In our earlier article, “Embarking on an AI Journey in Professional Services,” we outline foundational strategies for adopting artificial intelligence (AI), or more specifically Gen AI, in professional services settings. Building upon those insights, we now highlight the strategic importance of initiating AI projects that are modest in scope yet impactful in results.[1] These initiatives can deliver immediate and measurable value, establishing the foundation needed for future applications, such as agent swarms.
Why Starting Small with AI Works
Our initial article highlights the advantage of an AI implementation strategy that begins with manageable projects designed to solve specific, well-defined issues, ideally with known outcomes. These early implementations should seek to address common, repetitive tasks; automate straightforward processes; or reduce the manual workload. This approach quickly demonstrates the potential for measurable improvements and clearly illustrates the practical benefits of AI.[2]
Data Preparation and Quality: Ensuring a Solid Foundation
One key lesson from starting small is the importance of data readiness. Any great AI idea can crumble if the data feeding it is messy or unreliable. It’s best to start with the data available to the organization. However, it’s equally important to ensure the data is clean and well-understood enough to yield meaningful results. Selecting a manageable, high-quality internal data source for the first micro bot/agent sets the project up for success. In practice, this could involve:
- Limiting Scope to Reliable Data: If the firm’s historical documents are scattered and messy, it might be best to begin with a specific repository (e.g., proposals from the last two years) known to be consistent. Starting with this smaller, well-curated data set ensures the AI isn’t overwhelmed by noise.
- Light Data Cleaning: Don’t underestimate the payoff from spending some time checking for and fixing obvious gaps or errors in the data. For example, standardize client names or remove outdated files (e.g., data older than five years unless it is unique in some way). Beginning with a small project means this cleaning work should not be overwhelming, and it will likely dramatically improve the AI’s accuracy.
- Known Outcomes as a Guide: Choose pilot projects where it is known what a “good result” will look like. This may mean using data that has been used in the past to produce a correct solution. This way if the AI wanders off track, it is clearly evident and the project can be easily course corrected.
A focus on data quality minimizes risk. When data is clean, the micro-agent can showcase AI’s value without getting tripped up by avoidable data issues. Essentially, ensure the footing is solid before the bot takes its first steps.
Real-World Use Cases: AI in Document Retrieval and Legacy Content
Consider two illustrative examples of agents or targeted AI implementations that have the potential to consistently deliver significant benefits across various professional services:
- Intelligent Document Retrieval and Review Agent: Leveraging AI for efficient and accurate retrieval of historical documents, relevant data, and key insights can dramatically streamline the proposal development and review process. By swiftly accessing pertinent information, organizations can reduce response times, enhance document accuracy, and refocus human efforts on strategic analysis and client engagement. Say goodbye to Control F!
- Legacy Content Utilization Agent: Employing AI to rapidly search and analyze historical project data has allowed professional services firms to identify reusable insights and patterns efficiently. This accelerates decision-making, reduces redundancy, and elevates overall service quality and consistency. In practice, this means a consultant might quickly uncover how a similar issue was solved two years ago, saving time and effort.
These examples underscore the tangible, quantifiable value of targeted AI in professional services settings. By slowly improving efficiency and freeing staff to focus on higher-value work, small AI bots can build confidence in broader AI initiatives.
Failing Small, Learning Big: Lessons from AI Pilot Projects
Not every experiment produces great results, and that’s okay. Starting small means that if something doesn’t work out as expected, the impact is contained and becomes a learning experience rather than a major failure. As the organization begins its AI journey, it’s important to treat each pilot as an opportunity to gather feedback and learn those early lessons.
Key principles when facing an AI setback:
- Analyze Why, Adjust How: Dig into whether the issue was due to data quality, the model approach, or even user adoption. Was the data not as clean as assumed? Did the agent need better instructions? Understanding the root cause guides the next step.
- Pivot, Don’t Scrap: Instead of abandoning AI efforts, pivot. For example, if a customer support bot wasn’t very accurate, narrow its focus to fewer question types or choose another domain where the language is more consistent. Because these are small projects, making a change is relatively low cost.
- Document Lessons: Make it a practice to record what is learned (e.g., “Bot A had trouble with task X because data source Y was incomplete”) in an open and consistent manner. Over time, this builds an internal knowledge base of dos and don’ts, improving each subsequent venture.
Importantly, early setbacks reinforce a cautious and incremental approach. By failing small, the organization can refine its methods. This iterative learning aligns with the notion of “ongoing evaluation and continuous improvement” during scaling. Each mini project, whether a rousing success or a useful failure, contributes to the collective wisdom that propels the AI program forward.
Scaling AI: From Small Wins to Broader Integration
Encouraged by these initial successes and lessons learned, organizations can adopt a thoughtful and incremental approach to scalable AI solutions. Gradually increasing complexity and scope allows for controlled growth, ongoing evaluation, and continuous improvement. This deliberate approach helps minimize risk and ensure sustainability.
For example, after a firm achieves wins in document retrieval and data mining, its next step might be to combine those capabilities or expand them to new practice areas or teams. Because each step builds on proven results, stakeholders remain supportive, and users grow more comfortable with AI tools. This deliberate AI implementation strategy ensures the organization is never leaping blind into an enterprise-wide rollout. Rather, it’s scaling up with purpose based on real-time feedback and measured outcomes at each stage.
