In speaking recently with my current co-worker Amanda Estiverne-Colas, who serves as Director and Head of Payments Practice at Perficient, Amanda shared with me statistics she had provided to her audience at the 2025 GULF AML Forum, an annual conference for anti-money laundering (AML) professionals in the financial services industry and government. The statistics, which I found both fascinating and scary, included: 

• Phishing attacks have surged by 4,151% just since ChatGPT’s launch in 2022 

• Phone Phishing attacks increased by 28% in Q3 2024, while smishing incidents rose by 22% 

• More than half (53%) of all breaches involve customer PII, which can include tax identification numbers, emails, phone numbers, and home addresses 

For the clarity of readers, phishing is the fraudulent practice of sending email messages purporting to be from reputable companies in order to induce individuals to reveal personal information, such as passwords or credit card numbers, and smishing is the fraudulent practice of sending text messages purporting to be from reputable companies in order to induce individuals to reveal personal information, such as passwords or credit card numbers. 

In our hyper-connected world, digital transactions occur at lightning speed, creating a vast and complex landscape for financial crime. While artificial intelligence and machine learning tools are vital in the fight against fraud, the human element remains the cornerstone of effective defense. Fraud detection isn’t just about algorithms; it’s about the people behind the screens—the victims, the fraudsters, the analysts, and the developers. 

As I spoke with Amanda about how financial institutions and consumers can fight against burgeoning fraud, I was reminded of the teaching of a former co-worker from much earlier in my career. Having just finished serving in the army, that co-worker highlighted the motto of the Seven Ps. Those Ps being “Proper Prior Planning Prevents Piss-Poor Performance”. The current, and with all-due respect to the members of our armed forces, better-etiquette, saying is limited to the Four Ps—Protect, Prepare, Pursue, and Prevent. Using this, readers can gain a holistic understanding of how more resilient, human-centric systems can be designed and built to combat fraud. 

Protect: Safeguarding More Than Just Data 

Protection is the primary line of defense, extending beyond a company’s balance sheet to its reputation, customer trust, and employee well-being. In the digital age, this means creating safeguards that are both technologically advanced and human aware. 

The human side of protection involves recognizing that the primary target of many modern fraud schemes is not system vulnerability, but human psychology. Social engineering preys on trust, fear, and urgency. As such, the most crucial protective measure becomes continuous human training and awareness. Staff must be educated in the latest social engineering tactics, red flags in communication, and subtle behavioral changes that might indicate internal fraud, such as an employee living beyond their means or refusing to share job duties. 

Furthermore, dealing with victims of fraud requires a distinct human touch. A customer who has lost their life savings to an online scam needs empathy and clear, supportive guidance, not automated responses. Human analysts serve as the compassionate front line, helping victims navigate a distressing experience and rebuild trust in the institution. 

Prepare: Cultivating Resilience and Expertise 

Preparation means anticipating complexity and ambiguity, as fraudsters constantly adapt their methods. Technology helps, but it is the trained professional who must handle the unexpected. 

A significant human challenge in this phase is managing “alert fatigue”. Advanced fraud detection systems generate high volumes of alerts, many of which are false positives (legitimate transactions incorrectly flagged as fraud). Analysts, overwhelmed by the sheer volume, may become desensitized to actual threats. This is where human expertise and critical thinking are indispensable. Experienced analysts provide essential feedback on the utility of detection models, helping to tune systems to be more accurate and reduce false positives. 

Preparation also involves developing professional resilience. Investigators deal with angry victims and deceptive individuals, requiring emotional intelligence and clear communication skills. The human element in preparation ensures that institutions are not just structurally ready with protocols but also staffed with people who are mentally and skillfully equipped to handle high-stress situations. 

Pursue: The Art of Human Investigation 

When fraud occurs, the pursuit begins. While data analytics help “follow the money,” human investigators are the ones who put the pieces together, often leveraging a combination of technical knowledge and investigative experience. 

Transactions in a digital landscape rarely move in straight lines. Criminals use layering, cross-jurisdictional transfers, and digital assets to obscure the path. Pursuing these requires human ingenuity to connect seemingly unrelated data points and understand the “why” behind the transactions. 

Crucially, pursuit relies heavily on inter-institutional and human collaboration. Sharing information between banks and agencies, often hampered by misinterpretations of privacy laws, is a human-led effort to overcome organizational silos. Human networks and trusted relationships between compliance professionals are essential to disrupt criminal activity effectively. 

Prevent: The Continuous Cycle of Learning 

Prevention is about learning from every case and educating both consumers and staff to stop future occurrences. 

Starting with an all-digital approach, one bank that worked with Amanda and her team initiated real-time transaction notifications requiring instant customer verification, to help prevent fraud. Another financial institution worked with Perficient to modify their in-app fraud education library updated weekly with new threats. AI-powered analysis of customer transaction patterns triggers proactive educational interventions before fraud occurs.  

This final P is not just all-digital but also brings us back to the human element as the loop-closer in the system. Every investigation offers insights into new fraud typologies, compromised onboarding flows, or novel social engineering tactics. It is up to human teams—investigators, risk managers, and product developers—to establish effective feedback loops. 

The human side of prevention is fostering a culture where fraud is not a siloed responsibility but a part of the organization’s DNA. It involves embedding “compliance by design” into new digital products, ensuring that human-centric insights are used to make systems inherently more secure. 

Conclusion: 

Ultimately, the digital age has made fraud detection faster and more data-intensive, but the core battle remains human versus human—the fraudsters’ psychology against the collective ingenuity and integrity of those dedicated to stopping them. By embracing the Four Ps of Protect, Prepare, Pursue, and Prevent, Perficient can combine their AI expertise with technical and compliance staff and ensure that both human and artificial intelligence are combined successfully at the heart of your firm’s defensive strategies against fraud.  

Our financial services experts continuously monitor the financial services landscape and deliver pragmatic, scalable solutions that meet the required mandate and more. Reach out to Perficient’s Director and Head of Payments Practice Amanda Estiverne-Colas to discover why we’ve been trusted by 18 of the top 20 banks, 16 of the 20 largest wealth and asset management firms, and 25+ leading payment + card processing companies and are regularly recognized by leading analyst firms. 

Artificial Intelligence (AI) is everywhere. Every month brings new features promising “deeper thinking” and “agentic processes.” Tech titans are locked in trillion-dollar battles. Headlines scream about business, economic, and societal concerns. Skim the news and you’re left excited and terrified!

Here’s the thing: we’re still human – virtues, flaws, quirks, and all. We’ve always had our agency, collectively shaping our future. Even now, while embracing AI, we need to keep building for us.

We Fear What We Do Not Know

“AI this… AI that…” Even tech leaders admit they don’t fully understand it. Sci-fi stories warn us with cautionary tales. News cycles fuel anxiety about job loss, disconnected human relationships, and cognitive decline.

Luckily, this round of innovation is surprisingly transparent. You can read the Attention is All You Need paper (2017) that started it all. You can even build your own AI if you want! This isn’t locked behind a walled garden. That’s a good thing.

What the Past Can Tell Us

I like to look at the past to gauge what we can expect from the future. Humans have feared every major invention and technological breakthrough. We expect the worst, but most have proven to improve life.

We’ve always had distractions from books, movies, games, to TikTok brain-rot. Some get addicted and go too deep, while others thrive. People favor entertainment and leisure activities – this is nothing new – so I don’t feel like cognitive decline is anything to worry about. Humanity has overcome all of it before and will continue to do so.

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Humans are Simple (and Complicated) Creatures

We look for simplicity and speed. Easy to understand, easy to look at, easy to interact with, easy to buy from. We skim read, we skip video segments, we miss that big red CTA button. The TL;DR culture rules. Even so, I don’t think we’re at risk of the future from Idiocracy (2006).

