Many organizations have already given employees access to generative AI tools, launched isolated pilots, and begun experimenting with agentic systems. Yet experimentation is not the same as operational value. The harder question is how to redesign work so that AI improves measurable outcomes while preserving accountability, security, and trust.
Based on the analysis by Ankit Kapoor, the central leadership challenge can be framed simply: decide what to build, what to buy, and where to partner. The right answer will rarely be the same across an entire organization. It should be determined workflow by workflow and capability by capability, with strategy leading technology selection.
For business owners, executives, operators, and investors, this framework offers a practical way to move beyond enthusiasm or hesitation. It helps leaders determine what must remain distinctive, what can be sourced from the market, and where outside expertise can accelerate responsible implementation.
Start with the business problem, not the technology
AI projects often begin with a tool. A leader sees a new model, platform, or agent demonstration and asks where it can be used. That sequence can create activity without creating value.
A stronger approach begins with the operating model:
- Which workflows consume significant employee time?
- Where do delays affect customers, revenue, service quality, or compliance?
- Which decisions require judgment, and which steps are repetitive?
- Where do inconsistent procedures create avoidable risk?
- What outcome would justify investment?
This diagnosis should include the current workflow, its inputs, decision points, approvals, exceptions, systems, and handoffs. Leaders should also identify how performance is measured today. Without a baseline, an organization cannot determine whether AI has improved speed, accuracy, cost, service, or employee capacity.
The source commentary argues that many organizations have accumulated layers of exceptions across business units, geographies, and teams. Before automating those layers, leaders should determine whether the underlying process is coherent. AI can assist by reviewing documentation, training materials, process maps, and transaction patterns. It cannot decide which exceptions reflect legitimate business requirements and which are simply historical habits.
Process simplification comes first. Automation should not preserve confusion at higher speed.
Build the capabilities that make the business distinctive
Building is appropriate when the capability itself can create durable differentiation. That may include a proprietary workflow, specialized institutional knowledge, unique data, a distinctive customer relationship, or a decision process that competitors cannot easily replicate.
A custom capability may be worth developing when:
- It directly supports a core business advantage.
- It depends on proprietary data or operating rules.
- Existing products cannot meet the organization’s security, integration, or control requirements.
- The company has the technical leadership and resources to maintain the capability over time.
- The organization is prepared to own testing, monitoring, documentation, upgrades, and risk management.
Building does not necessarily mean training a foundation model. In most cases, the differentiating layer will sit above the model. It may consist of carefully designed workflows, retrieval systems, evaluation methods, business rules, integrations, approval structures, or domain-specific data products.
This distinction matters. A company may buy access to a language model while building the way that model interacts with its own policies, systems, customers, and employees. The model may be widely available. The operating knowledge surrounding it may not be.
Building also creates obligations. Ownership includes long-term maintenance, cybersecurity, employee training, incident response, and replacement planning. A capability that appears strategic in a pilot may become expensive to operate at scale. Leaders should test whether the organization can sustain the capability for several years, not merely launch it.
Buy mature foundations and common functions
Buying is often the right choice when the capability is widely available, rapidly advancing, or not central to competitive differentiation. Mature technology providers may offer capabilities that would take an internal team substantial time and expense to recreate.
Common candidates for purchase include:
- Access to large language models
- Cloud infrastructure and computing capacity
- Data storage and data services
- Identity and access controls
- Security tooling
- Monitoring and observability
- Governance and compliance features
- Agent-development platforms
- Standard document, search, classification, and summarization functions
Buying can shorten time to value and allow internal teams to focus on business-specific work. It can also provide access to ongoing vendor investment in performance, security, reliability, and infrastructure.
That does not make procurement a substitute for leadership. An organization still owns the responsibility for selecting a suitable provider, classifying its data, reviewing contractual terms, managing access, validating outputs, and ensuring that the solution fits the workflow.
