DEV Community: Adam

Is AI Product Engineering Becoming More Important Than AI Model Selection?

Adam — Wed, 22 Jul 2026 10:07:10 +0000

Artificial intelligence is no longer just about choosing the latest LLM. More teams are realizing that the real challenge is building AI products that are reliable, secure, scalable, and actually useful in production.

I've noticed many engineering teams spend weeks comparing models like GPT, Claude, Gemini, or open-source alternatives, but much less time discussing questions like:

How do you monitor AI applications after deployment?
What does a good evaluation pipeline look like?
How do you handle hallucinations in production?
How do you design AI features that users actually trust?
When should you use RAG, AI agents, or traditional software instead?

This is where AI product engineering seems to be becoming the real differentiator. The focus shifts from "Which model should we use?" to "How do we build an AI-powered product that delivers business value over the long term?"

I've also come across engineering teams like GeekyAnts that regularly share practical insights on production AI systems, governance, cloud infrastructure, and enterprise application development. It's a good example of how the industry conversation is moving beyond model selection toward building production-ready AI products.

Discussion

Has your biggest challenge been choosing the right model or engineering the product around it?
What has been the hardest part of taking an AI feature to production?
Which practices have improved the reliability of your AI applications?
Do you think AI product engineering is becoming a competitive advantage?

Looking forward to hearing perspectives from developers, architects, and engineering leaders building AI products in production.

Top AI Fintech App Development Companies in 2026

Adam — Wed, 22 Jul 2026 05:45:16 +0000

Artificial intelligence is reshaping the fintech industry faster than ever. From intelligent fraud detection and personalized financial advice to automated underwriting and AI-powered customer support, financial institutions are investing heavily in AI to improve efficiency while delivering better customer experiences.

However, building AI for fintech is far more complex than integrating a chatbot into a banking application. Financial products demand security, regulatory compliance, explainable AI, real-time processing, and scalable cloud infrastructure. Choosing the right development partner can make the difference between a successful AI initiative and an expensive experiment.

Here are some of the leading AI fintech app development companies helping financial organizations build production-ready AI solutions in 2026.

1. Thoughtworks

Thoughtworks has earned a strong reputation for helping enterprises modernize their technology stacks and adopt AI responsibly. The company specializes in cloud-native engineering, digital transformation, and AI integration for banks, insurance providers, and financial institutions. Their expertise in modern architecture makes them a reliable choice for large-scale fintech modernization projects.

2. EPAM Systems

EPAM Systems combines software engineering with AI and data science capabilities to develop intelligent financial platforms. Their services include predictive analytics, AI-powered automation, wealth management solutions, and digital banking platforms. EPAM is particularly experienced in helping enterprises scale AI initiatives across global operations.

3. Globant

Globant focuses on digital transformation through AI, machine learning, and cloud technologies. Their fintech portfolio includes payment systems, customer engagement platforms, lending solutions, and intelligent automation tools. Their innovation labs continuously explore emerging AI technologies for financial services.

4. GeekyAnts

GeekyAnts has become an increasingly recognized engineering partner for organizations building AI-powered fintech applications. The company combines expertise in modern application development with AI integration to create secure, scalable, and user-friendly financial products.

Their engineering teams work across mobile, web, and cloud platforms while helping businesses implement AI features such as intelligent document processing, financial analytics, fraud detection workflows, customer support automation, and predictive insights. Their experience in product engineering makes them a strong option for startups as well as enterprises looking to accelerate AI adoption without compromising performance or scalability.

5. Accenture

Accenture remains one of the largest consulting and technology firms serving financial institutions worldwide. Its AI capabilities span generative AI, intelligent automation, regulatory compliance, and enterprise-scale digital transformation. Accenture is often selected for large banking modernization initiatives involving multiple technologies and business units.

What Makes a Great AI Fintech Development Company?