That’s not to say that we don’t overcomplicate things. The Gods Must Be Crazy movie (1980) has a line that resonates, “The more [we] improved [our] surroundings to make life easier, the more complicated [we] made it.” We bury our users (our customers) in detail when they just want to skim, skip, and bounce.

Building for Computers

The computer revolution (1950s-1980s) started with machines serving humans. Then came automation. And eventually, systems talking to systems.

Fast-forward to the 2010s, where marketers gamed the algorithms to win at SEO, SEM, and social networking. Content was created for computers, not humans. Now we have the dead internet theory. We were building without humans in mind.

We will still have to build for systems to talk to systems. That won’t change. APIs are more important than ever, and agentic AI relies on them. Because of this, it is crucial to make sure what you are building “plays well with others”. But AIs and APIs are tools, not the audience.

Building for Humans

Google used to tell us all to build what people want, as opposed to gaming their systems. I love that advice. However, at first it felt unrealistic…gaming the system worked. Then after many updates, for a short bit, it felt like Google was getting there! Then it got worse and feels like pay-to-play recently.

Now AI is reshaping search and everything else. You can notice the gap between search results and AI recommendations. They don’t match. AI assistants aim to please humans, which is great, until it inevitably changes.

Digital teams must build for AI ingestion, but if you neglect the human aspect and the end user experience, then you will only see short-term wins.

Examples of Building for Humans

• Make it intuitive and easy. Simple for end users means a lot of work for builders, but it is worth it! Reduce their cognitive load.
• Build with empathy. Appeal to real people, not just personas and bots. Include feedback loops so they can feel heard.
• Get to the point. Don’t overwhelm users, instead help them take action! Delight your customers by saving them time.
• Add humor when appropriate. Don’t be afraid to be funny, weird, or real…it connects on a human level.
• Consider human bias. Unlike bots and crawlers, humans aren’t always logical. Design for human biases.
• Watch your users. Focus groups or digital tracking tools are great for observing. Learn from real users and iterate.

Conclusion

Building for humans never goes out of style. Whatever comes after AI will still need to serve people. So as tech evolves, let’s keep honing systems that work with and around our human nature.

……

If you are looking for that extra human touch (built with AI), reach out to your Perficient account manager or use our contact form to begin a conversation.

The AI Revolution is in Drupal CMS 

The way we create, optimize, and deliver content has fundamentally changed. Artificial Intelligence is no longer a futuristic concept; it’s a practical, indispensable tool for content teams. For years, Drupal has been the gold standard for structured, enterprise-level content management. Now, with the rapid maturation of the community’s Artificial Intelligence Initiative, Drupal is emerging as the premier platform for an Intelligent CMS. 

This post is for every content editor, site builder, and digital marketer who spends too much time on repetitive tasks like writing alt text, crafting meta descriptions, or translating copy. We’re moving the AI power from external tools directly into your Drupal admin screen. 

We will explore five essential Drupal modules that leverage AI to supercharge your content workflow, making your team faster, your content better, and your website more effective. This is about making Drupal work smarter, not just harder. 

The collective effort to bring this intelligence to Drupal is being driven by the community, and you can see the foundational work, including the overview of many related projects, right here at the Drupal Artificial Intelligence Initiative. 

1. AI CKEditor Integration: The Content Co-Pilot

This functionality is typically provided by a suite of modules, with the core framework being the AI (Artificial Intelligence) module and its submodules like AI CKEditor. It integrates large language models (LLMs) like those from OpenAI or Anthropic directly into your content editor. 

Role in the CMS 

This module places an AI assistant directly inside the CKEditor 5 toolbar, the primary rich-text editor in Drupal. It turns the editor from a passive text field into an active, helpful partner. It knows the context of your page and is ready to assist without ever requiring you to leave the edit screen. 

How It’s Useful 

• For Content Editors: It eliminates the dreaded “blank page syndrome.” Highlight a bulleted list and ask the AI to “turn this into a formal paragraph” or “expand this summary into a 500-word article.” You can instantly check spelling and grammar, adjust the tone of voice (e.g., from professional to friendly), and summarize long blocks of text for teasers or email excerpts. It means spending less time writing the first draft and more time editing and refining the final, human-approved version. 

• For Site Builders: It reduces the need for editors to jump between Drupal and external AI tools, streamlining the entire content creation workflow and keeping your team focused within the secure environment of the CMS. 

2. AI Image Alt Text: The SEO Automator

AI Image Alt Text is a specialized module that performs one critical task exceptionally well: using computer vision to describe images for accessibility and SEO. 

Role in the CMS 

This module hooks into the Drupal Media Library workflow. The moment an editor uploads a new image, the module sends that image to a Vision AI service (like Google Vision or an equivalent LLM) for analysis. The AI identifies objects, actions, and scenes, and then generates a descriptive text which is automatically populated into the image’s Alternative Text (Alt Text) field. 

How It’s Useful 

• For Accessibility: Alt text is crucial for WCAG compliance. Screen readers use this text to describe images to visually impaired users. This module ensures that every image, regardless of how busy the editor is, has a meaningful description, making your site more inclusive right from the start. 

• For SEO & Editors: Alt text is a ranking signal for search engines. It also saves the editor the most tedious part of their job. Instead of manually typing a description like “Woman sitting at a desk typing on a laptop with a cup of coffee,” the AI provides a high-quality, descriptive draft instantly, which the editor can quickly approve or slightly refine. It’s a huge time-saver and compliance booster. 

3. AI Translation: The Multilingual Enabler

This feature is often a submodule within the main AI (Artificial Intelligence) framework, sometimes leveraging a dedicated integration like the AI Translate submodule, or integrating with the Translation Management Tool (TMGMT). 

Role in the CMS 

Drupal is one of the world’s most powerful platforms for building multilingual websites. This module builds upon that strength by injecting AI as a Translation Provider. Instead of waiting for a human translator for the first pass, this module allows content to be translated into dozens of languages with the click of a button. 

How It’s Useful 

• For Global Content Teams: Imagine launching a product page simultaneously across five markets. This tool performs the initial, high-quality, machine-generated translation and saves it as a draft in the corresponding language node. The local editor then only needs to perform post-editing (reviewing and culturally adapting the text), which is significantly faster and cheaper than translating from scratch. 

• For Site Owners: It drastically cuts the time-to-market for multilingual content and ensures translation consistency across technical terms. It leverages the AI’s power for speed while retaining the essential human oversight for cultural accuracy. 

4. AI Automators: The Smart Curator

AI Automators (a powerful submodule of the main AI project) allows you to set up rules that automatically populate or modify fields based on content entered in other fields. 

Role in the CMS 

This is where the magic of “smart” content happens. An Automator is a background worker that monitors when a piece of content is saved. You can configure it to perform chained actions using an LLM. For instance, when an editor publishes a new blog post: 

1. Read the content of the Body field. 
2. Use a prompt to generate five relevant keywords/topics. 
3. Automatically populate the Taxonomy/Tags field with those terms. 
4. Use another prompt to generate a concise post for X (formerly Twitter). 
5. Populate a new Social Media Post field with that text. 

How It’s Useful 

• For Content Strategists: It enforces content standards and completeness. Every piece of content is automatically tagged and optimized, reducing the chance of human error and improving content discoverability through precise categorization. It ensures your SEO and content strategy is executed flawlessly on every save. 

• For Site Builders: It brings the power of Event-Condition-Action (ECA) workflows into the AI space. It’s a no-code way to build complex, intelligent workflows that ensure data integrity and maximize the usefulness of content metadata. 

5. AI Agents: The Operational Assistant

AI Agents, typically used in conjunction with the main AI framework, is a powerful new tool that uses natural language to execute administrative and site-building tasks. 