Executives should examine total cost of ownership rather than subscription price alone. Relevant costs may include integration, data preparation, training, change management, monitoring, internal support, vendor switching, and operational downtime. Interoperability also matters. A product that works well in isolation may become restrictive if it cannot connect to the systems and data required for broader operations.
Vendor concentration deserves attention as well. A low-friction purchase can create long-term dependency if the organization has no practical alternative, no data portability, or no documented exit plan.
Partner for transformation, implementation, and adoption
Partnerships are most useful when an organization understands the business problem but lacks sufficient engineering depth, implementation capacity, workflow design experience, or change-management resources.
A capable partner may help with:
- Process redesign
- System architecture
- Integration
- Agent development
- Data preparation
- Employee training
- Governance design
- Pilot management
- Adoption and change management
- Specialized engineering
A partnership should not mean indefinite dependence. Before work begins, leaders should clarify ownership of data, code, documentation, architecture decisions, security controls, performance metrics, and post-launch maintenance.
A strong agreement should also address knowledge transfer. Internal employees need enough understanding to supervise the system, evaluate performance, manage routine changes, and make informed decisions about future sourcing. The operating model should specify whether the partner will provide continuing support, transfer responsibilities to the internal team, or remain a specialist resource for defined activities.
Exit terms are equally important. Leaders should know how data, configurations, documentation, and code will be transferred if the relationship ends. They should also understand the practical cost and timeline of moving to another provider.
The best partnership creates capability inside the organization. It does not simply create a permanent external dependency.
What one external example shows
The source commentary describes an external real estate-services company that sought to reduce manual work in finance, particularly the matching of customer payments to invoices and the billing process. According to the commentary, the company used existing cloud infrastructure and agent-development tools rather than building the foundational technology from the ground up.
The distinctive work involved translating employee expertise and business rules into AI-supported workflows. The system reviewed financial documents, checked information against policies, identified uncertain or sensitive cases for human review, and recorded results in financial systems. The source reports that roughly half of the targeted workflow steps were automated.
This example is presented here as an attributed industry example, not as an MFHC case study. MFHC has not independently verified the reported results, and this article does not suggest that the example involved MFHC, its portfolio organizations, or their clients.
The lesson is the combination of decisions. The company reportedly bought foundational technology, built business-specific workflow logic, and partnered for implementation, process redesign, and change management. That structure allowed employees to focus on judgment and oversight while software handled more repetitive, rule-based steps.
Governance must be designed into the workflow
Human oversight should not be added after deployment. It should be part of the initial workflow design.
For each AI-enabled process, leaders should identify:
- Which tasks AI may perform independently
- Which outputs require human approval
- What level of uncertainty triggers escalation
- When the process must return to manual handling
- Who owns the final decision
- How corrections and overrides are recorded
- How incidents are reported and resolved
Controls should be proportionate to risk. A low-impact drafting assistant may require different review standards than a system handling financial records, employment decisions, customer eligibility, health information, construction safety, or property-related transactions.
The NIST AI Risk Management Framework offers a useful voluntary reference point for organizing governance around governing, mapping, measuring, and managing risk. Practical controls can include role-based access, data minimization, audit trails, model and vendor monitoring, testing for accuracy and bias, documented escalation paths, and periodic review of system performance.
Training is part of governance. Employees should understand what the system can do, what it cannot do, when to challenge an output, and how to report a concern. The Comportment Group’s focus on professional presence, communication, workplace comportment, and executive readiness offers one relevant perspective: responsible AI adoption also depends on how people communicate expectations, exercise judgment, and maintain trust when technology changes the way work is performed.
A practical build, buy, or partner checklist
Before approving an AI initiative, leaders can work through the following checklist:
- Define the business problem and the desired measurable outcome.
- Map the current workflow, including systems, decisions, approvals, and exceptions.
- Identify the capability that could genuinely differentiate the organization.
- Classify the data, including confidential, personal, financial, or regulated information.
