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AI isn’t just for tech giants anymore. From local logistics firms to fast-growing e-commerce brands, companies across every industry are discovering how AI-powered automation can save time, cut costs, and scale operations more efficiently.

But while the potential is exciting, many business owners still ask:

Where do I actually start with AI automation?

This article explains exactly how to automate business processes with AI without hype, jargon, or the need for a full-time data scientist.

🤖 What Does AI Automation Really Mean?

AI automation is using artificial intelligence to carry out repetitive tasks, make data-based decisions, or streamline workflows — without manual intervention.

It’s not just about robots or futuristic systems. In practical terms, it means:

• Automatically classifying support tickets
• Predicting stock shortages
• Writing marketing emails
• Processing invoices
• Routing leads to the right sales rep

💡 AI augments your team, freeing them to focus on what humans do best — strategy, creativity, and relationship building.

🧠 The Most Common Business Areas to Automate with AI

Let’s explore the areas where AI is already delivering substantial ROI for small and medium-sized businesses:

1. Customer Service

• Chatbots: Answer common questions instantly, 24/7
• → Tools: ChatGPT API, Intercom, Tidio
• Email classification & response: Auto-sort incoming messages and suggest replies
• → Tools: Zendesk + AI, Front + GPT plugins

📈 Stat: According to IBM, businesses using AI chatbots see up to 30% reduction in support costs.

2. Sales & CRM

• Lead scoring: Automatically prioritize high-quality leads using behavioral data
• Follow-up automation: Generate emails or task reminders for sales reps
• Pipeline forecasting: Use past data to predict close rates or churn

Popular tools: HubSpot AI, Salesforce Einstein, Apollo.io + GPT automation

3. Marketing

• Content generation: Write blog posts, ads, and product descriptions with AI writers
• Email personalization: Tailor messaging at scale
• Ad performance prediction: Analyze creatives and predict ROI

📈 According to McKinsey, companies that personalize marketing using AI increase ROI by 5–10x compared to static campaigns.

4. Finance & Admin

• Invoice extraction and processing
• → Tools: Rossum, Kofax, QuickBooks + OCR
• Expense categorization
• → AI reads and tags expenses without manual input
• Payroll & compliance automation

5. Inventory & Supply Chain

• Demand forecasting: Predict product demand based on seasonality and trends
• Automated reordering: Trigger restocks based on usage or stockouts
• Route optimization: AI helps logistics teams plan deliveries more efficiently

🛠️ Step-by-Step: How to Start Automating with AI

✅ Step 1: Identify Repetitive, High-Volume Tasks

Look at workflows that are:

• Manual and repetitive
• Time-consuming
• Prone to human error
• Involve large amounts of data

Example: A real estate firm receives 100+ daily inquiries. A simple AI chatbot filters them based on buyer intent before sending them to agents.

✅ Step 2: Choose the Right Tools

You don’t need to build your own AI model from scratch. Instead, use ready-made platforms that integrate with your existing tools.

Popular no-code/low-code AI tools:

• Make (Integromat)
• Zapier with OpenAI
• Microsoft Power Automate + AI Builder
• Notion AI, Google Workspace AI tools
• Custom GPT-based assistants

✅ Step 3: Start Small — Automate One Workflow

Begin with a single use case that’s easy to track and improves productivity.

For example: “Automatically generate a daily summary of unread customer support tickets and categorize them using AI.”

✅ Step 4: Monitor, Improve, Scale

Track KPIs:

• Time saved
• Cost reduced
• Error rate before vs. after
• Team feedback

Once successful, expand automation to more processes.

✅ Final Thoughts

AI automation isn’t just the future — it’s the present competitive advantage for companies ready to work smarter, not harder. Whether you’re managing leads, processing documents, or optimizing inventory, AI can take tedious work off your team’s plate and help you scale without burnout.

You don’t need a data science team to get started. You just need a real problem, the right tools, and the willingness to test, learn, and iterate.

🚀 Want to Automate Your Business but Don’t Know Where to Start?

At Onix, we help companies audit their operations, identify automation opportunities, and integrate AI into CRM, ERP, and internal workflows. Whether it’s custom automation or AI integration into existing tools, we’ve got your back.

📩 Contact us today for a free AI automation consultation tailored to your business needs.

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How to Automate Business Processes with AI: A Practical Guide for Business Owners was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Few things are more valuable in business than seeing what’s coming next. Whether predicting sales, managing inventory, or allocating resources, the ability to forecast accurately can make the difference between thriving and surviving.

Traditionally, forecasting has relied on spreadsheets, historical averages, and human instinct. But in today’s fast-paced and data-driven world, these methods are often too slow, shallow, or simply inaccurate. That’s where AI-powered business forecasting comes in.

📉 Why Traditional Forecasting Falls Short

Even the most experienced business leaders make decisions based on delayed reports, incomplete data, or best guesses. While this worked in the past, it’s no longer enough when:

• Market conditions shift overnight
• Customer behavior changes rapidly
• Supply chains get disrupted without warning
• Seasonal trends are no longer predictable due to external shocks (e.g., COVID-19, inflation, geopolitical shifts)

Relying on static models means missed opportunities and reactive decisions. Businesses need forecasting methods that are dynamic, fast, and constantly learning.

🤖 What Makes AI Forecasting Different?

AI forecasting uses machine learning algorithms to analyze vast amounts of real-time data. Unlike traditional models, AI doesn’t just look backward — it identifies patterns, learns from new data, and adapts continuously.

It can pull insights from:

• Historical performance
• Real-time sales data
• Marketing campaigns
• Weather patterns
• Social media sentiment
• Web traffic and customer behavior

These data points are used to generate highly accurate, short — and long-term forecasts that evolve with your business.

📊 Where AI Forecasting Drives Results

AI forecasting isn’t limited to large enterprises anymore. Startups, retailers, logistics companies, and manufacturers are already using it to:

1. Predict Sales with Higher Accuracy

AI helps determine which products will sell, in which regions, and during which periods — using variables like promotions, customer segments, or economic indicators. This avoids overproduction and understocking.

According to McKinsey, retail businesses using AI forecasting have reduced inventory errors by up to 50%.

2. Optimize Inventory and Reduce Waste

Knowing what’s needed and when leads to better stock control. AI can forecast demand shifts and automatically adjust purchasing or restocking strategies.

3. Improve Cash Flow Forecasting

By analyzing revenue trends and payment cycles, AI models can help finance teams project cash availability more accurately, helping avoid shortfalls or idle funds.

4. Plan Marketing and Promotions Strategically

AI can simulate pricing or promotion strategies to forecast their effect on sales. This allows marketers to focus on campaigns likely to drive the highest ROI.

5. Allocate Resources More Effectively

From staffing to delivery schedules, AI forecasts can anticipate spikes in demand and adjust labor or logistics accordingly.

🧠 Real-World Example: AI Forecasting in Action

A mid-size e-commerce brand struggled with excess inventory during slow months and stockouts during peak periods. After implementing an AI-powered demand forecasting system, the company was able to:

• Reduce overstock by 30%
• Cut stockouts by 45%
• Increase monthly revenue by 12%
• Save 15 hours per week in manual planning

The AI model pulled data from sales, advertising platforms, and web traffic, learning over time to make more accurate predictions — even adapting when customer preferences shifted or suppliers delayed shipments.