Role in the CMS

An AI Agent is like a virtual assistant for your Drupal back-end. Instead of navigating through multiple complex configuration forms to, say, create a new field on a content type, you simply tell the Agent what you want it to do in plain English. The Agent interprets your request, translates it into the necessary Drupal API calls, and executes the changes. The module comes with various built-in agents (like a Field Type Agent or a Content Type Agent). 

How It’s Useful 

• For Site Builders and Non-Technical Admins: This is a revolutionary step toward conversational configuration. You can issue a command like: “Please create a new Content Type called ‘Product Review’ and add a new text field named ‘Reviewer Name’.” The agent handles the creation process instantly. This dramatically reduces the learning curve and time needed for common site-building tasks. 

• For Automation: Agents can be chained together or triggered by other systems to perform complex, multi-step actions on the CMS structure itself. Need to update the taxonomy on 50 terms? A dedicated agent can handle the large-scale configuration change based on a high-level instruction, making system maintenance far more efficient. It turns administrative management into a conversation. 

Conclusion:

The integration of AI into Drupal is one of the most exciting developments in the platform’s history. It is a powerful affirmation of Drupal’s strength as a structured content hub. These modules—the AI CKEditor, AI Image Alt Text, AI Translation, AI Automators, and now the transformative AI Agents—are not here to replace your team. They are here to empower them. 

By automating the mundane, repetitive, and technical aspects of content management and even site configuration, these tools free up your content creators and site builders to focus on what humans do best: strategy, creativity, and high-level decision-making. The future of content management in Drupal is intelligent, efficient, and, most importantly, human-powered. It’s time to equip your team with these new essentials and watch your digital experiences flourish. 

Artificial Intelligence (AI) is like teaching computers to think and make decisions like humans. Instead of just following instructions, Artificial Intelligence can learn from data, recognize patterns, and solve problems.

The most used Artificial Intelligence types are explained below:

Generative AI

Generative AI is like an intelligent robot that can create new things — such as writing stories, making pictures, composing music, or even writing computer code. It learns from a massive amount of data (like books or images) and then uses that knowledge to make something new. For example, you give it a few words, and it can write a poem or draw a picture. People utilize it in areas such as design, customer support, and software development to save time and enhance creativity.

Example:

AI Agents

Artificial Intelligence Agents are programs that take input, think, and act to complete a task using tools, memory, and knowledge. They can be simple (like a chatbot that answers questions) or complex (like a system that recommends products based on your preferences). These agents communicate with users, other agents, or systems to accomplish tasks. They solve specific problems by using innovative, flexible approaches.

Example:

Agentic AI

Agentic AI is a system where one or more AI agents work autonomously, often over long tasks, making decisions, using tools, and even other agents to reach a goal. It doesn’t need someone to tell it what to do all the time. It can set goals, make plans, and change its actions if something unexpected happens. Think of a self-driving car — it decides where to go, how to get there, and what to do if there’s traffic. This type of Artificial Intelligence is used in robots, smart assistants, and other systems that require independent operation.

Example:

Comparison Table

Refer: Generative AI vs AI Agents vs Agentic AI: A Comparison Guide

Conclusion: A Changing Future

Artificial Intelligence no longer seems like a far‑off idea; it sits at the core of today’s progress. From generative tools that help people make art and text, to AI agents that run simple jobs, and now the new agentic systems that may offer reasoning and self‑adjustment, the scene appears to shift fast.

The biggest impact likely comes when the pieces join together. Companies that mix these bits could cuts costs, and maybe open fresh ways to serve clients. Still, just buying tech is not enough. There must be careful use, ethical checks, and ongoing learning to keep trust and openness.

Many fear jobs may be lost. Looking ahead, the question isnt if AI changes business— but how ready we are to use it right. Those who study these tools now may shape the market tomorrow.

In Part 1 we’ve talked about why we urgently need to make sure AI is used responsibly and has clear rules. We looked at the real dangers of AI that isn’t checked, like how it can make existing biases worse, invade our privacy, create tricky legal problems around who owns what, and slowly make people lose trust. It’s pretty clear: if we don’t handle the amazing power of Generative AI carefully and proactively, it could easily go off track and cause a lot of harm instead of bringing good things. 

But just pointing out the problems isn’t enough. The next important step is to figure out how we can actually deal with these challenges. How do we go from knowing why to actually doing something? This is where the idea of Responsible AI becomes not just a theory, but something we absolutely must put into practice. To build a future where AI helps humanity achieve its best, we need to design it carefully, manage it well, and keep a close eye on it all the time. 

How Do We Implement Responsible AI? A Blueprint for Action 

The challenges are formidable, but so too is the potential of Generative AI to benefit humanity. To realize this potential responsibly, we cannot afford to let innovation outpace governance. We need a concerted, collaborative effort involving governments, industry, academia, civil society, and the public. Here’s a blueprint for action: 

1. Ethical Principles as a Guiding Star

Every stage of AI development and deployment must be anchored by strong ethical principles. These principles should include: 

• Fairness: Ensuring AI systems do not perpetuate or amplify biases and treat all individuals and groups equitably. This means actively identifying and mitigating discriminatory outcomes. 

• Accountability: Establishing clear lines of responsibility for AI system actions and outcomes, allowing for redress when harm occurs. Someone, or some entity, must always be answerable. 

• Transparency & Explainability: Designing AI systems that are understandable in their operation and provide insights into their decision-making processes, especially in high-stakes applications. The “black box” needs to become a glass box. 

• Privacy & Security: Protecting personal data throughout the AI lifecycle and safeguarding systems from malicious attacks. Data must be handled with the utmost care and integrity. 

• Safety & Reliability: Ensuring AI systems operate dependably, predictably, and without causing unintended harm. They must be robust and resilient. 

• Human Oversight & Control: Maintaining meaningful human control over AI systems, especially in critical decision-making contexts. The ultimate decision-making power must remain with humans. 

These principles shouldn’t just be abstract concepts; they need to be translated into actionable guidelines and best practices that developers, deployers, and users can understand and apply. 

2. Prioritizing Data Quality and Governance

The adage “garbage in, garbage out” has never been more relevant than with AI. Responsible AI begins with meticulously curated and ethically sourced data. This means: 

• Diverse and Representative Datasets: Actively working to build datasets that accurately reflect the diversity of the world, reducing the risk of bias. This is a continuous effort, not a one-time fix. 

• Data Auditing: Regularly auditing training data for biases, inaccuracies, and sensitive information. This proactive step helps catch problems before they propagate. 

• Robust Data Governance: Implementing clear policies and procedures for data collection, storage, processing, and usage, ensuring compliance with privacy regulations. This builds a strong foundation of trust. 

• Synthetic Data Generation: Exploring the use of high-quality synthetic data where appropriate to mitigate privacy risks and diversify datasets, offering a privacy-preserving alternative. 

3. Emphasizing Transparency and Explainability 

The “black box” nature of many advanced AI models is a significant hurdle to responsible deployment. We need to push for: 

• Model Documentation: Comprehensive documentation of AI models, including their intended purpose, training data characteristics, known limitations, and performance metrics. This is akin to an engineering blueprint for AI. 

• Explainable AI (XAI) Techniques: Developing and integrating methods that allow humans to understand the reasoning behind AI decisions, rather than just observing the output. This is crucial for debugging, auditing, and building confidence. 

• “AI Nutrition Labels”: Standardized disclosures that provide users with clear, understandable information about an AI system’s capabilities, limitations, and data usage. Just as we read food labels, we should understand our AI. 

4. Upholding Consent and Compliance

In a world increasingly interacting with AI, respecting individual autonomy is paramount. This means: 

• Informed Consent: Obtaining clear, informed consent from individuals when their data is used to train AI models, particularly for sensitive applications. Consent must be truly informed, not buried in legalese. 