- Test available buy options against security, integration, governance, and performance needs.
- Determine whether internal teams can build and maintain the differentiating layer.
- Define partnership terms, including ownership, knowledge transfer, support, and exit provisions.
- Set a limited pilot with a baseline, success measures, budget, timeline, and review point.
- Establish human controls, escalation paths, auditability, and fallback procedures.
- Review results before scaling, including total cost, adoption, quality, risk, and employee impact.
What this means for Bay Area operators and MFHC
For an Oakland or Bay Area holding company, AI strategy is not limited to software companies. It touches property operations, construction coordination, hospitality, investment analysis, professional development, and in-home services.
Drea Finch Real Estate Services offers a real estate perspective on the importance of accurate information, responsive service, and sound judgment. Atlas Premier Services & Consultants provides a construction and project-management perspective in which documentation, scheduling, coordination, and accountability are central. McFadden-Finch Restaurant Consulting Group brings a hospitality perspective where guest experience and operating discipline must remain connected.
Mission Cats In-Home Care illustrates why reliability, communication, and individualized service matter in an in-home care setting. Nucleus Holdings contributes a business-development perspective focused on operational improvement, innovation, and sustainable growth.
These are separate portfolio organizations with distinct missions and sector perspectives. This article does not claim that any of them built or deployed the external example described above, invested in a named AI company, or established a client relationship based on this analysis.
MFHC’s interest in AI infrastructure and Oakland’s emerging innovation economy is similarly analytical. Responsible adoption can strengthen local businesses when it improves productivity, develops workforce capability, and supports durable enterprises. It can also create new risks if leaders overlook privacy, access, accountability, or the community effects of workforce change.
The build, buy, or partner decision is therefore an operating decision, not a technology slogan. Leaders who begin with workflow clarity, preserve what makes their organizations distinctive, and establish practical safeguards will be better positioned to pursue innovation without losing control of the business.
Explore the MFHC Resource Library for additional perspectives on leadership, operations, project management, real estate, hospitality, pet care, and business strategy, or contact MFHC for general information about strategic growth and responsible innovation.
Published by McFadden-Finch Holdings Company. MFHC builds value-driven ventures across hospitality, real estate, community philanthropy, and pet care, uniting expertise across industries to deliver sustainable growth, quality, and trust. To explore partnership or engagement, visit www.m-fhc.com.
Built to grow strong businesses, meaningful partnerships, and lasting community impact. Connect with McFadden Finch Holdings Company today.
McFadden Finch Holdings Company
Vision. Leadership. Lasting Impact.
Lake Merritt Plaza
1999 Harrison Street, 18th Floor
Oakland, CA 94612
(800) 994-9028 | (510) 973-2677
Fax: (800) 210-5252
www.m-fhc.com | info@m-fhc.com
McFadden Finch Holdings Company (MFHC) is a premier holdings and investment management firm dedicated to driving sustainable growth and long-term value. Our mission is to bridge the gap between visionary capital and community-centric development, ensuring tomorrow’s infrastructure meets today’s needs. Through strategic project management and rigorous market analysis, we empower our partners to navigate the complexities of the California economic landscape with confidence and clarity.
Disclaimer: This article is provided for general informational and educational purposes only. It does not constitute legal, financial, investment, tax, accounting, securities, lending, real estate, architectural, engineering, construction, employment, veterinary, medical, nonprofit, philanthropic, public-policy, or other professional advice. Business conditions, regulations, services, programs, costs, funding, investment criteria, and availability may change. Readers should verify current information and consult qualified professionals before acting. References to McFadden-Finch Holdings Company, its subsidiaries, portfolio organizations, affiliated nonprofits, outside organizations, products, services, or resources do not imply a guarantee of engagement, funding, investment, approval, availability, endorsement, partnership, or outcome. Reading an article or submitting an inquiry does not create an advisory, fiduciary, client, funding, investment, or professional relationship.