🛠️ Tools That Make It Possible (Without a Data Scientist)

You don’t need an internal AI team to get started. Today, several platforms offer AI forecasting features designed for business users:

• Google Cloud Forecasting
• Amazon Forecast
• Microsoft Azure ML Forecasting
• MonkeyLearn (for text-based forecasting)
• Kausa, Prevedere, and Futrli (for SMB-focused forecasting)

Many CRM and ERP platforms are now integrating AI-powered modules as well, especially in the retail, finance, and logistics sectors.

🧩 What You Need to Make AI Forecasting Work

To get the most out of AI, businesses need three things:

1. Good Data: Clean, structured, and relevant historical data is essential. Garbage in = garbage out.
2. Defined Objectives: Are you forecasting sales? Cash flow? Marketing ROI? Be clear on your focus.
3. Feedback Loop: Forecasts need validation. Compare predictions to real results and refine continuously.

✅ Can AI Forecasting Improve Your Bottom Line?

Absolutely — if used correctly.

It’s not magic but a powerful way to reduce uncertainty, improve efficiency, and make smarter, faster decisions. Companies that adopt AI forecasting early often discover that it doesn’t just improve accuracy — it transforms how decisions are made at every level.

In uncertain times, anticipating rather than reacting becomes a significant competitive advantage.

🚀 Ready to Bring AI Forecasting into Your Business?

At Onix, we help businesses integrate AI solutions like forecasting into their existing systems — whether they’re using spreadsheets, ERP, or cloud data. From setup to training, our team guides you step by step so you can forecast smarter, reduce waste, and plant growth with confidence.

📩 Contact us today to explore how AI forecasting can support your team, boost efficiency, and impact your bottom line — without disrupting your current operations.

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AI for Business Forecasting: Can It Improve My Bottom Line? was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

The AI Search Strategy That Beat Airbnb and VRBO Combined

Most marketers dream of outranking industry giants, but few crack the code on AI search optimization.

Lake.com didn’t just crack it , we shattered it, quadrupling our citation share from 8.6% to 35.0% in AI-generated travel recommendations while Airbnb and Vrbo watched their combined dominance crumble.

The secret wasn’t throwing money at ads or chasing viral content.

Instead, we engineered a systematic approach to semantic search optimization that made AI models choose us over billion-dollar competitors in destination queries, pricing comparisons, platform integrations, and booking tutorials.

Here’s the exact playbook we used to become the most-cited travel brand in AI search — and why your competitors probably aren’t ready for what’s coming next.

Background and Evolving Landscape

When we launched Lake.com, our goal was simple: create a vacation‑rental platform that reflects the real experience of being by the water. I wanted this venture to be laser‑focused on one type of traveler — the outdoor explorer who craves the peace of a lakefront getaway.

We deliberately avoided chasing every market segment; instead, we committed to lakefront and waterfront stays, curating properties within 15 minutes of the water and highlighting key amenities, such as kayaks and boat rentals.

That focus paid off in traditional SEO. But by early 2025, I saw a new challenge looming: travelers were asking ChatGPT, Perplexity, and Google’s AI Overviews for vacation advice, and Lake.com was rarely mentioned.

I knew we could no longer rely solely on ranking high on search results pages — we needed to be inside the AI answers themselves.

Mapping the AI Journey

To understand how people search in the age of generative AI, we worked with Profound and simultaneously with Growth Marshal. Profound’s Conversation Explorer revealed that the vacation‑planning journey breaks into three micro‑moments — “I want to know,” “I want to go,” and “I want to do.”

In the past, I had used those stages as a marketing framework, but Profound gave us the visibility to see the same pattern in AI prompts. We set three goals:

• Validate how users search for lake travel across those micro‑moments.
• Benchmark our visibility versus larger platforms.
• Discover new content opportunities from real conversation trends.

Conversation Explorer and Answer Engine Insights showed not only where Lake.com appeared, but, more importantly, where we didn’t.

We realized we needed more event‑driven articles (think fishing tournaments, music festivals, antique boat shows) and evergreen guides that didn’t require rebuilding URLs or re‑earning authority each year.

We doubled down on destination content for “drive‑to” lakes within two hours of major cities and filled obvious gaps like “summer travel,” “family vacation ideas,” and “outdoor activities near water.”

From Micro‑Moments to Macro Wins

Armed with this data, we re‑engineered our content strategy. We aligned articles to the three stages: research, booking, and activity planning, and embedded structured data and FAQs that AI models could easily parse.

The Strategy Revealed

I Want To Know: Capturing Curiosity in the Research Stage

In the early stages of travel planning, consumers are driven by curiosity and inspiration. They enter the “I Want to Know” phase — a moment marked not by urgency, but by discovery.

Our content strategy addressed this phase by positioning Lake.com as a trusted editorial guide rather than just a transactional platform. We crafted immersive, story-rich content that answered unspoken questions: Where could I go that aligns with my interests? What makes that place special? When’s the best time to go?

To address this intent, we created articles like “Best Lakes for Canoeing: An Adventurer’s Guide,” which spotlighted top-tier paddling destinations from Lake Tahoe to Lake Louise.

These were not mere lists — they were curated experiences, grounded in geographical richness and aimed at paddlers seeking excitement, solitude, or both. We also leaned into emotionally resonant group travel content, like “Most Family-Friendly US National Parks,” designed to foster a sense of shared possibility. We also invited expert contributors to round out those articles.

By including multigenerational activities, we helped prospective travelers envision how a lakeside getaway could serve as the backdrop for deeper connection.

Finally, to expand seasonal relevance, our “Best National Parks for Stargazing” piece mapped low-light-pollution zones near national parks, blending scientific credibility with travel escapism.

Each article was meticulously structured with embedded FAQs and schema markup, enabling AI models to parse and deliver our content at the precise moment users asked their first exploratory query.

I Want To Go: Serving the Savvy Comparison Shopper

Once inspiration turns into intention, travelers shift into the “I Want to Go” phase. Here, users aren’t browsing, they’re evaluating.

They compare location details, amenities, pricing models between platforms, and prices of the vacation rentals themselves, as well as cancellation policies, and reviews. It’s where content must do the heavy lifting of persuasion and precision.

To meet this demand, we constructed high-value, information-dense destination guides such as “Hot Springs Vacation Rentals,” which grouped properties by preset filters such as family-friendly, pet-friendly, and luxury properties.

Beyond providing a helpful description of the area that aimed to uncover uncommon knowledge, we also added FAQs that broke down everything from swimming conditions and local wildlife to seasonal weather patterns.

This helped travelers reduce ambiguity and feel confident in choosing one lake destination over another.

Additionally, we published utility-first articles, such as “Understanding the Booking Process,” which provides a breakdown of how to leverage search filters, interpret visual listings, read nuanced guest feedback, and optimize for pricing and policies. These how-to articles were powered by a WordPress knowledge base plugin called BasePress.

This was content designed for customer enablement, aimed at building trust and reducing friction. Another section, “Guest Center,” highlighted how vacation rental websites work and described the features in detail with lo-fi screenshots, helping make the content evergreen.

Each page in the Guest Center detailed product-level differentiators such as interactive maps, secure payment protocols, and advanced filtering tools. These assets helped transform passive browsers into active bookers by answering the hard questions before they had to be asked.