• Adherence to Regulations: Rigorous compliance with existing and emerging data protection and AI-specific regulations (e.g., GDPR, EU AI Act, and future national laws). Compliance is non-negotiable. 

• User Rights: Empowering users with rights regarding their data used by AI systems, including the right to access, correct, and delete their information. Users should have agency over their digital footprint. 

5. Continuous Monitoring and Improvement

Responsible AI is not a one-time achievement; it’s an ongoing process. The dynamic nature of AI models and the evolving world they operate in demand constant vigilance. This requires: 

• Post-Deployment Monitoring: Continuously monitoring AI systems in real-world environments for performance degradation, emergent biases, unintended consequences, and security vulnerabilities. AI systems are not static. 

• Feedback Loops: Establishing mechanisms for users and stakeholders to provide feedback on AI system performance and identify issues. Their real-world experiences are invaluable. 

• Iterative Development: Adopting an agile, iterative approach to AI development that allows for rapid identification and remediation of problems based on monitoring and feedback. 

• Performance Audits: Regular, independent audits of AI systems to assess their adherence to ethical principles and regulatory requirements. External validation builds greater trust. 

6. Maintaining Human in the Loop (HITL) 

While AI is powerful, human judgment and oversight remain indispensable, especially for high-stakes decisions. This involves: 

• Meaningful Human Review: Designing AI systems where critical decisions are reviewed or approved by humans, particularly in areas like medical diagnosis, judicial rulings, or autonomous weapon systems. Human oversight is the ultimate safeguard. 

• Human-AI Collaboration: Fostering systems where AI augments human capabilities rather than replacing them entirely, allowing humans to leverage AI insights while retaining ultimate control. It’s about synergy, not substitution. 

• Training and Education: Equipping individuals with the skills and knowledge to effectively interact with and oversee AI systems. An AI-literate workforce is essential for responsible deployment. 

Conclusion: A Collaborative Future for AI 

The implementation of responsible AI is a grand, multifaceted challenge, demanding nothing short of global cooperation and a shared commitment to ethical development. While regional efforts like the EU AI Act are commendable first steps, a truly effective framework will require international dialogues, harmonized principles, and mechanisms for interoperability to avoid a fragmented regulatory landscape that stifles innovation or creates regulatory arbitrage. 

The goal is not to stifle the incredible innovation that Generative AI offers, but to channel it responsibly, ensuring it serves humanity’s highest aspirations. By embedding ethical principles from conception to deployment, by prioritizing data quality and transparency, by building in continuous monitoring and human oversight, and by establishing clear accountability, we can cultivate a future where AI is a force for good. 

The journey to responsible and regulated AI will be complex, iterative, and require continuous adaptation as the technology evolves. But it is a journey we must embark upon with urgency and unwavering commitment, for the sake of our shared future. The generative power of AI must be met with the generative power of human wisdom and collective responsibility. It is our collective duty to ensure that this transformative technology builds a better world for all, not just a more automated one. 

Perficient’s global colleagues share a commitment to excellence, grounded in the belief that our success is defined by the outcomes we achieve for our clients. Our focus on delivering exceptional results inspires us to leverage our expertise and foster innovation in every project. By deeply understanding our clients’ unique needs, we strive to create tailored solutions that not only meet expectations but consistently exceed them.  

This blog series celebrates Perficient’s recognition as a 2025 USA Today Top Workplace. In our last blog, we discussed how Perficient is Shattering Boundaries through our global delivery model and cutting-edge strategies. In the third installment of the series, we’re showcasing how our outcomes-driven approach strengthens client relationships, enhances our industry expertise, and harnesses data to foster success. Watch the video below to see how our colleagues passionately pursue results to accelerate growth for both our clients and our business. 

Engaging Clients and Exceeding Expectations 

Client success relies on understanding and trust. By cultivating genuine connections, we gain valuable insights into our clients’ challenges, allowing us to refine our strategies. Our team listens actively and approaches every project with empathy, crafting personalized solutions that address immediate needs while supporting long-term, scalable growth. We prioritize our clients’ objectives and always keep the end goal in mind, fostering lasting partnerships.  

“Relationships are the basis of everything we do. When we engage with a client, we’re often doing something that’s cutting edge, and it’s our job to build trust and collaboration. It’s important to get down to the root cause of a client’s concerns, determine what they’re scared of, and outline what their goals are to create the building blocks for long-term success.” – Gina Hart, Managing Director, Dallas 

READ MORE: Delighting the Client 

Perficient’s Cross-Industry Expertise Empowers Businesses with Agile Strategies 

Our client-first approach and pragmatism enable businesses to act swiftly and maintain a competitive edge in a rapidly evolving market. From enhancing customer relationships and increasing revenue to saving costs and leveraging technologies, our expertise transforms businesses and drives real results. With thousands of skilled strategists and technologists worldwide, we have the knowledge and industry experience to create end-to-end digital solutions that elevate the world’s biggest brands.  

“It’s something I talk about all the time—using the ability to measure what we’re doing to drive where we’re going and iterating rapidly. As the world changes around us, and as we observe and measure that change, we can feed that back into an experimental process. With speed and agility, we can change our approach to accomplish new things, try new approaches, and potentially create innovative new ways of doing business.” – Eric Walk, Principal, Digital Strategy

Our subject matter experts are at the forefront of digital change across various industries, sparking innovation through their thought leadership. Now, let’s explore some of the top industries we serve and how our expertise enhances client outcomes. 

Healthcare and Life Sciences  

Healthcare is our largest vertical, serving clients such as providers, plans, and health solution companies. Our healthcare and life sciences experts bring extensive knowledge that fosters strong collaboration with clients across the care ecosystem, as we partner to streamline operations, enhance patient experiences, and support better health outcomes. We are proud to have served the 10 largest health systems and the 10 largest health insurers in the U.S. Additionally, Modern Healthcare consistently recognizes Perficient as a top healthcare management consulting firm.  

In the healthcare industry, we have enhanced health system outreach by leveraging our expertise and platform-agnostic approach to drive large-scale personalized patient communications.  

• Client Challenge: A leading pediatric health system wanted to expand its community outreach and improve patient engagement, but its legacy CRM system lacked the scalability for growth and effective data management.
• Solution: We integrated Salesforce Health Cloud with the organization’s Marketing Cloud to create a centralized patient data platform, and we enhanced system functionality to optimize patient outreach and data management.  
• Outcome: This new multi-cloud system supports the organization’s long-term growth and outreach campaigns, streamlining workflows and enabling more targeted patient communications through improved data capabilities.

Our life sciences team is dedicated to supporting clients in the pharmaceutical, biotechnology, medical technology, and contract research organization sectors. With specialized expertise in patient journeys, medical devices, and artificial intelligence (AI), we focus on enhancing patient experiences and outcomes, accelerating software development, ensuring compliance in device delivery, and transforming the research and development process. As a result, we have driven innovative growth for 14 of the 20 largest pharmaceutical and biotechnology companies, as well as 14 of the 20 largest medical device firms.  

LEARN MORE: Intelligently Powering Digital Pathology Data and Workflows for Biosciences Imaging Leader 

Financial Services 

Alongside healthcare and life sciences, our experts spearhead innovation in the financial services industry, working with clients in banking, wealth and asset management, capital markets, and payments. Whether it’s navigating regulatory shifts, managing compliance, developing personalized experiences, or creating customer intelligence tools and data platforms, our solutions enable clients to enhance customer loyalty and trust, operational efficiency, and regulatory compliance.  

The largest financial institutions, including 18 of the 20 largest commercial banks and 16 of the 20 largest wealth management firms, have relied on us to create adaptable digital experiences that scale to evolving business needs and customer expectations.  

Perficient has helped financial services companies intelligently mine and optimize complex data for smarter insights and greater productivity.  