As the organization scales, two things happen: its internal AI infrastructure and expertise are strengthened, and its trust in AI grows. By the time it’s ready for a broader deployment, the organization has a solid track record and a knowledgeable team, as well as leadership buy-in thanks to the accumulated evidence of value. In other words, small wins pave the way for bigger moves.
Ensuring AI Integrity: Data Governance from Day One
Adopting AI in professional services means dealing with a lot of confidential and sensitive data: client information, contracts, financial details, and so on. Living up to “AI With Integrity” requires strong AI data governance—ensuring guardrails are in place to prevent any accidental disclosure of sensitive information. Even when implementing a small AI bot, data governance and privacy controls must be front and center. Here are some simple ways to ensure a bot/agent doesn’t report what it shouldn’t:
- Principle of Least Privilege: Limit what data the bot can access. If an agent is designed to retrieve historical case studies, perhaps restrict it to a specific folder or database that contains only nonconfidential case studies. By not giving it access to ultra-sensitive files in the first place, the risk of those ever showing up in responses is eliminated.
- Governance Oversight: As the firm’s AI ecosystem grows, plan to use governance agents (much like an automated compliance officer). Recall the idea of specialized agents in a swarm; one could be a compliance agent that monitors the others. In the future, multiple AI agents will cross-check each other, with one specifically watching for policy breaches (like leaking confidential info) and flagging or stopping them in real time.
By embedding these practices, a professional services firm’s AI solutions uphold the same standards of confidentiality and integrity that its clients and stakeholders expect in all its professional services. Trust is the currency of AI adoption; users must trust that the system won’t spill secrets or act irresponsibly. Strong AI data governance from day one helps secure that trust.
Introducing Agent Swarms
After steadily expanding capabilities through individual GenAI use cases, the next evolutionary step in the AI journey is the concept of agent swarms.[3] An agent swarm involves multiple specialized AI agents working together seamlessly in real time. Instead of one big AI trying to do everything and failing, you have a team of AI small-scale agents—each an expert in a particular function—that collaborate to tackle complex, interrelated tasks.
What could an agent swarm look like in practice? Imagine our professional services assistant of the future composed of, say, five bots:
- A Compliance Agent checking to ensure that no confidential information is contained in outputs.
- A Data Validation Agent checking figures or facts against databases.
- The Risk Assessment Agent using historical data to evaluate the inherent risks for a process.
- The Information Retrieval Agent, scouring the trackers for the exact information that is needed for a task.
- And perhaps a Summary or Insight Generator Agent, reviewing trackers and data and seeing the patterns that a human may miss.
When do swarms enter the picture? Agent swarms come into play when an organization’s AI adoption is mature enough; after it’s built the trust, the infrastructure, and the data governance to let these agents collaborate. It’s not wise (or feasible) to attempt a swarm on day one. But once, say, a firm has implemented five or six agents, each doing useful work, and has put into place the proper integration and governance plumbing, connecting those agents will amplify returns.
By coordinating specialized agents, these swarms elevate operations to new heights of efficiency and intelligence, while still operating within the safe bounds that have established.[4] It’s a vision of the future that the organization has been steadily working toward—one small bot at a time.
Conclusion: Build AI with Integrity, One Small Bot at a Time
Building upon the groundwork laid in our first article, this perspective underscores not only the importance of initiating AI integration through well-defined micro-AI projects, but also of preparing for those projects correctly and learning from every outcome. These successes and lessons provide operational improvements and serve as stepping stones toward more complex, interconnected AI systems. By prioritizing focused, impactful projects and coupling them with strong AI data governance and integrity practices, organizations can build the trust, infrastructure, and expertise necessary for broad and effective AI adoption.[5]
To learn more about agent swarms, data governance in AI, or for guidance on implementing small-scale AI solutions with integrity, please reach out to K2 Integrity. Together, we can continue to advance on this exciting and transformative journey.
[1] Brian Eastwood, “Practical AI Implementation: Success Stories from MIT Sloan Management Review,” MIT Sloan School of Management, 1 April 2025, https://mitsloan.mit.edu/ideas-made-to-matter/practical-ai-implementation-success-stories-mit-sloan-management-review.
[2] “AI Not Just for Large Companies, As Small and Midsize Businesses Reap Benefits Too,” Business Wire, 3 December 2024, https://www.businesswire.com/news/home/20241203670474/en/AI-Not-Just-for-Large-Companies-As-Small-Midsize-Businesses-Reap-Benefits-Too.
[3] Rachel Gordon, “Multi-AI Collaboration Helps Reasoning and Factual Accuracy in Large Language Models,” MIT News, Massachusetts Institute of Technology, 18 September 2023, https://news.mit.edu/2023/multi-ai-collaboration-helps-reasoning-factual-accuracy-language-models-0918.
[4] Michael Nuñez, “OpenAI Unveils Experimental ‘Swarm’ Framework, Igniting Debate on AI-Driven Automation,” VentureBeat, 13 October 2024, https://venturebeat.com/ai/openai-unveils-experimental-swarm-framework-igniting-debate-on-ai-driven-automation/.
[5] “AI Adoption Surges to 72% among Professionals,” The CFO, 3 June 2025, https://the-cfo.io/2025/06/03/ai-adoption-surges-to-72-among-professionals/.