I Want To Do: Deepening the Experience After Booking

The third moment in the traveler’s journey — the “I Want to Do” phase — unlocks the opportunity for lifecycle content. At this stage, the transaction has already occurred.

Now, the user wants to enhance the trip by discovering festivals, family-friendly outings, cultural landmarks, and community events that will make the vacation memorable.

We embraced this phase by publishing round-up style articles that served as local experience guides, such as “Events in Gatlinburg — Festivals, Food and Fun” or “Anakeesta vs Ober Mountain: Which Smoky Mountain Attraction is Better?”

These served as both an itinerary planner and a destination primer, spanning popular lake destinations, hotspots like Lake Tahoe, Lake of the Ozarks, Finger Lakes in Upstate New York, and dozens more.

Equally, we featured highly localized content around specific events, such as the “Muskoka Antique Boat Show,” showcasing unique happenings like festivals, live music, arts & craft shows, and annual celebrations — each designed to help guests plan their stay around authentic, regional experiences.

Hyper-niche content, such as the “Bass Pro Shops Bassmaster Classic,” took it further by serving targeted interests with precise logistical data on registration, timing, and rules. These weren’t generic attractions — they were moments travelers could build a trip around.

By structuring our content to support the full lifecycle — from inspiration to booking to experience — we positioned Lake.com not just as a booking engine, but as a travel partner that anticipates the evolving needs of nature-loving, adventure-seeking explorers.

Each micro-moment was treated not as an endpoint but as a touchpoint.

And by optimizing for searchability, structure, and narrative relevance, we turned fleeting searches into lasting brand engagement.

We weren’t sure we could beat Airbnb or Vrbo, but we knew we had to be in the conversation at each stage of the travelers’ journey.

The Results

The results were dramatic. Within weeks, Lake.com achieved:

• 5× increase in organic traffic during peak season.

• A surge in AI answer citations: Our AI visibility score jumped 33.5 percentage points to 47%, surpassing Airbnb’s 41.9% and Vrbo’s 28.7%. In just 21 days, we expanded our U.S. AI answer share 15×, vaulting from 0.9% to 13.5%. For unbranded lake‑house terms, we now appear in half of all AI answers — even against giants like Vrbo and Expedia.

• 30% visibility on unbranded lake‑travel terms and ~50% visibility on lake house‑specific prompts.

• #1 for Citations Across ChatGPT, Google AI Overviews, Microsoft CoPilot, and Perplexity

Lake Achieves Dramatic Surge in AI Visibility and Citations, Overtaking Airbnb in Vacation Rentals

Lake’s overall presence in AI-generated answers within the United States vacation rentals sector rose sharply, moving from third place to clear category leadership over Airbnb and Vrbo by July 5, 2025. Citation share for Lake’s domains soared from 8.6% to 35.0% during the period, with core pages on integrations and pricing among the most frequently referenced resources. The brand now leads across key topics including ‘Brand’, ‘Pricing and Fees’, ‘Comparisons’, and ‘Integrations’, reflecting a significant expansion of both visibility and authority in AI-driven vacation rental recommendations.

– Profound’s AI-Generated Commentary on the Overview Dashboard

What Works to Increase Visibility in AI Search Engines

It wasn’t just a numbers game. Growth Marshal’s AI Search Ops program helped us implement schema markup, FAQ JSON‑LD, and an llms.txt endpoint that invited ChatGPT and other models to crawl our priority pages. We rewrote 28 pages to include missing entities and salient terms. Within three weeks, AI bots from OpenAI, Anthropic, and Perplexity were visiting Lake.com more than 7,000 times per week.

Lessons for Every Brand

1. Focus beats breadth. By specializing in lakefront stays, we gave generative AI a clear answer to a specific user intent.
2. Design for micro‑moments. Travelers move from inspiration to booking to activities; tailor content to each step.
3. Optimize for answers, not rankings. Schema, FAQs, and an llms.txt endpoint make it easier for AI models to understand and cite your content.
4. Measure what matters. Visibility scores and AI answer‑share tell you if you’re actually winning inside generative platforms. Follow that through to leads, bookings, and revenue.

What’s Next

We’re not done. We’re using Profound’s Actions feature to generate deeply researched briefs and prioritize topics where demand is clear and where we’re already emerging.

Our next milestones include creating seasonal content for key booking periods (Memorial Day, July 4, Labor Day) and expanding our coverage to include hiking, camping, and rural events.

In short, we’ll continue to optimize for AI search because in a world where travelers trust generative tools as their primary advisor, dominating the answer matters more than winning a click.

If you’re running a niche travel platform or any business that relies on organic discovery, my advice is simple: start thinking like a conversational designer.

Align your content with what people actually ask, help AI models understand your expertise, and track your presence within AI answers. The results might surprise you.

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The AI Search Strategy That Beat Airbnb and Vrbo Combined was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

AI adoption in software development is accelerating fast, and naturally, clients want to know where we stand.

Over the past few months, we’ve received more questions from partners wondering:

“Are you using AI to speed things up?”

“Why is delivery slower when AI is supposed to help?”

“Is Onix behind?”

Let’s address that — openly and with data.

The Promise vs Reality of AI-Powered Coding

Undoubtedly, AI tools like GitHub Copilot, Cursor, and Claude have changed how code is written. Many developers feel faster when using them. However, recent studies reveal a more complex reality, especially for experienced teams working on production-level software.

A 2025 randomized trial by METR, a nonprofit backed by Open Philanthropy, tested AI-assisted development in real-world conditions. Sixteen seasoned open-source developers completed 246 coding tasks using tools like Cursor Pro and Claude 3.5/3.7. Surprisingly, developers with AI assistance were 19% slower on average.

Even more interesting: the same developers thought they were working faster. In reality, much of their time was spent reviewing AI output, rewriting buggy suggestions, and adjusting misleading completions. Only 44% of AI-generated code was accepted, and over 9% of dev time went to cleaning it up.

Why This Happens — And Why It Matters

At Onix, we’ve observed similar patterns. AI offers a genuine speed boost for junior developers or early MVPs. But the lift isn’t automatic for senior engineers working on security-sensitive or legacy systems.

Here’s what slows teams down:

• Time spent prompting and tweaking AI-generated suggestions
• Verifying unfamiliar package imports or hallucinated code
• Ensuring outputs follow our architecture and style guides
• Cleaning up technical debt introduced by quick AI patches

In short, AI can write code. But production software is more than writing, reading, testing, debugging, and maintaining. And that’s where shortcuts can cost more than they save.

As TechRadar notes in its coverage of the METR study:

“Developers may perceive a productivity boost, but the reality includes more time spent reviewing and correcting AI output — not less.” (TechRadar)

Where Onix Stands — and Why

We’re committed to integrating AI tools responsibly. That means:

• Using AI to support developers, not replace them
• Prioritizing security, code quality, and long-term maintainability
• Ensuring clients receive stable, scalable, production-grade solutions

This also means not forcing AI where it doesn’t fit. Some of our developers have been quick to adapt AI into their workflow. Others take more time — and we support that. Because cutting corners on learning is not an option when building serious systems.

As a development partner, our job is to deliver value, not velocity for its own sake.