• Client Challenge: An independent investment management firm archived research information in PDF files and a central data warehouse, limiting data accessibility and efficiency. 
• Solution: We ingested the PDFs into the company’s Snowflake data cloud and built semantic models using Snowflake’s Cortex Analyst and Cortex Search GenAI tools to contextualize the data. We also optimized the data with large language models and created a user interface for queries. 
• Outcome: This AI-driven solution enhances productivity by mining information and generating contextually relevant natural language responses from a single data source, improving data confidence and accessibility.

READ MORE: Explore Perficient’s Client Success Stories 

Harnessing AI to Transform Client Outcomes and Achieve Operational Excellence 

To foster successful client outcomes, our team combines deep industry knowledge with relevant data to shape our strategy. In today’s dynamic landscape, AI is redefining the future of technology. With our cross-industry expertise, we empower clients to leverage AI for maximized value, improved operational efficiency, and accelerated growth. Perficient’s Generative AI Unlocked video series showcases AI’s cutting-edge capabilities and provides expert insights on its innovative impact across various sectors. In addition to our thought leadership, our award-winning AI practice offers tailored solutions that enhance user experience and boost productivity.

Manufacturing companies often encounter customer service challenges due to product complexity and the need for specialized assistance. Perficient has helped clients in this sector by integrating AI into their customer service touchpoints, enhancing efficiency and support. 

• Client Challenge: A multinational manufacturing conglomerate struggled with customer service, receiving about 2.5 million inbound service calls and emails annually that led to slow response times and high labor costs. It aimed to reduce contact volume by shifting customers to self-service options.  
• Solution: We leveraged our Envision Framework and Journey Science Methodology to develop a comprehensive roadmap. Next, we implemented Amazon Connect along with AWS AI and machine learning services to enhance customer service through a chatbot, virtual agent, and personalized automated email replies. The chatbot uses customer data and indexed knowledge bases for natural responses to customer inquiries, while the virtual agent utilizes product data, web content, and FAQs to suggest responses and next steps to resolve customer issues.  
• Outcome: Our multi-year strategy achieved $25 million in traceable annual cost savings, $61 million in projected revenue lift, and a 39% reduction in inbound customer service calls and emails.

In the automotive industry, we have applied AI to personal selling tactics to guide customers through the car buying journey.  

• Client Challenge: A leading automotive company aimed to differentiate itself by using an AI agent to deliver personalized vehicle recommendations tailored to customers’ specific needs.
• Solution: We developed and integrated a conversational virtual agent into the company’s website using the Google Vertex platform, which leverages thousands of data points to assist customers through the vehicle exploration process.
• Outcome: The client experienced a 28% increase in user engagement, with our solution driving 5,000 conversations per day, 76% more time spent on the site, and 171% more page views per visit.  

LEARN MORE: Accelerating Value with GenAI Innovation for Major Not-For-Profit Health Insurer 

Perficient’s obsession with outcomes fuels our commitment to client success. By building strong relationships, we develop customized solutions that go above and beyond their expectations. Our dedication to innovation and excellence enables us to adapt quickly and deliver meaningful results across diverse industries. Stay updated for the next blog in our series, where we’ll explore how Perficient is forging the future and making a difference in our communities.  

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A friend recently shared a research paper from Oxford Academic about Large Language Models (LLMs) and their human-like biases. I found it fascinating.

The article explains how some groups use LLMs to simulate human participants. Since these models are trained on human-generated data, they can emulate human responses across diverse psychological and behavioral experiments.

It further notes that LLMs favor socially desirable responses that align with the Big Five personality traits – including agreeableness. Notably, during their experiments, LLMs would modify the responses if the researcher appeared to be evaluating it.

Confidently Wrong AI (Posturing)

I had planned to write about the phrase “confidently wrong,” which we hear often when talking about Artificial Intelligence (AI) models. Combined with the concept of AI hallucinations, this can mislead people who are expecting reliable answers from these tools.

More and more users are favoring AI over traditional online search. No doubt you’ve noticed that Google now shows an AI response above the SERP. This experience is often faster and feels more natural than manual trial-and-error clicks through links.

However, it becomes risky when the LLM is wrong. Once the AI selects an answer, it may be reluctant to admit it was wrong. Even if you challenge a correct statement, the LLM might apologize and change its answer to be agreeable. Users need to be cautious and validate the information received.

Confidently Incorrect Humans (Learning)

I have an 11-year-old son who is hell-bent on being contradictory. If I say the sky is blue, he’ll point out that it can be gray, yellow, orange, red, purple, or black. He’s not wrong, but he is frustratingly contrarian. When I tell him he’s being contradictory, he says, “No I’m not!” Even when he is flat wrong, he won’t let it go!

You’ve probably also heard the phrase “fake it ‘till you make it.” It’s meant to help those who are learning and to ease imposter syndrome. I used to hate the phrase because I prefer transparency. I’d rather hear “I don’t know” than to incorrectly think you have it under control. However, I now appreciate that it helps escape a negative mindset.

AI Confidently Mimicking Humans (Refining)

The Oxford Academic article points out that AI learns behaviors from us! It’s mostly trained on data created by humans, so it picks up our natural tendencies. If our writing is polite and avoids confrontation, the AI will be trained to follow those patterns.

Additionally, humans help validate the training – even crowdsourced to the general public. When you give a thumbs up to a response from an LLM, you’re teaching it what you prefer to see in the output. Over time it will lean toward agreeableness. While it’s not conscious, AI is learning to mimic humans.

The Con Man (Tricking)

The term “con man” or “con artist” comes from the word “confidence.” It refers to the act of manipulating or persuading people into believing something false.

Con artists have existed as long as humans have been able to communicate. There are fun ones, like magicians who amaze us with the spectacular. Then there are the bad kind that scam people out of their life savings. Even reputable sources like the BBC, CNN, Forbes, The Atlantic, and others can sometimes spread misleading information, confusing us even further.

AI is trained on a mix of data, including quality sources like scientific research papers but also the text of trolls attacking everything, and your mother, on Reddit. It learns from both the best and the worst of humanity.

The Confident Leader (Inspiring)

Confidence has two sides. We’re often inspired by confident leaders. When leaders seem uncertain, many people get nervous and may leave the group. It’s clear that we prefer a strong front over complete transparency.

Don’t get me wrong… We know that transparency is important. A quick Google search shows droves of experts saying that transparency is the best policy. We also understand the consequences of an over-confident leader.

But at the end of the day, we’re just regular folks looking for stability and security. Time and again, we’re attracted to leaders who exude confidence and instill inspiration.

Conclusion

We often laugh at poorly executed AI – it makes us feel superior. The same goes for poorly articulated statements from people – it makes us feel superior. We’ve all seen how we collectively attack and criticize each other online.

AI learns from us. It relies on us for continual improvement. It adopts our positive traits but can also mimic our negative behaviors.

As we continue to use AI, it will become a bigger part of our lives. Often, we’ll seek out the interaction, while other times, hidden AI will quietly work in the background. Just like with other people, we need to validate our interactions with AI. Trust, but verify.

……

If you are looking for a strong partner that loves AI but will verify results, reach out to your Perficient account manager or use our contact form to begin a conversation.

Driven by factors ranging from generational wealth transfer to technological advancements, Perficient’s Principal in Wealth and Asset Management, Gerardo Montemayor, provides valuable insights into the wealth management trends set to transform the industry in 2025.

Wealth Management Trend #1: Hyper-Personalized Experiences With AI

Driven by advancements in AI, big data, and machine learning, hyper-personalization is reshaping wealth management firms’ ability to tailor financial services based on individual preferences, behaviors, and investment goals. This transformation has been accelerated by a confluence of shifting client demographics and expectations along with mounting competitive pressure from emerging tech-driven players, financial product innovation, and industry consolidation. To remain competitive, wealth managers are driven to strengthen client relationships, redefine client experience, foster loyalty, deepen engagement, and drive long-term growth.

