A friend of mine, called Anna, runs a small marketing agency. She heard AI is “all the rage” and thought, “Great , I’ll hire an AI consultant and they’ll just plug something in and magic happens.” After six months and a decent spend, she had some dashboards, a half-built chatbot, and more confusion than clarity.
This is a common trap. The role of AI Consultants isn’t to sprinkle fairy dust; it’s to guide you through messy tradeoffs, align tech with business, and help bridge gaps you didn’t even know existed.
In fact, navigating that “messy middle” is exactly what Navigent does: they position themselves as an AI consultancy dedicated to real estate and property management, promising practical, ROI-driven AI tools without the hype or complexity.
That sets a useful frame: AI consulting isn’t about dazzling demos, it’s about making AI usable in your world.
What is an AI consultant really (and what do they do)?
Let’s demystify it. You’ll often see in tech and business sources that AI consultants support businesses in identifying, evaluating, and implementing AI use cases not just technically, but strategically and organizationally.
Here are typical hats an AI consultant wears:
| Role | What they actually do | Why it matters |
| Strategist / Vision layer | Work with leadership to ask: “Where could AI help us most? What’s feasible?” | Without strategy, you build toys, not tools |
| Use-case filter | You might think “AI for everything.” Consultant helps pick 2–3 high-impact, low-risk use cases | Minimizes wasted effort |
| Solution architect | Design how AI components (models, APIs, pipelines) fit into existing systems | Prevents “island AI” that doesn’t talk to your real workflows |
| Change manager / adoption guide | Train staff, address fears, fine-tune human + AI workflows | A tool is only useful if people use it |
| Governance / ethics guardrail | Help with privacy, bias, compliance, auditability | Avoids reputation or legal disasters |
In short: AI consultants are translators between tech and business, making sure your AI actually delivers value.
How consulting is shifting (and where AI consultants fit in)
Consulting as an industry is being reshaped by AI itself. Harvard Business Review recently ran an article entitled “AI Is Changing the Structure of Consulting Firms,” pointing out that many tasks once done by junior consultants that were research, data pulls, slide prep are now automated.
At top firms like McKinsey, BCG, and others, internal AI units now assist human consultants, helping them crunch data quicker, propose insights faster, or generate first drafts of reports. But the “human in the loop” (making sense of AI outputs, negotiating clients, understanding political dynamics) still remains crucial.
So a good AI consultant needs to be comfortable in this hybrid zone: not just deploying models, but asking which problems to solve, how to measure success, and when to override the AI’s suggestions.
What makes a great AI consultant (not just a service provider)
Here are some qualities or practices I believe separate the useful ones from the flashy ones:
- Domain empathy
If your consultant knows your industry (say, property management), they intuitively understand pain points, jargon, and where AI can make a difference. Navigent uses this to their advantage by focusing precisely on real estate clients. - Data realism & humility
Many businesses overestimate how clean or usefully structured their data is. A good consultant spends real time in the weeds (data exploration) and tells you what’s possible (and what’s not). - Rapid prototyping mindset
Design small proofs of concept first. Don’t build the perfect system from day one. See what works, iterate, learn. - Transparent “human + AI” roles
Be explicit about what the AI does vs what the human does. Use techniques like “explainable AI,” provide overrides, and document decisions. - Metrics & feedback loops
Set up KPIs up front (efficiency gains, accuracy improvements, cost savings). Monitor, evaluate, and course-correct. - Ethics, bias, compliance baked in
Especially in sensitive domains (finance, hiring, real estate), you can’t leave bias or privacy to chance. - Continuous learning & tool fluency
The AI landscape changes quickly. Your consultant should be experimenting with new tools, models, frameworks not just relying on last year’s stack.
A sketch of how an engagement might unfold
Let me walk you through a fictional but realistic engagement:
Client: A midsize property management company with 200 units, struggling with lead conversion, tenant queries, and document management.
Phase 1: Discovery & alignment
- Consultant interviews stakeholders: leasing agents, maintenance, legal, finance.
- Maps existing systems, data sources, and tech stack.
- Co-creates a shortlist of 3 possible AI use cases (chatbot for tenant queries, auto lease document review, predictive maintenance alerts).
Phase 2: Pilot / proof of concept (PoC)
- Build a simple chatbot that can answer top 10 tenant FAQs (with human fallback).
- Run it for 1 building or 50 tenants.
- Track metrics: query handling rate, escalation rate, user satisfaction.
Phase 3: Iteration / scaling
- Based on feedback, refine the bot, add document NLP models, integrate with property-management software.
- Expand to more buildings.
- Introduce predictive models for maintenance issues (e.g. detect water leak risk).
Phase 4: Governance & handover
- Train internal staff on monitoring, prompts, overrides.
- Set up dashboards, alerts, audit logs.
- Document workflows: when should a human intervene?
Phase 5: Ongoing optimization
- Quarterly reviews: what’s working, what’s failing, what to try next.
This kind of structure helps keep things grounded (no runaway AI dreams) and ensures value is realized before major spend.
Common pitfalls & red flags
Just like in Anna’s case at the start, here are some things to watch out for if you’re evaluating or working with AI consultants:
- Too many use cases, no focus
If your consultant throws 20 ideas at you, chances are none will be well executed. - Black box overkill
If you can’t understand how models make decisions (no logs, no transparency), it will bite you later. - Neglecting people & processes
A shiny AI tool fails if your team doesn’t adopt it, or if process mismatches exist. - No measure of success
If they can’t tell you how they’ll judge “success,” don’t trust it. - Vendor lock-ins hidden
If your consultant builds with a proprietary stack and you’re stuck, that’s a concern. - Underestimating change management
Resistance, fear, unclear roles without engagement, adoption lags.
Why the “niche AI consultant” model might win
From what I see (and what Navigent is doing), the more value an AI consultant can bring quickly, the more likely they’ll succeed. And doing that often means specializing, picking one or two verticals, deeply understanding them, and building repeatable templates or toolkits.
For instance, Navigent’s promise is not “we’ll build any AI for you,” but “we’ll make AI simple for real estate & property management, and do it in a way that saves time, cuts cost, boosts lead capture.”
When a consultant can say, “We’ve done this for 5 similar clients, here’s what we learned, and here’s how we’ll adapt for you,” it removes guesswork and increases trust.