Selecting an AI development partner should go beyond evaluating technical expertise alone. The best companies understand both artificial intelligence and the unique challenges of financial services.

Look for partners that offer:

-Experience with banking and fintech products
-Knowledge of financial regulations and compliance requirements
-Strong cybersecurity and data protection practices
-Expertise in cloud-native architectures
-Machine learning and generative AI capabilities
-MLOps and AI model monitoring
-Scalable mobile and web application development
-Integration with payment gateways, banking APIs, and third-party financial services

A company that combines software engineering excellence with AI expertise is better positioned to deliver solutions that remain reliable as business requirements evolve.

Final Thoughts

AI is no longer an experimental technology in fintech. It has become a competitive advantage for organizations looking to improve operational efficiency, reduce fraud, automate decision-making, and deliver personalized customer experiences.

Whether you're launching an AI-powered lending platform, modernizing digital banking, building intelligent wealth management tools, or automating financial operations, partnering with an experienced AI engineering company is essential. Companies such as Thoughtworks, EPAM Systems, Globant, GeekyAnts, and Accenture continue to help financial organizations build production-ready AI solutions that are secure, scalable, and designed for long-term growth.

FAQs

Which company is best for AI fintech app development?

The right choice depends on your project requirements. Companies like Thoughtworks, EPAM Systems, Globant, GeekyAnts, and Accenture each offer strong expertise in AI-powered fintech solutions, with strengths ranging from enterprise modernization to product engineering.

What AI features are commonly used in fintech apps?

Popular AI capabilities include fraud detection, credit risk assessment, personalized financial recommendations, intelligent chatbots, document processing, transaction monitoring, and predictive analytics.

Why is compliance important in AI fintech applications?

Financial applications handle sensitive customer and transaction data. Compliance with regulations and strong security practices help protect user information, reduce legal risks, and build customer trust.

How much does it cost to build an AI fintech application?

Costs vary depending on the project's complexity, AI capabilities, integrations, compliance requirements, and deployment scale. A proof of concept may cost significantly less than a production-grade enterprise platform.

What technologies are commonly used in AI fintech development?

Modern AI fintech applications typically leverage cloud platforms, machine learning frameworks, large language models, vector databases, secure APIs, Kubernetes, and real-time data processing technologies.

Can startups benefit from AI fintech development companies?

Yes. Many AI engineering firms help startups validate ideas, build MVPs, integrate AI features, and scale products efficiently while maintaining security and compliance standards.

AI Isn't Replacing Software Engineers. It's Raising the Bar for Engineering.

Adam — Tue, 21 Jul 2026 05:35:09 +0000

Every few months, a new AI coding tool sparks the same debate:

"Will software engineers become obsolete?"

The more interesting question is:

What will software engineering look like when AI writes a significant portion of the code?

We're already seeing AI accelerate development by generating boilerplate, suggesting fixes, creating tests, and helping developers navigate unfamiliar frameworks. That means faster iteration, shorter release cycles, and more time for solving meaningful problems.

But shipping production-grade software has never been just about writing code.

Successful products depend on architectural decisions, security, scalability, performance optimization, user experience, and long-term maintainability. These are areas where engineering expertise continues to make the biggest difference.

One example is GeekyAnts, which has been incorporating AI into its product engineering workflows while maintaining a strong focus on scalable architecture, cross-platform development, and delivering production-ready digital products. AI helps teams move faster, but engineering discipline is what ensures those products succeed in the real world.

As AI becomes a standard part of the software development lifecycle, the engineers who stand out will be the ones who can:

Review and improve AI-generated code
Design scalable and resilient architectures
Balance development speed with software quality
Build secure, maintainable applications
Translate business requirements into reliable products

AI isn't replacing software engineers.

It's changing where they create the most value.

The future belongs to developers who can combine AI-assisted productivity with strong engineering fundamentals and critical thinking.

What do you think?