Recommended Approach: By leveraging user research and behavioral analytics, journey sciences help define and optimize the user journey, supporting more-meaningful hyper-personalization that addresses customer needs and preferences at each touchpoint. AI, machine learning, and big data analytics for predictive insights and personalized financial strategies require well-governed, securely democratized data. Cloud computing and modern data integrations unlock the benefits of scalable data management and real-time collaboration while advanced visualization tools and API integrations with third-party data equip teams to isolate priority scenarios and accelerate deeply tailored outreach.

Learn More: Optimize Every Touchpoint

Wealth Management Trend #2: Client Advisor Empowerment

Empowered client advisors drive AUM growth. Wealth managers, meanwhile, aim to boost advisor collaboration and, together, grow their book of business. To be effective, both groups need the tools and insights to drive faster progress, efficiently delivering high-quality, proactively personalized service in less time and with fewer manual tasks. With those efficiencies in place, these important business contributors can focus on strategic, high-value activities—ultimately driving client acquisition, retention, and long-term growth.

Recommended Approach: Empower client advisors with cutting-edge technologies that enhance efficiency, provide deeper insights, and strengthen client engagement, transforming raw information into actionable insights that support personalized investment strategies and tailored financial advice. Strategically implemented AI, augmented analytics and visualizations can accelerate smarter decision-making, while intelligent automation equips teams to streamline processes, enhance efficiency, and drive significant cost savings. Ease client management and foster seamless collaboration with compliant CRM and messaging tools. Mobile and cloud-based solutions enable anytime, anywhere, access to critical data and tools, ensuring greater flexibility.

See Also: Speeding Insights and Powering Investment Experience

Wealth Management Trend #3: Navigating ESG in a Shifting Political Landscape

The political environment significantly influences the integration of Environmental, Social, and Governance (ESG) considerations in wealth management. With the current administration rethinking of ESG, regulatory frameworks are rapidly evolving, creating both challenges and opportunities for investors and their advisors. Despite federal pushback, state-level initiatives and international regulations, particularly from the EU, continue to drive the importance of ESG. Investors are increasingly focused on aligning their portfolios with personal values, financial goals, and global sustainability trends, even as they navigate a complex and often contradictory regulatory environment. This dynamic underscores the need for adaptability and vigilance in ESG investing.

Recommended Approach: Build regulatory agility without losing site of investors’ expectations and consider how digital efficiencies and advisor enablement can differentiate your brand experiences. Advanced analytics and AI can provide personalized scoring, risk assessment, and greenwashing detection, ensuring transparency and data-driven decision-making. Big data integration is crucial for obtaining a unified view of diverse data sources, supporting robust ESG insights – particularly as ESG reporting becomes more fragmented when no longer mandated. Additionally, proactive regulatory insights can equip your organization to mitigate risks and capitalize on new opportunities. By staying agile and informed, investors can continue to drive long-term growth and meaningful impact through sustainable investment strategies.

Success In Action: Elevating ESG in Wealth Management Portfolios

Wealth Management Trend #4: Omnichannel Access

Digital platforms are reshaping how investors monitor portfolio progress, driven by expectations for convenience, accessibility, and personalized services. Omnichannel tools enhance client experience by offering seamless access to investment portfolios, performance insights, and account management tools. These platforms not only serve existing clients but also expand wealth management services to a broader audience. Additionally, they enable a wider range of investment products, support a do-it-yourself (DIY) model, and facilitate more strategic client collaboration, allowing for deeper engagement and more effective financial planning.

Recommended Approach: Mobile and cloud-based solutions form the backbone of digital platforms, providing seamless, anytime-anywhere access to critical data and tools for greater flexibility. Automation platforms support the DIY model, streamlining administrative tasks and enhancing efficiency. Additionally, custom development allows wealth managers to differentiate their services, offering tailored solutions and personalized collaboration channels to strengthen client relationships.

Explore More: Future-Proof Your Tech Investment

Wealth Management Trend #5: Optimizing Operational Efficiencies

In the current macro-environment, wealth and asset management firms face rising costs, global volatility, increasing competition, and compliance challenges. Despite expanding industry assets and revenues, operating margins are under pressure, making profitability a top priority. Firms are also navigating a landscape of mergers and acquisitions, strategic outsourcing, and vendor consolidation to hone core strengths and boost a competitive edge. These business pressures drive the need for digital investments to enhance efficiency and maintain profitability.

Recommended Approach: Leverage a power combination of process mining and automation to streamline operational workflows and improve experiences. Process mining helps identify and prioritize inefficiencies, errors, and delays in complex business processes that are prime for optimization. Automation platforms, in turn, reduce costs by intelligently handling time-consuming tasks in back-office operations, portfolio management, reporting, and compliance, allowing advisors and client support teams to focus on what matters most: nurturing meaningful relationships and delivering value. By enhancing data quality and supporting advisors’ strategic planning efforts, these technologies drive scalability and sustainable growth, enabling firms to remain competitive. These advantages can be further amplified with a digital partner that offers robust, agile global delivery capabilities and platform relationships, maximizing cost savings without compromising quality and ensuring rapid deployments that drive business outcomes.

You May Enjoy: Transform Your Business With Cutting-Edge AI and Automation Solutions

Embrace the Future and Transform Your Wealth Strategy

We empower wealth and asset managers with proactive insights, hyper-personalized experiences, and proactive risk management to drive sustainable growth.

• Business Transformation: Develop and optimize strategies and processes for efficient wealth management operations.
• Modernization: Upgrade technology and processes to ensure seamless integration and enhanced, streamlined client advisor experiences.
• Data + Analytics: Harness data-driven insights for personalized investment strategies, client collaboration, and operational efficiency.
• Risk + Compliance: Implement robust strategies to safeguard investor relationships and ensure regulatory adherence.
• Consumer Experience: Enhance engagement and satisfaction with tailored advisory services and digital tools.

Discover why we have been trusted by 16 of the 20 largest wealth management firms. Explore our financial services expertise and contact us to learn more.

In 2025, communications, media, and entertainment companies are continuing to focus on shared goals and strategies to drive innovation, improve customer experience and reduce operating costs. The most successful companies in the industry are not doing it alone – they’re working with trusted advisors in the IT space to strategize and implement the most effective solutions. 

Our expertise in the communications, media, and technology (CMT) industry has been growing and strengthening due to extensive work with clients in the space and investment in cutting-edge technology. As a result, Perficient has once again been recognized by a leading global technology research and advisory firm’s report highlighting notable telecom, media, and entertainment industry consultancies in the U.S. and Europe. 

“As communications and media companies are looking to leverage AI, IoT, Cloud, augmented experiences and more, Perficient is partnering with them to consistently deliver positive results.” Kevin Colletti, Director of Strategy and Communications + Media + Technology Industry Lead, Perficient. 

Experts Guiding the Transition from Telco to Techno 

Perficient is not only closely watching but actively participating in the major shift from “Telco to Techno,” which involves the evolution from telecommunications to technology that many of the leading brands are experiencing. Business model shifts and expansions into new markets are calling for the full range of support from Perficient’s end-to-end capabilities and leveraging expertise in other industry verticals. 

Open Partner Ecosystems and 5G 

Specifically, open partner ecosystems are changing the game, especially with the evolving  technology making it possible for telecom operators to use different suppliers for multiple parts of the network. With many companies starting to decouple infrastructures and service layers, this has led to many new opportunities. Perficient is supplying what these organizations need to take advantage of expanded network access and new revenue streams and build sustainable growth. 