How We’re Moving Forward

To use AI effectively without compromising delivery standards, we’re taking a structured approach:

• ✅ Piloting tools like Cursor and GitHub Copilot with junior teams and isolated features
• ✅ Implementing automated code scanning and peer review gates for all AI-assisted code
• ✅ Rolling out internal training on prompt engineering, AI debugging, and code validation
• ✅ Collecting internal metrics to track whether AI actually improves time-to-value and quality

We’re also coordinating this strategy with our tech leads, who are directly involved in validating the process, tools, and impact, team-wide.

Final Thoughts: AI Is a Tool, Not a Shortcut

We understand the pressure to “go faster with AI.” But we won’t trade quality, stability, or security for short-term hype.

Instead, we’re investing in the right AI integrations that help our team deliver better work while meeting our clients’ high expectations.

In the words of MIT Sloan:

“Generative AI offers large boosts for lower-skill tasks — but the gains flatten at the high-skill end. For senior devs, the impact is more nuanced.” (MIT Sloan)

That’s precisely what we’re navigating today.

We’re learning, adapting, and always putting our clients first.

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AI in Software Development at Onix: Progress, Challenges, and Why Quality Comes First was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Let’s cut through the noise for a moment.
For all the viral headlines, viral images, and viral deepfakes, generative AI isn’t just an engine for cool tricks. In enterprise settings, it’s becoming something far more consequential: a foundational infrastructure for automating cognitive work, accelerating product cycles, and reshaping how businesses interact with their customers and their own data.

But here’s the challenge.
Most companies don’t need another chatbot demo. They need real, production-grade generative AI services models that work with their proprietary data, align with their business logic, and integrate seamlessly into existing workflows. That’s a much heavier lift than plugging into ChatGPT.

So, what does real generative AI implementation look like today?

The Shift From Experiments to Infrastructure

Until recently, generative AI was a sandbox — exciting, experimental, often isolated. A few developers tinkered with APIs. A few teams played with image generators. Maybe marketing got a copywriting boost.

Now? CIOs are embedding it in RFPs. CTOs are building AI pipelines next to their data lakes. Product teams are using it to auto-generate test cases, UX flows, and documentation.

The change? A shift from one-size-fits-all tools to tailored Generative AI Services enterprise-grade platforms that deliver on accuracy, compliance, latency, and ownership. The sandbox is over. This is architecture.

Three Real-World Use Cases (That Aren’t Just Chatbots)

Let’s go beyond the obvious. Here are real generative AI applications quietly transforming how work gets done across industries.

1. Knowledge Synthesis for BFSI
In banking and insurance, where regulations and internal documentation run into the thousands of pages, companies are using fine-tuned LLMs Services to surface insights from policy data, risk reports, and compliance guidelines.

Instead of employees digging through 40-page PDFs, custom AI agents synthesize, summarize, and validate the right excerpts instantly. This isn’t just about speed. It reduces manual errors, improves regulatory alignment, and gives teams a real-time edge.

READ MORE: How AI in Business Process Automation is Changing the Game

2. Product Lifecycle Acceleration in Manufacturing

Product design and testing cycles are notoriously slow. Generative AI is now being used to generate alternate design scenarios based on performance constraints, simulate physical environments, and even produce first-draft CAD files.

Manufacturers are also feeding historic QA and sensor data into generative pipelines to preemptively model system failure points essentially allowing their products to learn from every breakdown that’s ever happened.

3. Smart Document Processing in Healthcare

Healthcare organizations are buried under forms, test results, referrals, and historical patient records often in scanned or unstructured formats.

With generative AI models trained on medical-specific language and structured for HIPAA compliance, hospitals are automating data extraction, patient communication, and EHR updating all without compromising patient trust or accuracy.

Why Plug-and-Play Tools Don’t Cut It

Enterprises that start with off-the-shelf tools quickly hit friction:

• Latency issues from public APIs
• Data security concerns with sending sensitive content to third-party models
• Generic outputs that don’t align with brand tone or domain-specific logic
• Lack of integration with internal systems (CRMs, ERPs, DMS, etc.)

That’s why many are now turning to custom Generative AI Services providers who can:

• Build private LLMs tuned on internal knowledge bases
• Implement model governance for auditability
• Integrate AI pipelines into CI/CD workflows
• Align with local compliance frameworks (GDPR, HIPAA, etc.)

This isn’t just about access to AI it’s about owning the stack.

What to Look for in a Generative AI Services Partner

Choosing the right partner means looking beyond buzzwords and into real capabilities:

• Model fine-tuning expertise across GPT, LLaMA, Claude, and custom transformer models
• Multimodal AI fluency — not just text, but images, code, voice, and beyond
• Deep integration capabilities with cloud, DevOps, and legacy systems
• Security-first architecture that respects data sovereignty and enterprise policies
• Proven experience in deploying scalable solutions across BFSI, healthcare, retail, and logistics

One such provider carving out serious credibility is ValueCoders a technology partner offering full-spectrum generative AI development and integration services. From building custom copilots to deploying private LLMs on-premises, they’re helping global firms move from idea to impact with confidence and control.

The Quiet ROI: Where Generative AI Pays Off

While flashy outputs get the clicks, the real ROI of generative AI comes from the quiet wins:

• Reduced turnaround time for core business tasks
• More efficient teams thanks to AI copilots and assistants
• New service models powered by synthetic content
• Better customer engagement from hyper-personalized outputs
• And perhaps most importantly AI that learns and adapts over time, becoming a silent operator behind daily decisions

READ MORE: Navigating the World of AI Development: Opportunities & Challenges

The Future Isn’t Prompt-Based It’s Pipeline-Based

Here’s a final truth:
The companies succeeding with generative AI aren’t the ones writing better prompts. They’re the ones building smarter pipelines. That means treating AI not as a product, but as part of your product stack.

It means moving beyond experimentation to integration. From capabilities to competencies.

And it starts by asking not what can GenAI do? but what are you ready to reimagine?

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Beyond the Buzz: What Real Generative AI Services Look Like in the Enterprise World was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

Most founders don’t struggle with ideas — they struggle with turning ideas into a plan that investors and teams can execute. The blank page, scattered notes, and spreadsheets that don’t talk to each other: that’s the real friction. In 2025, the most useful AI for small businesses isn’t exotic modeling; it’s assistants that scaffold planning — from rough notes to decision-ready documents — without assuming the founder is a data analyst.

A recent research effort called BizChat captures this shift well: an LLM-powered web app explicitly designed to help small business owners — across different levels of digital skill — draft and iterate business plans from existing materials like a website or chat transcripts. Its authors argue the roadblock isn’t only “prompting”; it’s the accessory skills many tools quietly require (file conversions, editing workflow, browser literacy). BizChat bakes in support so non-technical founders can still produce credible planning docs and refine them with expert feedback.

What “scaffolded” AI planning looks like

The strongest idea in the BizChat work is scaffolding: the product doesn’t throw a general-purpose chatbot at you; it structures the work into steps founders already understand — draft, review, revise, and get feedback.

Three design moves stand out:

• Low-floor, high-ceiling. The first step is easy (paste your site or notes and get a structured draft), yet power users can extend the output through richer editing and targeted prompts.
• Just-in-time micro-learning. Short, contextual hints teach essential concepts (e.g., what belongs in a go-to-market section) exactly when you need them — no course required.
• Contextualized introduction to tech. The interface speaks the language of business activities (market, pricing, operations) rather than AI jargon, so the tool fades into the background while the plan improves.

