizing the risks involved.