While the road may be longer and more winding than expected, achieving near-limitless connectivity will pave the way for new services and innovations. To reap these benefits, telecommunications will need a trusted partner who can guide them in efforts like exponentially increasing network capacity, improving data throughput and spectrum, and reducing latency and energy consumption. 

AI-Enabled Immersive and Personalized Customer Experiences 

When it comes to curating content and providing excellent products and services to customers, personalization and immersive experiences are important differentiators. Customer experiences can be made stronger by better understanding the target audience, gathering and analyzing data on their behaviors and preferences, and using those insights to refine offerings and journeys. Personal touches can be made across their experiences, from products and services recommendations to their interactions with customer service. In return, loyalty will strengthen in a customer base that has high expectations. 

To do this more effectively and at scale, organizations in the industry are delving into how AI can enable more immersive and personalized experiences. Across industries, we’ve developed GenAI solutions that ensure the consumer feels heard, seen, and understood. By leveraging AI foundation models, we’ve created virtual assistants that mine a library of ingested documents and return tailored, reliable answers based on the customer’s plan. Further, we’ve integrated virtual assistants into websites that help narrow search results and navigate visitors through their buying journey to increase conversions and customer satisfaction.  

With the pace at which telecom, media, and entertainment companies are expanding, there’s no shortage of work that needs to be done. Learn more about Perficient’s communications, media, and technology expertise. 

Generative AI. The phrase itself conjures images of intricate artwork, realistic text, and even code springing forth from the digital ether. It’s not just hype; generative AI is rapidly transforming industries, offering unprecedented potential for innovation and efficiency. Unlike traditional AI models that primarily classify or predict, generative AI creates new content, from images and text to music, code, and even 3D models. This capability is unlocking a wave of use cases across diverse sectors, promising to reshape how we work, create, and interact with the world around us. This blog delves into the transformative power of generative AI, exploring its applications across multiple industries, examining its implementation, weighing its pros and cons, and ultimately, assessing its profound impact on the future. 

What is Generative AI? 

At its core, generative AI leverages sophisticated machine learning models, often based on deep learning architectures like Generative Adversarial Networks (GANs) and transformers, to learn the underlying patterns and structures of input data. Once trained, these models can generate new data that shares similar characteristics with the training data. Think of it like an artist studying the works of the masters. After absorbing the techniques and styles, they can create original pieces that reflect those influences. Generative AI models operate in a similar fashion, learning from vast datasets to produce novel outputs. 

Use Cases Across Industries: 

Let’s explore the tangible impact of generative AI across several key industries: 

1. Healthcare:

• Drug Discovery: Generative AI can accelerate the drug discovery process by generating novel molecules with desired properties, predicting their efficacy, and optimizing their design. This can drastically reduce the time and cost associated with bringing new drugs to market. 

• Personalized Medicine: By analyzing patient data, generative AI can create personalized treatment plans, predict disease risk, and even generate customized prosthetics or implants. 

• Medical Imaging: Generative models can enhance medical images, improve diagnostic accuracy, and even generate synthetic data for training other AI models, addressing the challenge of limited labeled data. 

• Virtual Assistants: AI-powered chatbots can provide personalized health advice, answer patient queries, and even monitor patients remotely. 

2. Creative Industries (Art, Music, and Entertainment):

• Content Creation: Generative AI can create stunning visuals, write compelling stories, compose original music, and even generate realistic voiceovers. This opens up new avenues for artists, writers, musicians, and filmmakers. 

• Game Development: Generative AI can be used to create realistic game environments, generate character designs, and even develop dynamic storylines, enhancing the player experience. 

• Marketing and Advertising: AI-powered tools can generate personalized marketing content, create targeted ads, and even design unique product packaging. 

• Fashion Design: Generative AI can create new fashion designs, predict trends, and even personalize clothing recommendations. 

3. Manufacturing:

• Product Design: Generative design tools can explore numerous design options, optimizing for factors like performance, cost, and manufacturability. This can lead to innovative and more efficient products. 

• Predictive Maintenance: By analyzing sensor data, generative AI can predict equipment failures and generate optimal maintenance schedules, minimizing downtime and improving operational efficiency. 

• Quality Control: Generative models can be used to identify defects in manufactured products, improving quality control and reducing waste. 

• Supply Chain Optimization: AI-powered tools can analyze supply chain data, predict demand fluctuations, and optimize logistics, improving efficiency and reducing costs. 

4. Finance:

• Fraud Detection: Generative AI can be used to detect fraudulent transactions by identifying patterns and anomalies that are difficult for humans to spot. 

• Risk Management: AI models can assess financial risk, predict market trends, and generate personalized investment recommendations. 

• Algorithmic Trading: Generative AI can be used to develop sophisticated trading algorithms that can adapt to changing market conditions. 

• Customer Service: AI-powered chatbots can provide personalized financial advice, answer customer queries, and even help with account management. 

5. Software Development:

• Code Generation: Generative AI can assist developers by generating code snippets, automating repetitive tasks, and even creating entire programs. This can significantly increase developer productivity. 

• Bug Detection: AI models can be used to identify potential bugs in code, improving software quality and reducing development time. 

• Automated Testing: Generative AI can create test cases and generate realistic test data, simplifying the testing process. 

• Documentation Generation: AI can automatically generate documentation for code, making it easier for developers to understand and maintain software. 

Implementing Generative AI: 

Implementing generative AI is not simply a matter of plugging in a pre-trained model. It requires a strategic approach, encompassing data collection and preparation, model selection and training, and deployment and monitoring. 

• Data is King: Generative AI models thrive on data. The quality and quantity of training data are crucial for the model’s performance. Data collection, cleaning, and preprocessing are essential steps. 

• Model Selection: Choosing the right model architecture is critical. GANs, transformers, and variational autoencoders (VAEs) are just a few examples, each with its strengths and weaknesses. The choice depends on the specific application and the available data. 

• Training and Tuning: Training a generative model requires significant computational resources and expertise. Fine-tuning the model’s parameters is essential to achieve optimal performance. 

• Deployment and Monitoring: Once trained, the model needs to be deployed in a production environment. Continuous monitoring is essential to ensure the model’s performance and identify any potential issues. This often involves setting up feedback loops to refine the model over time. 

Pros of Generative AI: 

• Innovation and Creativity: Generative AI can unlock new levels of creativity and innovation, enabling the creation of novel products, services, and experiences. 

• Increased Efficiency: Automation through generative AI can streamline processes, reduce costs, and improve efficiency across various industries. 

• Personalization: Generative AI can personalize experiences, tailoring products, services, and content to individual needs and preferences. 

• Problem Solving: Generative AI can help solve complex problems by generating new solutions and exploring different possibilities. 

• Accelerated Development: In areas like drug discovery and software development, generative AI can significantly accelerate research and development cycles. 

Cons of Generative AI: 

• Bias and Fairness: Generative models can inherit biases from the training data, leading to unfair or discriminatory outputs. Addressing bias is a critical challenge. 

• Ethical Concerns: The ability of generative AI to create realistic fake content raises ethical concerns about misinformation, deepfakes, and intellectual property. 

• Computational Resources: Training large generative models requires significant computational resources, making it accessible primarily to organizations with substantial computing power. 

• Explainability: Understanding how a generative model arrives at a particular output can be challenging, making it difficult to interpret and trust the results. This lack of explainability can be a barrier to adoption in certain fields. 

• Job Displacement: As generative AI automates tasks, there are concerns about potential job displacement in certain industries. However, it’s also argued that it will create new job opportunities in other areas. 

Addressing the Challenges: 

While the challenges are real, they are not insurmountable. Researchers are actively working on addressing bias, improving explainability, and developing more efficient training methods. Ethical guidelines and regulations are also being developed to ensure the responsible use of generative AI. 