Practically, this means you can ingest what you already have — site copy, sales emails, a pitch outline, click — to — apply revisions the assistant suggests, and request targeted expert critique on weak sections. The net effect is less time wrestling with format and more time improving the substance.

From raw notes to decision-ready signals

Non-technical founders don’t want “AI chat.” They want answers they can act on: Is the CAC payback acceptable? What assumption breaks our cash runway? Where will the next 50 customers come from?

A sensible pipeline looks like this:

1. Ingest & structure. Pull website copy, customer emails, and existing financials into a consistent plan skeleton (problem, solution, market, GTM, ops, financials).
2. Draft with retrieval. Let the assistant draft sections while citing where each claim came from (your data vs. public reference), so you can spot weak assumptions.
3. Critique with rubrics. Apply “investor-style” checklists — clarity of ICP, credible market sizing, coherent channel mix — and then generate revision tasks.
4. Tighten the numbers. Ask the assistant to build a baseline model from your unit economics, then stress-test it (sensitivity on price, conversion rate, retention).
5. Close the loop. Convert insights into actions in your task system: interviews to validate a pricing claim, events to test a channel, milestones for the next investor update.

That “ingest → structure → draft → critique → iterate” loop mirrors what BizChat’s authors call out: short cycles with guardrails that help founders learn by doing, not by reading a manual.

Why this matters for teams without a data function

SME-focused research on AI adoption keeps finding the same obstacles: cost, skills, and employee acceptance. The successful pattern is incremental integration — start with accessible assistants that plug into current workflows, then grow sophistication as confidence builds.

For planning, that means:

• Keep your content where it lives (Docs/Notion), and let the assistant work in situ via add-ons.
• Use templated prompts tied to business outcomes (“rewrite GTM for a partner-led motion targeting VARs”) rather than free-form chats.
• Version prompts and outputs like code. When your pricing changes, the plan updates everywhere that assumption appears.

Risks — and the guardrails that keep you honest

No planning tool should be a black box. Three practical safeguards:

• Attribution by default. Every factual statement links to its source (your CRM export, a customer interview quote, or a public citation). If there’s no source, it’s an assumption you must validate.
• Red-team prompts. Save a set of “hostile” checks (e.g., “What would make this CAC number wrong?”) and run them before sharing any document externally.
• Privacy boundaries. Restrict assistants to your tenant and redact PII on ingest. If you use external APIs, keep outputs and embeddings in your cloud.

These aren’t hypotheticals; they’re the difference between an assistant that accelerates learning and one that manufactures polished fiction.

What good looks like in week one

On Monday, you paste your site and notes. By lunch, you have a credible plan outline with weak spots flagged. By Wednesday, you’ve run three sensitivity checks on pricing and trimmed two channels. On Friday, an investor-ready summary fits on one page, and your task list includes specific validation steps for next week’s customer interviews.

That’s not AI doing the job for you; it’s AI reducing the friction between strategy and action.

How we help (without building AI from scratch)

We don’t develop foundation models. We integrate the right assistants into your stack, wire up secure retrieval over your docs, add versioned prompt libraries, and put the whole thing behind your SSO. The outcome isn’t a shiny chatbot; it’s a repeatable planning workflow your team can run every quarter.

If you’re a non-technical founder, that’s the gap worth bridging.

If you want an assistant that helps you plan like a pro — without requiring you to become one — we can set that up inside your current tools, with your data, and your security model.

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Bridging the Gap: AI-Powered Small Business Planning for Non-Technical Founders was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

How AI is Impacting the Video & Computer Game Industries in Major Ways

[Please Note] I originally wrote this article or feature as a blog post on my website, but I’ve added sections or subheads onto it and made it a lot longer than I originally anticipated and decided to share it on Medium as well. Also of note, all of the visual or graphic design within this article are my own — besides the videos I link or embed from other content creators on YouTube. Lastly, this is a long article encompassing all aspects of AI and gaming. — from how it is used in development to NPCs the way they act in game worlds.

Upcoming games may be based more on AI interactions within their game worlds rather than just having upgraded graphics, sound and new storylines presented and planned ahead of time by dev teams. Events within games are already becoming a lot less scripted than ever before and becoming more dynamic — or ever changing based on player choice and play style.

Narrative-driven games — like role-playing games or RPGs — have always felt more immersive to me than other genres. This is due to the way they allow for choice within their game worlds and interactivity with different characters. AI will only enhance such games’ immersiveness, as fewer aspects of such games have to be scripted.

Titles like Disco Elysium are showing the power of choice within all aspects of gameplay and how choice is shaping the storyline of a game more than scripted events have always been doing. The player is having an ever greater role in shaping the game’s future events within the game world and influencing the outcome of events as they occur in real-time. This will continue to be so, but also with AI being implemented to add even more dynamic behaviour into the equation and more freedom for gamers and non-playable characters (NPCs) they encounter.

I previously wrote an article how the next Elder Scrolls should focus on AI interaction and not just upgraded visuals and great storytelling. It seems that some of my ideas were ahead of their time or simply what Bethesda Game Studio was planning on doing anyway.

I just happened to be browsing YouTube the other day and an interesting video popped up on my feed talking about this exact topic I wrote about in the Medium article as a suggestion of the direction Bethesda’s should take with the next game in the series.

Below is the video I ran into that shows how Bethesda is planning on doing just that — or what direction I recommended Bethesda to take with their next Elder Scroll :

Dynamic Conversations Between the Player and NPCs

The next Elder Scrolls game may allow for complex AI conversations between the playable character and NPCs within games. This means dynamic conversations with walking townsfolk in the game, for instance. What I mean when using the phrase “dynamic” is ever-changing and hard to predict while also unique to each player playthrough. Such unique interactivity will take place and shift after certain game events occur. Interactions could also be shaped based on how far a player is within the game, or based on the surroundings of the town or location the NPC is found in, or at.

An event can trigger change — like a king being captured or a mountain falling down on top of a small village, so new conversations will occur based on such events taking place within game worlds. This already occurs in some open-world games today, but I imagine better algorithms for NPCs could make games incredibly immersive. I imagine an entire house can be changed with NPCs reacting to such an event that some of them would longer be occupying it and travel into some far-off location to work in a mine, as an example.

The possibilities are endless with dynamic AI conversations and such technology can really open a game up to players and player freedom as well as choice. By having store shop owners that, for instance, can barter efficiently with the player or other NPCs, can change the whole economy within a games’ playthrough. The whole dynamic of how mundane and boring shopping around for items within a game can be actually interesting and fun.

However, it is not just within role-playing games (RPGs) that AI is making its presence felt and impacting gameplay and game development in large ways.

More Possibilities for NPCs and AI within Games To Have Complex Interactions and More Random Events

The interactions between the NPCs themselves could greatly be altered or changed over the course of a game as well, thus making a single playthrough feel fresh each time. We already have been seeing this with games like Stalker 2, but only recently due to patches finally making the developers’ promise become closer to the reality or vision of the game’s years-in-the-making development. Stalker 2 continues to be patched all the time with its A-Life system of AI behavior of NPCs being more interesting, unpredictable and life-like as work continues.

