As professional services firms continue exploring the promise of Generative AI, the real question is no longer if to start—but where. In our recent article, AI with Integrity: How Small AI Bots Drive Big Wins in Professional Services, we laid out the case for starting with narrowly scoped, high-impact projects that deliver immediate value and create a foundation for future scaling.
Curious how these concepts play out in the real world? We spoke with DeShon Clark, a Microsoft AI engineer at Allventa, who has worked directly with clients deploying micro-bots in professional services. He breaks it down—one use case, pitfall, and lesson learned at a time. Below is an edited version of his conversation with Christopher Ward, managing director at K2 Integrity.
CW: What makes an AI micro-bot or agent different from a large-scale enterprise AI rollout?
DC: In a word: focus.
A micro-bot tackles one tightly scoped task, draws from a narrowly defined data set, and operates on a highly structured prompt. That focus lets us test, tune, and deploy in weeks—not quarters—while collecting clean feedback loops.
Enterprise rollouts are orchestras: multiple agents, many data domains, and several stakeholder groups. Coordination, governance, and subject-matter-expert (SME) alignment add complexity—and risk—so wins take longer to surface.
CW: Which professional services processes are most ripe for an AI micro-bot or agent, and why?
DC: Processes where “close is good enough” outperform “precise to the decimal.” We’ve seen repeatable success with:
- Email drafting and response suggestions
- Policy and procedure generation
- Hyper-targeted newsletters
- Conversational SMS responders
All of these allow creative latitude within a framework—so the bot can add value fast without a Ph.D.-level math check.
CW: How do you choose the first data source when the data is messy?
DC: Start small and trustworthy.
- Pick the cleanest subrepository available—say, proposals from the last two years.
- Run a light data-hygiene sprint: deduplicate client names, remove obsolete files.
- Validate with SMEs before it feeds the retrieval-augmented generation or RAG pipeline.
A 5 GB clean set beats a 500 GB swamp every time.
CW: What metrics prove a micro-bot or agent is worth it?
DC: We show return on investment, or ROI, through a time-to-value lens:
- Hours saved per month × fully loaded hourly cost
- Cycle-time reduction—for example, cutting proposal prep from 6 hours to 90 minutes
- Redeployed capacity: what higher-value tasks those freed hours now fund
When leadership sees both cost avoidance and new revenue moments, the business case clicks.
CW: Have you ever shelved an AI bot due to poor data? What were the lessons?
DC: Absolutely—early GPT 3.5 pilots stalled because data quality and model limits collided. The takeaways:
- Map the process end-to-end first; inject AI only where it truly fits.
- Classify segments by model type needed (standard, deep reasoning, web-augmented, etc.).
- Pilot in baby steps, validate with SMEs, and wrap every output in an observation-layer QA agent before scaling.
CW: What’s a simple way to stop an AI bot or AI agent from exposing sensitive data?
DC: Don’t give it what it can’t reveal.
Redact personally identifiable information (PII) or confidential fields before ingestion. If exposure is unavoidable:
- Add first-pass filters at the prompt/response layer.
- Use second- and third-level observer agents that scan and veto sensitive outputs.
You’ll trade some latency for safety—but safety trumps than speed.
CW: What makes an AI bot or agent swarm, and when do swarms make sense?
DC: A swarm emerges when agents autonomously collaborate—deciding in real time which peer to invoke next. Today (Q3 ’25) we reliably orchestrate multi-agent pipelines, but fully autonomous swarms are still maturing.
Prerequisites include:
- Five to six solid single-purpose agents delivering value
- Mature data governance—role-based access, audit trails, compliance guardrails
- Clear fallback paths if autonomy drifts
My forecast: within two to three years we’ll see swarm architectures driving nonprecision, highly contextual workflows. Precision-critical domains will follow as observability tools catch up.
Turning Gen AI Insights Into Action
Successful AI adoption isn’t about leaping to the finish line—it’s about building trust, structure, and capability, one small win at a time. From choosing the right first dataset to putting governance in place early, a thoughtful, incremental approach creates real traction—and lasting results.
Stay tuned as we continue to explore what’s next in Generative AI for professional services.