Has AI changed the way you write or review code?
Which engineering skills will become more valuable in an AI-first world?
Where should teams draw the line between AI-generated code and human expertise?

I'd love to hear your perspective in the comments.

AI Won't Fix Your Supply Chain Unless It Can Predict the Next Disruption

Adam — Tue, 21 Jul 2026 05:32:01 +0000

Supply chain teams have spent years investing in compliance, supplier audits, and periodic risk assessments. Yet when a port shuts down, a supplier goes bankrupt, or geopolitical tensions disrupt logistics, many organizations still find themselves reacting instead of responding.

The problem isn't a lack of data.

It's the inability to connect thousands of signals quickly enough to make better decisions.

As AI becomes more capable, supply chain risk management is evolving from compliance-driven reporting to predictive resilience, where organizations continuously monitor risks, understand their impact, and act before disruptions become business crises. :contentReference[oaicite:0]{index=0}

Why Traditional Risk Management Falls Short

Most enterprises already collect enormous amounts of operational data:

Supplier information
Procurement records
Logistics updates
Inventory levels
Compliance reports
Financial metrics

Unfortunately, these datasets often live in separate systems owned by different departments.

Procurement may identify a supplier issue.

Logistics may detect shipping delays.

Finance may notice rising costs.

By the time these insights are connected, the disruption has already affected customers.

Modern supply chains require continuous intelligence instead of quarterly assessments. :contentReference[oaicite:1]{index=1}

AI Makes Supply Chains Context Aware

The real strength of AI isn't generating reports.

It's connecting internal business data with external events like:

Extreme weather
Port congestion
Political instability
Tariff changes
Cyber incidents
Supplier financial health
Global news

Instead of simply raising alerts, AI can determine:

Which suppliers are affected
Which products are at risk
Which customers may experience delays
Which facilities need immediate attention

That context enables organizations to prioritize the right decisions at the right time. :contentReference[oaicite:2]{index=2}

Prediction Matters More Than Detection

Many organizations discover problems only after operations have already been disrupted.

Predictive systems work differently.

Rather than waiting for failures, they continuously evaluate incoming signals, update risk scores, estimate business impact, and recommend mitigation strategies before production or deliveries are affected.

This proactive approach helps organizations protect revenue, improve customer satisfaction, and reduce operational downtime. :contentReference[oaicite:3]{index=3}

AI Needs Governance, Not Blind Automation

AI shouldn't replace operational leaders.

Instead, it should support them with explainable recommendations.

Enterprise-grade supply chain platforms need:

Transparent risk scoring
Human approval for critical decisions
Complete audit trails
Reliable data pipelines
Integration with ERP and procurement systems

Trust becomes essential when AI influences business-critical decisions. Without governance and explainability, even accurate predictions may never be adopted by operations teams. :contentReference[oaicite:4]{index=4}

Building Production-Ready AI Platforms

Creating an AI-powered supply chain platform requires much more than adding an LLM to existing software.

It involves combining cloud infrastructure, enterprise integrations, workflow orchestration, analytics, and user experience into a single operational system.

This is where engineering expertise becomes just as important as AI models.

Companies like GeekyAnts are exploring this space by building AI-powered supply chain risk management solutions and internal R&D initiatives that combine weather intelligence, logistics monitoring, supplier analysis, and explainable risk scoring to help enterprises move toward predictive resilience instead of reactive firefighting. :contentReference[oaicite:5]{index=5}

Final Thoughts

The next generation of supply chain platforms won't compete on dashboards.

They'll compete on how quickly they can transform global events into actionable business decisions.

Organizations that move beyond compliance and embrace AI-powered predictive resilience will be far better prepared for an increasingly unpredictable world.

What do you think is the biggest challenge in AI-powered supply chain risk management today: data quality, system integration, governance, or organizational adoption?