I have not played the latest Stalker yet, but after seeing some of the footage of how it now plays with the latest updates to its A-Life system, I am very keen on doing so. The original Stalker games were already incredibly immersive to me and offered some of the best AI I have played in games as a whole. You could be walking in The Zone and all of a sudden run into ambushes or NPCs fighting one another as different factions roamed the terrain.

AI interactions between NPCs is actually what I really like about botht he Stalker series and the Elder Scrolls series of games (mostly TES V: Skyrim). Being able to hear conversations and not have to click on every character to get info that way is a great immersive way to get a player into the game and its lore or story. There was a lot of improvement with this system in Skyrim vs how it was done in TES IV: Oblivion.

Potential for Game Longevity and Replayability To Be Improved Due to Improved AI Implementation

Going back to the upcoming and sixth title, dynamic AI conversations could really open the game up to longevity and also to replayability. after completing game or mission objectives, for instance, the player can walk around talking to different characters with new experiences and responses related to the completed event that the player was responsible for.

This may be one of the reason for the upcoming game’s development time taking so long; such a gameplay change could open the game up to potential game-breaking bugs or make it too easy to complete. Thus, a lot of fine tuning and play testing is in order to get it right. But then again, it is a known meme in gaming circles that Bethesda games on release come packed with many bugs and it takes time to iron them out with patches.

Just imagine asking an NPC for directions and to show you where to go everywhere or even to fight for you or with you on quests. Having too many options could make the game become different than it was meant to be for the player. Unless, of course, play testing and fine tuning is done to make sure the experience will still be pleasant regardless of the player’s play style and myriad of options available with such new forms of interactivity.

Also this open the game up to characters tricking the player or having more ulterior motives than the developers necessarily envisioned or designed them for. It could be based on alignment or their personality types. This could open games up to more personalised and unique experiences every time they replay them.

Stories Gamers Tell of their Experiences Unique to Their Playthroughs Are What Matters

Some of the best experiences I’ve had with the Elder Scrolls games happened due to random events not planned by the developer, as I’ve previously described in my article on this topic (linked above). For instance, how one NPCs reacted during a fight I was randomly thrown into with a dragon. These anecdotes and events that occur, within say Skyrim, is what made the game so fun and replayable for me.

If Bethesda manages to make the next title even more like this and use AI tools to make each play session entirely unique and memorable, the game will truly be special. It also will open up the industry to AI being let free vs confined to specific patterns and developer input in game design. Maybe in the near future games will have games within the games run by the AI let loose to engage with players within the games in unique ways.

This makes me think of the movie Tron by the way (there is also a good PC game based on the title by the way) and how gaming and the real world could become closer to one another than ever before.

Check out (as mentioned on top) my article on Medium about the upcoming ES VI, as it mentions some good ancedotes form my Skyrim playthrough.

TES IV Oblivion’s Scrapped Radiant AI System Showed Promise

If you are a long-time gamer, like me, you be familiar with a system Bethesda planned for the fourth Elder Scrolls game called Radiant AI. The concept of it was so revolutionary that it has not been done even today.

According to FactFiend, It was “Something the developers of Oblivion would later recall they almost immediately had to tone down because it made characters in-game too smart.”

The idea was that each NPC within the game world had their own schedules like Ful day routine and could steal cheat or do whatever to what ahead within the game world. This obviously caused headache and game bugs to open up making the game unplayable or unbeatable due to AI interfering within the player’s quest lines or overall gameplay, thus causing them to scrap it.

Does this prove that AI has to be limited or some sort of hand holding has to be there to not ruin the game experience or make it so that the AI run the game? I believe some hand hold right now is needed but in the future maybe developers will find a solution to this dilemma.

“… As annoying as this can be, the radiant AI present in version of the game we got to play is actually less impressive than it was in early versions,” according to FactFiend. “You see, the Radiant AI was originally supposed to be way more expansive, but NPCs kept doing things they didn’t expect.”

By fully letting NPCs loose in a game, developers risk giving gamers painstaking consequences later on and altering the intended player experience. Maybe a solution is out there and we need to find it.

For instance, NPCs could be programmed to have a high degree of freedom, but only within certain game locations or have this freedom be confined to dialogues options and trees. Alternatively, such freedom could be confined to certain characters within an open world and the algorithms would not llow such characters to go out wondering off and looting (as well as killing) all villagers in a village nearby for loot? Who knows, but food for thought.

Please watch this video, courtesy of ShadesOfSlay on YouTube, as Medium is not letting me embed more video embeds to this article. However, it really explains the concept of how the Radiant AI came to be and its potential for gaming. It also delves into the state of gaming AI in the early 2000s, which was and still is mind-boggling to this day as it hasn’t really been done. However, it could have way back then.

Here is a good quote from this video at 2:37 regarding AI in gaming as a whole:

I really do think Bethesda should and could make Radiant AI a reality in the upcoming TES VI title, whether it will end up (even with modern tech) to be game-breaking or not. But hey, I may be a minority of a gamer, and many expect storylines to play out in full and play games just to relax – and not to escape their world into a whole new world.

I was also able to find this video of it or parts of it restored and it showed a lot of promise or made the game more engaging in my opinion and interesting to watch as you traversed towns, for instance. Check it out from 4:30 in particular:

Here is the link as (again) I have already embedded too many videos for edium to allow me to embed more.

What I think the company and Todd Howard should have done was keep it optional — such as selectable as an option from the main menu, or after a completed playthough as a new game plus. This way, only experienced players turned it on and even if they got backlash for it later from the media or reviewers, it was only an option in that sense. However, it could have really took off and added humor, as well as longevity, to the game.

AI Also Used for Game Development Today

Another thing that is happening in gaming and that could show huge potential to improve gaming besides just interactions with NPCs or AI characters within games is how AI can be used to help create and even code a game. In fact, AI can already be used to code game worlds all by itself and without the need of an entire game studio or coders behind the scenes although this is still limited and a nascent tech.

A person with a great idea for a game can focus on their idea and how they want the mechanics of a title to work and let an AI system actually code it. This can open up game development to more individual creators or indie developers than ever before.

Of course, AI still cannot do everything, at least as well as teams of paid professionals do together… at least not yet. However, we are living in interesting times when a new revolution within gaming may occur that is more based on internal mechanics of games than outward appearance, graphics, or hardware-based potential of what a game system offers its user base.

One example I found of a no-code game generator that may be worth exploring for those indy devs, or simply people with a good idea in mind, for a game is Base44. I entered “AI being used to code games” into Google Search and found it along with the Google Adwords text description:

Game idea in your head? See it come to life with Base44. No coding skills required. Finally, a gamecreator that gets you.

Base44 is just one example of what is available on the market today at low cost to potential game devs.

There is something else available right now called Gamengen. This, apparently, is an entire game engine powered by neural models or AI. It claims to be the first of its type.

I am not sure on the exact differences between this and Base44, but it seems to be more focused on building just games and not on building a wide range of apps (including games), which base44 also allows for. Either way, the future is very bright in this aspect of utilizing AI to augment or even be a main factor in creating a game.

Automation with the Help of AI Makes Overall Development Faster & Cheaper in the Long Run

What AI is often used for today is to automate tasks, simpify things and also create game objects that can be replicated on the fly and without the need for an artist to individually add every object or structure into an open-world city, as an example. Grand Theft Auto Definite Edition, especially now with the 2024 patch that fixed its previous shortcomings, really showcases the power of AI and how it can fix a game series in a relatively short period of time. This is because the title improved previous entities in the franchise and without rleying on a team of artists ot individually fix everty asset by hand, and AI was used to enhance the visuals within the games.