The Future of Generative AI: 

The future of generative AI is bright. As the technology continues to evolve, we can expect to see even more groundbreaking applications across industries. Generative AI is poised to revolutionize how we create, innovate, and interact with the world around us. We are only at the beginning of this transformative journey, and the potential is immense. Imagine personalized education tailored to each student’s learning style, on-demand creation of any product imaginable, or even AI-powered scientific breakthroughs that solve some of humanity’s greatest challenges. 

Conclusion: 

Generative AI is not just a technological marvel; it’s a powerful tool with the potential to reshape industries and redefine possibilities. While challenges remain, the benefits are undeniable. By understanding the capabilities and limitations of generative AI, we can harness its power to create a more innovative, efficient, and personalized future. As we move forward, it’s crucial to prioritize ethical considerations, address biases, and ensure that this powerful technology is used for the benefit of all. The generative AI revolution is underway, and its impact will continue to unfold in the years to come. It’s a space to watch closely, as it promises to transform the world as we know it. 

Artificial Intelligence (AI) has exploded into the mainstream, largely through chatbots and agents powered by Large Language Models (LLMs). Users can now have real-time conversations with multimodal AI tools that understand your text, voice, and images – even documents! The progress has been mind blowing, and tech companies are racing to integrate AI features into their products.

AI features today are being released with obvious interfaces and promoted heavily. My prediction though is that the future of AI will increasingly lean toward hidden, unnoticeable improvements to our daily experiences.

Visible AI – Current State

In our haste to compete, most AI tools today share a similar experience: either a chatbot interface or a feature trigger. What started as fresh and magical is becoming repetitive and forced.

ChatGPT, Bard, Claude… They all share the same conversational interface, resembling many lackluster customer service chatbots. The great ones now offer multimodal capabilities like voice or video input, but the concept is the same – back-and-forth dialogue.

Meanwhile, operating systems, web browsers, word processors, and other apps are tacking on AI features. Typically, these are triggered through a cool new AI icon to generate, summarize, or improve your content.

Invisible Enhancements – Yesterday & Today

Machine Learning (ML), on the other hand, has typically been rolled out as behind-the-scenes improvements that exponentially raise user expectations. Most users don’t even realize what ML processes are at play! Nearly invisible algorithms have transformed industries.

Google revolutionized search with its deceptively simple interface – a single search box delivering surprisingly targeted results. YouTube and Netflix ushered in streaming video, but they gained more attention surrounding their advanced recommendation engines. No more wandering the aisles of the local video store and reading the back of DVD cases!

The banking industry’s automated fraud detection is another perfect example of unobtrusive features. Instead of combing through your bank statement, you are notified in real time that your bank card has been disabled and the funds returned.

AI Ubiquity – Future State

AI is not going away – it offers tremendous opportunities for both businesses and consumers. Like subscription services where businesses cut costs and increase revenue, while the consumers enjoy better experiences, convenience, and options.

However, as with subscription services (access vs ownership), there are trade-offs. AI introduces trust issues, ethical concerns, and bias. Even so, the benefits are likely to outweigh the downsides. AI will reduce cognitive load in your daily life and have a far more natural interaction with digital systems. With AI, exciting products and benefits will be introduced.

Industries like healthcare, finance, automotive, retail, and energy are already exploring AI applications. At first these will be noticeable additions, but over time, AI will become seamlessly integrated and nearly invisible.

Conclusion

There will be bumps along the way (we should learn from our past). Legal disputes and unethical practices are inevitable, but progress will continue. We’ll need to get through some of the bad to reap the benefits – in the same way that fire is crucial to society but can also be destructive – we learn from our mistakes and move forward. Human creativity and innovation have brought us this far, and now we will integrate AI to amplify our potential.

I’m excited to see what is yet to come! We humans get nervous about game-changing technologies, but history shows that we are adept at adding safeguards and correcting our course. I think we’re going to surprise ourselves.

……

If you are looking for a digital partner who is excited about the future of AI, reach out to your Perficient account manager or use our contact form to begin a conversation.

We are thrilled to share the highlights of our very first Google meetup hosted at our company premises on 11 Aug 2024. The event was packed with insightful sessions, engaging discussions, and valuable networking opportunities. Here’s a recap of the day’s events.

The Event was organized by Google GDG Team was open to all and entries for the event registered through the GDG event page. Attendees were provided with key details such as location, time, and speaker information beforehand.

Welcome Greeting

The day began with a warm welcome from Saniya Imroze, setting a positive tone for the event. With over 200 attendees comprising students, professionals, and a Nagpur GDG Google team, the atmosphere was charged with enthusiasm and anticipation.

Virtual Google I/O Keynote

Next, we had the privilege of presenting a recorded video by Sundar Pichai, CEO of Google. He spoke about the Virtual Google I/O Keynote, highlighting the latest innovations and advancements in technology. His insights set the stage for the tech-driven discussions that followed.

Keynote from Perficient Director

Mr. Prashant Nandanwar (Directory Cloud & API) delivered an engaging session, providing an in-depth overview of our company’s expertise in Google products. He shared compelling case studies and elaborated on our strong partnership with Google. His presentation reinforced our commitment to innovation and excellence in the tech industry.

Perficient Director (Cloud & API) Mr. Prashant addressing the attendees

Prashant also introduces his Technical Team members who were specialized in their areas like AWS/GCP Cloud, API, Artificial Intelligent, UI Team to the audience present in the event.

Perficient Director – Prashant Nandanwar with his Team, along with Senior Human Resource Manager Mrs. Shweta Rawlani with her Team.

Session on Generative AI

Following the keynote, Mukta Paliwal delivered an engaging session on Generative AI and the current developments in this space. The audience was fascinated by the possibilities of AI and how it’s shaping the future of technology.

Unleashing Flutter with Gemini

Debasmita Sarkar explored the power of Flutter with Gemini. Her session was a deep dive into unleashing the potential of Flutter, and the audience left with a clear understanding of how to leverage this powerful framework in their projects.

Interactive Quiz by GDG Team hosted

One of the event’s highlights was an interactive quiz organized by the GDG team. The participants eagerly engaged in the quiz, and the winners were rewarded with delicious chocolates. The quiz added a fun and competitive edge to the day, and everyone enjoyed the spirited participation, it was conducted by Henay Lakhwani

Lunch

As the morning sessions concluded, attendees were treated to a delightful lunch, generously sponsored by the Google team. It was a great opportunity for everyone to relax, network, and discuss the exciting topics covered so far.

Exploring APIs with Postman Flows & Google Cloud Gemini

Post-lunch, Ali Mustafa and Aanchal Mishra led an insightful presentation on exploring APIs with Postman Flows and Google Cloud Gemini. Their session provided practical knowledge on utilizing these tools for efficient API management and development.

Beyond the Checkout: Unlocking Payment Success

Later in the day, Namrata More presented an enlightening session on “Beyond the Checkout: Unlocking Payment Success.” Her expertise in the field provided valuable insights into enhancing payment processes and ensuring smooth transactions.

Felicitation & Closing Keynote

The event concluded with a felicitation ceremony and a closing keynote by Saish Adlak, capturing the essence of the day and thanking the speakers, participants, and the Google Nagpur team for their contributions.

Event Highlights

The energy and excitement of the event were captured in photos, reflecting the success of our first Google meetup. Both the attendees and the Google GDG Nagpur team as well speakers were impressed by our company premises and appreciated the smooth organization of the event. Hosting this meetup for the first time was a significant milestone for us, and we’re proud of how well everything turned out.

The Google meetup was a 4-6 hour-long event filled with insightful discussions on Google Vertex, AI, and more. We’re excited about the possibilities ahead and look forward to hosting more such events in the future.

Stay tuned for more updates and check out the event photos we’ve shared below!

GDG Nagpur Team and Speakers with Mr. Prashant Nandanwar and Mrs. Shweta Rawlani

Event Attendees