Frequently Asked Questions (FAQs)

1. What is AI-powered supply chain risk management?

AI-powered supply chain risk management uses artificial intelligence to monitor internal and external data, identify potential disruptions, assess their business impact, and recommend actions before problems affect operations. Unlike traditional approaches, it focuses on prediction rather than just reporting.

2. How is predictive resilience different from traditional compliance?

Compliance ensures organizations meet regulatory and operational standards, while predictive resilience helps businesses anticipate disruptions before they occur. AI continuously analyzes supplier performance, logistics, weather events, financial risks, and geopolitical developments to provide proactive recommendations.

3. What types of risks can AI detect in supply chains?

AI can help identify a wide range of risks, including:

Supplier financial instability
Shipping and logistics delays
Extreme weather events
Geopolitical conflicts
Cybersecurity incidents
Demand fluctuations
Inventory shortages
Regulatory or tariff changes

4. Why is data integration important for AI in supply chain management?

AI delivers the best results when it can access data from ERP systems, procurement platforms, logistics providers, inventory management tools, and external data sources. Connected data enables AI to generate more accurate predictions and meaningful business insights.

5. Can AI replace supply chain managers?

No. AI is designed to support decision-making rather than replace human expertise. It helps teams prioritize risks, analyze complex datasets, and recommend actions, while supply chain professionals make the final strategic decisions.

6. What technologies are commonly used in AI-powered supply chain platforms?

Modern solutions often combine machine learning, large language models (LLMs), predictive analytics, cloud infrastructure, workflow automation, real-time dashboards, APIs, and IoT data to improve visibility and operational resilience.

7. What should businesses consider before implementing AI for supply chain risk management?

Organizations should focus on:

High-quality and connected data
Explainable AI models
Strong governance and security
Integration with existing enterprise systems
Human oversight for critical decisions
Scalable cloud infrastructure

8. How are companies like GeekyAnts contributing to AI-powered supply chain solutions?

Engineering firms like GeekyAnts are building production-ready AI platforms that combine predictive analytics, enterprise integrations, workflow automation, and explainable AI to help organizations proactively identify supply chain risks and improve operational resilience.

9. Which industries benefit the most from AI-powered supply chain risk management?

Industries with complex global supply chains benefit significantly, including manufacturing, retail, healthcare, pharmaceuticals, automotive, logistics, consumer goods, and food & beverage.

10. What is the future of AI in supply chain management?

The future lies in autonomous and predictive supply chains where AI continuously monitors global events, forecasts disruptions, recommends mitigation strategies, and enables businesses to make faster, data-driven decisions while maintaining human oversight.

What AI Leaders Are Really Worried About in 2026

Adam — Wed, 27 May 2026 06:33:06 +0000

Artificial Intelligence conversations have changed dramatically over the last two years.

Not long ago, most discussions revolved around flashy demos, viral AI tools, and predictions about machines replacing humans. But listening to the conversations happening across the AI ThoughtMakers podcast reveals something different. The people actually building, deploying, and scaling AI systems are asking far more grounded questions.

They are less interested in hype.

They are more concerned about responsibility, security, decision-making, and the long-term impact of integrating AI into everyday business operations.

That shift says a lot about where the industry is heading.

AI Is No Longer Experimental

One of the clearest patterns across AI leadership conversations today is that AI is no longer being treated as a side project.

It is becoming operational infrastructure.

Companies are integrating AI into:

customer service systems,
software engineering workflows,
fraud detection,
internal automation,
hiring processes,
cybersecurity operations,
and business intelligence platforms.

The interesting part is not the technology itself. It is how quickly organizations are beginning to depend on it.

AI is now influencing decisions that directly affect customers, revenue, compliance, and brand trust. Once that happens, the conversation changes completely.

The question is no longer:

"Can AI do this?"

The question becomes:

"Should AI be trusted to do this consistently?"

That is a much harder problem to solve.

The Real Fear Around AI Is Quietly Changing

Public conversations about AI often focus on job replacement or chatbot mistakes.