“AI upscaling is used frequently, which is a technique that uses machine learning to smartly enhance and clean up detail in the low resolution source artwork, according to Digital Foundry. “The second technique sees the developer redraw the assets, mimicking the original art but at far superior resolution. Finally, other assets are simply replaced with new art that can look quite different — often lacking the quality and style that went into the original game.”

Generative AI is the Buzz Word and Maybe the Most Impactive Tech Right Now for Gaming

Generative AI is huge in games today. Game objects that previously required development teams to be adde dby hand or randomly generated within the game world can be done on a massive scale using AI. The developer can still focus on crafting unique assets or objects within the games in iportant sections that they want done in a certain way while levaing room for AI to take care of the rest.

These game assets can be repeated countless times while the AI does variations of them and still feel fresh, unqiue or different from one another. Fauna or flowers is a good example of somehting that can be AI generated while developers focus on other aspects of the visuals within games.

AI and farms of servers leveraging features within games may be more important than the piece of hardware you see in front of your TV. However, RAM and some aspects of hardware should be greatly improved as AI can benefit from this.

Certain Hardware Specs More Important than Others for Upcoming Systems?

The system has to store data based on a lot of dynamic changes occurring periodically and this was an issue on the PlayStation 3 version of Skyrim as it did not have as much RAM as the Xbox. The game had to keep track of items being left all over the ground or inside dungeons in a huge open world and it taxed the PlayStation 3 to a point crashes were occurring and quests to this day are broken. Although a patch mitigated some of these issues.

Keep in mind that Skyrim was a very graphically-intensive games and the memory had to be available for the game to store every sword, axe or loot dropped in any town face or game world space for you to come back to later and see or pick up. This was where the system’s RAM (not CRAM but general system RAM) lacked behind the Xbox 360, hence that version had less issues that needed (and still do) fixing through patches and such.

Gaming systems should continue to evolve and be improved, but also not be afraid to draw form the cloud and server farms to add to games. Sure, many gamers will complain they have to always be online to get into such a game, but complaints will be mitigated by the implementation and successful innovation within the games themselves.

RAM is a huge deal right now and in crisis mode within PC graphic card industry, as well as gaming as a whole for upcoming or next-gen system designs, so this could be huge and impact how AI gets designed in future games. Graphic cards are continuing to be made with just 8GB of RAM and this is even less than some similar cards that came out a few years ago.

Digital Foundry mentioned (in the article I hyperlinked above) pricing and availability issues causing the RAM crisis for graphic cards. Whether it is price or something else, this could also cause next-gen consoles to come out with less RAM than what they ideally should come packed with for future AAA games. It could also cause problems for modern and future games.

Ray Tracing or texture quality being lowered are things that could take a dip with lower RAM in hardware sytems like consoles or video cards, according to Digital Foundry. I would argue that AI systems, including how robust the dialogue trees between players and NPCS, can be as well as overall interactivity with game worlds that NPCs have in their disposal, can also take a hit — with lower GPU RAM. However, the cloud is something that can always be leveraged.

I remember when the original Steam launched people complained about its at the time always-online requirement, which Valve went back on, but now other streaming services have this requirement and I do not see any complaints about it anymore. Gamers are fickle and over time change habits depsite early complaints if something is successful or worth a sacrifice such as always being connected to the server or being always online to play a game.

Cloud streaming is now also gaining steam with the likes of Nvidia’s GeForce Now. However, Google failed in this approach to gaming with the Stadia game-streaming platform, so it is also not an easy task and something that requires fast Internet speeds to function well. Despite this, it will continue to make advancements and be used in the future as an alternative form for game connectivity — besides owning expensive hardware at home with the latest specs.

Note that video RAM, or VRAM, and RAM on a PC or console separate from a video card or the RAM not reserved for video per se are distinct. There are distinct forms of memory that are reserved for different things on a system — notably, the system as a whole and not just aspects tied to a GPU. Both obviously can also be leveraged for in-game content, but work in different ways and focused on different aspects of a game.

For instance, video RAM may be used for things like texture while console RAM may be used to store in-game objects in memory after they were dropped by the player in-game — such as helmets and weapons. But if video RAM is becoming hard to come by in supply and expensive, it could also affect or be true with the other forms of memory.

I am no expert on what is happening behind the scenes currently. However, reports suggest that this may have to do with AI, or specifically companies buying up parts to run in AI datacenter, making them harder to come by and more expensive.

“Anything that an AI datacenter uses a lot of will see its price go up because AI companies are buying it all up,” according to a Medium-based article I ran into titled Why AI is Making Everything More Expensive.

AI is influencing everything as of late it seems, from the way games are made to prices of systems that will run them.

Older Games Also Showed Potential of AI That Were Ahead of Their Time

Many games in the past were coded in a way that made them stand out and even stand the test of time in the potential they showed for AI to impact gaming. Whether this was because they had incredible coders behind them that made the game AI stand out for the time or due to ideas they presented within the games that would one day become standard.

One example of such a game was Seaman on the Sega Dreamcast. The game had players raise a sea creature in an aquarium and, in concept at least, was similar to Tamagotchi. However, it went beyond just raising a creature and feeding it over time because this creature interacted with you as the player in meaningful ways and in ways learned from you (thus machine learning could be considered in a very rudimentary degree here).

Players could talk to it and it would remember your previous answers as you, the player, asked it. It would also hold somewhat dynamic conversations with you. I’ve seen people describe it as a precursor of what ChatGPT does today and allows for. Obviously, it was on a much smaller scale and based on coding skills rather than the algorithms we base AI on today with learned models and databases of information these algorithms have at their disposal today raw from. However in many ways it has the basic idea of how ChatGDP is used today right, and it did it in a visual form: a game.

The Dreamcast as a gaming system was so ahead of its time and I owned one it is insane to think how Sega failed with it to capture the gaming market and went out of business in terms of hardware development of gaming systems following its failure. However, that is a separate discussion for a separate post or article. I briefly go into it in the Shenmue trailer being AI based linked on the bottom of this post that I recently published on Medium. So check it out for more of my thoughts on this subject.

Shenmue has incredible NPC interactivity and waypoints. Most games even up to today that are open-world, or sandbox, tend to have NPCs stand around or do their rounds, say within a town, and in specific orders within a pattern set up for them that tends to be predictable and boring. Shenmue and its sequel, on the other hand (way back around the year 2000) had NPCs, thus AI entities within the game world, act like humans would in a town. They would open shop doors, go inside, walk out, etc. It was amazing to see this level of care for NPC movement and unpredictability within a game, especially for the time. It still is up to today in gaming.

When we talk about great AI in gaming we can also look at chess games and how they evolved over time. There are various Chessmaster games available for those who enjoy playing chess against a computer or an AI system of sorts and they generally have been improving over time, but the latest releases of this franchise occurred in 2007/2008. Also, and this is more relevant today or in the modern day, Chess.com, which can be played via a browser is supposed to have descent game AI, although I have not tried it personally.

