Chase Keating - AI Consultant

AI consulting is like having a trusted guide to help your business navigate the fast-evolving world of artificial intelligence. Think of it as a shortcut—instead of spending months (or years!) figuring out how AI applies to your industry, consultants work with you to pinpoint exactly where tools like machine learning, automation, or predictive analytics can solve real problems.

Here’s what it looks like in practice:

We start by listening. What’s keeping you up at night? Maybe it’s slow processes, outdated systems, or competitors pulling ahead. We’ll audit your workflows, data, and goals to find the right AI opportunities—not just trendy tech for the sake of it.

No one-size-fits-all solutions. If you’re drowning in customer service requests, we might build a chatbot that learns from your team’s best responses. If you’re a retailer, we could design a system to predict inventory needs or personalize shopping experiences.

We stick around. Implementing AI isn’t a “set it and forget it” deal. We’ll help you roll out tools, train your team, and tweak things as you grow (because if your AI isn’t adapting, it’s becoming obsolete).

Why does this matter?

Cut the busywork. Automate repetitive tasks so your team focuses on what humans do best—creative problem-solving.

Smarter decisions, faster. Turn raw data into insights you can actually use, like spotting trends or avoiding risks before they blow up.

Future-proofing. AI isn’t just for tech giants. We make it accessible and practical, whether you’re a hospital streamlining patient care or a factory optimizing supply chains.

In short, AI consulting isn’t about selling you robots—it’s about unlocking opportunities you might not see yet. Curious how it could work for you?

What Can Be Automated with AI in a Business?

AI automation isn’t just about robots taking over jobs—it’s about freeing your team from repetitive, time-sucking tasks so they can focus on what truly moves the needle. Here’s the reality: if a task is rules-based, data-heavy, or repetitive, AI can probably streamline it. Let’s break it down:

Common Areas Businesses Automate with AI

Customer Support

Chatbots: Handle FAQs, booking, or troubleshooting 24/7 (e.g., “Track my order” or “Reset my password”).

Email Triage: Automatically sort and prioritize customer emails, flagging urgent issues.

Data Entry & Processing

Invoice Processing: Extract data from invoices, receipts, or forms and auto-populate systems.

Spreadsheet Work: Clean, organize, or analyze bulk data (no more manual copy-pasting!).

Marketing & Sales

Personalized Campaigns: Use AI to segment audiences, recommend products, or send hyper-targeted emails.

Lead Scoring: Automatically rank leads based on likelihood to convert.

HR & Recruitment

Resume Screening: Scan resumes for key skills or experience, shortlisting top candidates.

Employee Onboarding: Automate paperwork, training schedules, and FAQs for new hires.

Operations & Logistics

Inventory Management: Predict stock needs, reorder supplies, or flag shortages.

Supply Chain Optimization: Adjust shipping routes or warehouse workflows in real-time.

IT & Security

Troubleshooting: Fix common tech issues (e.g., password resets) with AI-driven scripts.

Threat Detection: Monitor systems 24/7 for suspicious activity or breaches.

Content & Creativity

Social Media Posts: Generate draft captions, schedule posts, or analyze engagement.

Basic Content Creation: Draft blog outlines, product descriptions, or meeting notes.

But Here’s the Key

AI automation works best when it’s customized to your business’s unique needs. For example:

A restaurant might automate table reservations and food waste tracking.

A law firm could auto-scan legal documents for critical clauses.

A manufacturer might use AI to predict equipment failures before they happen.

The goal isn’t to replace humans—it’s to remove the friction so your team can innovate, build relationships, or tackle complex challenges.

How Long Does It Take to Implement AI?

AI implementation timelines vary based on your goals, but most projects see early results in 4–12 weeks. Think of it like building a house—you need a solid foundation before adding smart features.

Key Factors Influencing Timelines

Project Scope: A simple chatbot? 2–4 weeks. A custom supply chain prediction model? 3–6 months.

Data Readiness: Clean, organized data cuts setup time by 30–50%. Messy data? We’ll help clean it first.

Customization: Off-the-shelf tools work faster (1–3 weeks), but tailored solutions take longer (2–5 months).

Integration: Connecting AI to legacy systems or APIs may add 1–4 weeks.

Typical Implementation Phases

Discovery (1–2 weeks): Align on goals, audit data, and plan the roadmap.

Prototyping (2–4 weeks): Build and test a minimum viable product (MVP).

Refinement (2–6 weeks): Train models, fix edge cases, and validate accuracy.

Deployment (1–3 weeks): Integrate with your systems and train your team.

Example Projects

Quick Wins (2–6 weeks): Document processing automation, basic chatbots, or analytics dashboards.

Complex Solutions (3–6+ months): Predictive maintenance systems, hyper-personalized recommendation engines, or enterprise-wide AI governance.

Engagements are scoped per project — it depends on the systems involved, the data, and how much needs building versus integrating. Get in touch and I'll put a number against your specific scope.

I also take on a small number of companies as an embedded equity partner rather than on a fee basis. See the partnership page for how that works.

Success with AI isn’t just about deploying technology—it’s about solving real business problems. Here’s how we define and track impact:

Key Metrics We Track

Efficiency Gains: Reduced time spent on repetitive tasks (e.g., hours saved/week).

Cost Savings: Lower operational costs (e.g., reduced manual errors, faster processing).

Revenue Growth: Increased sales from personalized campaigns or improved customer retention.

Accuracy Improvements: Higher precision in tasks like demand forecasting or fraud detection.

User Adoption: How seamlessly your team integrates AI tools into daily workflows.

Our Process for Ensuring Results

Baseline Assessment: Measure current performance before AI implementation.

KPIs Aligned to Goals: Define clear targets (e.g., “Cut customer response time by 40%”).

Continuous Monitoring: Use dashboards to track progress in real time.

Iterative Refinement: Adjust models based on feedback and changing needs.

Real-World Examples

A retail client saw a 25% boost in sales after AI-driven product recommendations.

A logistics company reduced delivery delays by 35% using predictive route optimization.

No vague promises—just measurable outcomes.