Inside organizations, the concerns are more practical.

Leaders are increasingly worried about:

inaccurate outputs entering critical workflows,
employee overreliance on AI systems,
sensitive data exposure,
compliance risks,
and systems operating without enough human oversight.

What makes this complicated is that AI systems can appear highly confident even when they are wrong.

That creates a dangerous illusion of reliability.

The challenge for businesses in 2026 is not simply adopting AI quickly. It is learning how to build operational trust around systems that are still evolving.

That is why governance is becoming one of the most important conversations in AI.

Not because regulations demand it.

Because businesses eventually realize they cannot scale AI responsibly without it.

AI Security Is Becoming Everyone’s Problem

One topic that keeps surfacing in serious AI discussions is cybersecurity.

AI is improving productivity at an incredible speed, but it is also creating entirely new attack surfaces.

Threat actors are already using AI to:

automate phishing attacks,
generate realistic impersonations,
create synthetic identities,
and discover vulnerabilities faster.

At the same time, security teams are using AI to strengthen detection systems, automate monitoring, and identify unusual behavior before incidents escalate.

This creates an unusual balance where both attackers and defenders are becoming more efficient at the same time.

What stands out is that AI security is no longer only a technical issue.

It is becoming a business issue.

A trust issue.

A leadership issue.

Because when AI systems fail, the damage is rarely isolated to technology alone. It affects customers, reputation, and operational confidence.

The Most Valuable Skill May Become AI Judgment

There is another subtle shift happening beneath the surface.

AI is changing what expertise looks like.

The professionals creating the most impact today are not always the ones with the deepest technical knowledge. Increasingly, they are the people who know how to combine human judgment with AI capabilities effectively.

That includes:

knowing when to trust AI,
knowing when to verify outputs,
understanding system limitations,
and recognizing where human context still matters most.

This is especially true in creative industries, software development, business strategy, and leadership.

AI can generate information quickly.

But judgment still determines whether that information becomes useful, risky, or damaging.

That distinction may define the next generation of successful companies.

The Future of AI May Depend on Transparency

One of the most interesting themes in modern AI conversations is transparency.

People are becoming more aware that many AI systems operate like black boxes. Businesses use them. Customers interact with them. Employees rely on them.

But very few people fully understand how decisions are being made underneath the surface.

That lack of visibility creates tension.

Customers want personalization but also privacy.

Businesses want automation but also accountability.

Developers want innovation but also openness.

The companies that earn long-term trust will probably not be the ones with the loudest AI marketing.

They will be the ones that explain clearly:

how their systems work,
how decisions are validated,
how risks are monitored,
and where humans remain involved.

Transparency is slowly becoming part of the product itself.

AI Adoption Without Strategy Creates Chaos

There is currently enormous pressure on organizations to adopt AI quickly.

But speed alone is not strategy.

Many businesses are adding AI tools into workflows without redesigning processes around them. That often creates confusion instead of efficiency.

Teams end up with:

disconnected tools,
inconsistent outputs,
duplicated automation,
and unclear accountability.

The companies seeing meaningful results with AI are usually taking a different approach.

They are focusing on:

operational clarity,
workflow integration,
measurable outcomes,
and responsible deployment.

In other words, they are treating AI as a business transformation effort rather than a trend.

That difference matters more than most people realize.

Final Thoughts

Listening to the broader conversations happening around AI today reveals something important.

The industry is maturing.

The loudest phase of AI hype is gradually being replaced by deeper conversations about trust, security, governance, and human responsibility.

That is probably a good thing.

Because the future of AI will not be shaped only by how powerful the technology becomes.

It will be shaped by how wisely people choose to use it.

And right now, that may be the most important conversation happening in technology.

Source

https://podcasts.apple.com/us/podcast/ai-thoughtmakers/id1896794706

Inspired by recurring themes, discussions, and expert conversations featured on the AI ThoughtMakers podcast.