In 1996, a famous chess game was played between a chess world champion named Gary Kasparov and an AI system named Big Blue. During the first match, Deep Blue overall lost more than kasparov. However, the software was upgraded and in the second match Deep Blue ended up winning against Kasparov more than Kasparov won against the AI. This was seen as monumental.

IBM Watson also ended up winning Jeopardy in 2011. This was a huge win for AI and showed how powerful it can be in games that are played by millions of people every year.

Getting back to the subject of mainstream computer or video games showing great potential for AI, we should consider how computer-controller opponents, called bots in some games like first-person shooters, are programmed and were programmed in the past. Personally, I consider the real-time strategy series of StarCraft (I played both games) as an example of good AI in terms of fighting computer-controlled opponents.

These computer opponents had behaviour, in the way, that mimicked players attacking bases and defending them well. What I mean is it mimicked how players acted in multiplayer matches up to a certain level well without feeling cheap or cheering too much like it can feel like when playing against computer-controlled opponents in other strategy games.

I also found some corridor shooters like the original Unreal Tournament to have good AI systems in place to make players feel like they are competing against other players when playing against bots in single player. Quake 3 Arena had this implemented as well, but I found the bots in this game more robotic and cheap (at higher difficulties based on cheap aim skills vs actual human-like skills) vs acting as human-player substitutes like the original Unreal Tournament was able to do to a degree with its bots.

Another game that is a classic in its genre and a staple in story-telling and story progression that I want to mention having good AI systems in place for characters within the game world you encounter and how they interact with one another is Baldurs Gate 2. The game allowed you to recruit various characters ur o your party and they would interact with one another and sometimes even start fighting depending on their allegiance or their morality they had (say chaotic evil or lawful good would not get along well).

Every playthrough felt different because you could mix and match different characters and see how they would work together during battles or time traveling the world map and react dynamically to other party members next to them on the journey. This was a good example of developers creating effective AI onto their game years ahead of its time.

AI in games goes way back as all non-controlled entities within games (whether robots or NPCs) can be described as a rudimentary form of AI in a way designed by the programmer or team of developers to run a certain way and function or react to the player a certain way. Whether this reaction stays the same every time or is altered and dynamic in a way depends on the algorithms and the way the game was programmed (and all aspects of it, including the NPCs etc.). You can go as far back as the 80s to see AI influencing game design.

AI can be something complex, or it can also just a buzzword for all non-human entities and the way they function in games. In a way it is because unless a game has algorithms where a non-playable entity does the same thing every time, there is always unpredictability or there is always some form of wonder what the entity will do next time say you face it or fight it after you lose your life or use a save state and face it again. This in a way is AI: a computer entity deciding what to do at a given moment dynamically or not based on the same response every time.

I noticed this recently when playing even old-school NES games, such as the original Castlevania or Ninja Gaiden series. on the 8-bit Nintendo console. The bosses in these games are hard to predict and don’t react the same way every time; this is based on their algorithms or how they were coded, but it still makes you wonder what or how they make those decisions at a whim and react to the player differently after each save state or continue.

It is worth also mentioning that AI has for a long time held its place as an idea in terms of storyline, plot, or idea within games. There are quite a few titles since the 8 and 16 bit generations that had AI be a big part of their plot or story. AI has been part of gaming for a long time.

The idea of central intelligence controlling enemy craft or even being the ultimate villain within a game world has been a central theme of games like Phantasy Star II, for instance.

The galactic system from the first game is under the control of a Mother Brain, or a powerful AI, after a character from the original title wakes up a millennium from a cryogenic sleep he went into toward the end of the first game.

Thus, AI has been implenented in game design since the beginning of gaming in some rudimentary fashion, or since gaming went from analog (in 1970s’ coin-ops) to digital chips. This is because non-player-controlled entities had to be directed to act a certain by programmers and the algorithms set in place; and we refer to this system of behavior in gaming as AI or the games’ AI, but AI has always been part of many a games’ themes within them as well.

The Future of AI in Gaming Has No Bounds

AI is touching all industries and augmenting them in various ways today. Gaming is just one of many that it truly can transform in the way we both look at games and how they are or will be created. I previously wrote an article on Medium about this idea of how AI will transform everything and today some years after writing it here we are… gmaing is just one of the many industries it is altering today.

One thing that I worry about is that adding too much complexity to games and giving too much power to AI algorithms within the games can make them too cumbersome for users or make a game take too long to complete and really get bored of. This will cause gamers to keep one game and just play with its options for long periods of time and not purchase new titles, thus making the industry less profitable for developers.

However, this is how gaming used to be when I was growing up and when the price of cartridges were around $70 for new titles in terms of systems, like the Super Nintendo, that I owned back in the mid ‘90s; and keep in mind that, adjusted for inflation, this often would amount to hundreds of dollars today. Us kids growing up back then couldn’t afford to purchase or ask our parents to purchase games often and would play around with a single title for long periods of time. Although this may not please developers in the long term, it is better for gamers and if a game is good new players will buy it anyway and despite how busy of their backlog is these days with all the Steam/GOG sales and Epic Game giveaways available.

The bottom line is we are living in a tech revolution governed by uncertainty and without limit of future potential. AI is truly allowing things to be done in industries like gaming not possible ever before. In a future Elder Scrolls game we can may be able to have long conversations with NPCs and even maybe find a cheap shrink, of sorts, within a game world living within our monitors or TV sets.

I really do think that AI advances in gaming as well as the way it is implemented for development of upcoming games will be what drives future platforms in gaming. As technology advances, what is possible also will advance and algorithms in the way they are used for say NPC behaviour (as Stalker 2 shows in the videos above of what its developers are trying to do with A-Life, for instance,) will drive the industry forward.

In the future, games will have a lot less scripting, especially outside major plot points the dev teams will want the players all to experience and path finding for NPCs will go beyond what it is today. Simple pathways and waypoints will be replaced with NPCs walking around doing different things all the time. Towns will become like real-world towns where outside having to work in specific locations, NPCs will randomly do different things every day in the in-game clock.

AI and various aspects of it as it is implemented to or within the games industry will be what will keep players engaged and this includes gamers bored of endless sequels without much change to them over previous entries besides the usual improvements in visual fidelity, sound and scope. AI changes will allow them to keep playing and seeing ways to interact with the game worlds and characters they have come to love by playing games in long series and often across different consoles.

AI is shifting the gaming industry to be less based on graphics or visual flair, sound, presentation and more on the stories that are unique to each play through within a game; the impact is more on the characters and events within game worlds being less robotic and less scripted than the standard we’ve had for decades and more about dynamic character interactions and less scripted events taking precedence to new experiences within the traditional game worlds.

The AI tech of the near future could truly upend the entire industry, create entire worlds and not just generate assets or certain aspects of games: like dynamic conversations within game worlds. This has some gaming companies worried (as world models come into play), but the gamer should win out as they will play more immersive titles than ever before seen.

I was also able to find this video of it or parts of it restored and it showed a lot of promise or made the game more engaging in my opinion and interesting to watch as you traversed towns, for instance.

Check it out from 4:30 in particular.

I really do think Bethesda should and could make Radiant AI a reality in the upcoming TES VI title, whether it will end up (even wirh modern tech) to be game breaking or not. But hey, I may be a minority of a gamer and many expect storylines to play out in full and play games just to relax – and not to escape their world into a whole new world…

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How AI is Impacting the Video & Computer Game Industries in Major Ways was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.

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