Key takeaways:
- Create a comprehensive AI strategy: Use disciplined planning to manage risk, maximize return on investment, and align AI initiatives with business objectives.
- Prioritize cybersecurity and compliance: Establish strong security policies and follow applicable legal and regulatory requirements to protect your operations and data.
- Invest in training and communication: Targeted education and clear communication can help employees adopt AI tools responsibly and adapt to new ways of working.
- Support responsible innovation: Combine ethical oversight with ongoing evaluation to improve efficiency, reduce costs, and generate sustainable business value.
Maximizing AI in Your Organization
Adoption and Initial Implementation
Companies are using artificial intelligence (AI) solutions—such as Microsoft Copilot—to streamline processes, increase productivity, and support innovation. High-growth businesses are continually seeking new ways to improve their digital operations and customer experiences.
Rapid AI adoption can improve operational efficiency, but adoption is only the first step. If you want to maximize your investment, you need a structured plan before rolling out new technology. LBMC’s AI and data capabilities help organizations identify practical use cases, evaluate readiness, prioritize initiatives, and build an implementation roadmap before significant investments are made.
Strategic Methodology and Cybersecurity
Your company can realize more value from AI by executing a comprehensive strategy that includes strong cybersecurity measures. This disciplined approach can also help differentiate your organization in a competitive B2B environment.
Addressing data security requirements from the beginning supports effective risk management and helps protect the long-term value of your technology investments.
Why Does a Strategic Approach to AI Implementation Matter?
The Value of a Structured AI Strategy
A comprehensive strategy provides a clear roadmap for integrating AI capabilities into business processes safely and effectively. One option is a 90-day implementation plan that divides the work into three phases: the first 30 days, days 31 through 60, and days 61 through 90. These phases should then be supported by a longer-term plan for scaling, governance, and continuous improvement.
A 90-Day AI Integration Framework
First 30 Days: Establish the Foundation and AI Policy
Lay the Foundation
This initial phase is critical because it aligns AI initiatives with company goals, security expectations, and compliance requirements. During the first 30 days, organizations should focus on building awareness, creating policies, and assessing their current capabilities.
Identify Stakeholders and Roles
Identify the stakeholders who will participate in the initiative and clearly define their responsibilities. Everyone involved should understand the organization’s objectives, decision-making authority, and expectations for implementation.
Establish an AI Policy
Create a policy that defines which tools are approved, how they may be used, and what governance requirements apply. Communicate the policy clearly so employees understand which platforms they may access and what information they may enter into them.
Conduct a Flash Assessment
Consider performing a preliminary assessment using a framework such as the NIST AI Risk Management Framework. This review can help identify weaknesses in current security, governance, and risk-management practices.
Set Standards and Goals
Establish clear standards for evaluating the business impact of AI initiatives. Develop a roadmap that defines short-term actions, long-term goals, expected outcomes, and the metrics that will be used to measure progress.
Days 31–60: Tool Selection and AI Governance
Select the Right Tools
Once the foundational elements are in place, the next step is selecting solutions that align with your organization’s needs and goals. To determine whether tools such as Microsoft Copilot are appropriate, assess how they integrate with Microsoft 365, Dynamics, existing workflows, security requirements, and applicable regulatory standards.
Businesses already using the Microsoft ecosystem may also benefit from understanding how Dynamics 365 Copilot connects sales, marketing, customer service, and CRM data to improve productivity across departments.
Create an AI Governance Committee
Establish a cross-functional governance committee to oversee the strategic, ethical, and effective use of AI. The group should include representatives from leadership, IT, cybersecurity, compliance, legal, operations, and relevant business functions.
Conduct Comprehensive Reviews and Evaluations
Evaluate each proposed tool against criteria relevant to your business environment. Confirm that it supports the intended use case without compromising operational integrity, security, privacy, or compliance.
This process should include input from appropriate stakeholders so the organization can identify potential implementation challenges before deployment.
Develop User Support and Training Programs
Design training and support programs that help employees understand how to use approved tools effectively and responsibly. Clear education can improve adoption, reduce misuse, and help teams generate more value from the technology.
Evaluate Integration and Business Impact
When connecting existing data and processes to platforms such as Microsoft 365, assess how Microsoft Copilot will interact with current systems and workflows.
An experienced advisor can help identify where Copilot may create the greatest return, particularly in areas such as data analysis, software development, customer engagement, and sales. Organizations interested in improving sales productivity should also explore how Microsoft Copilot for sales can streamline account management, automate administrative work, and help teams spend more time with customers.
Adopt Data Governance Policies
A specialist can also help ensure that a Copilot deployment aligns with corporate data governance standards and security requirements. Careful integration can reduce disruption to ongoing operations while improving performance and adoption.
Days 61–90: Pilot Testing, Evaluation, and Rollout
Conduct Training and Real-World Testing
The third phase focuses on using selected tools in controlled, practical environments. Activities may include purchasing licenses, configuring systems, training employees, and testing a customized use case.
A pilot program allows the organization to evaluate effectiveness before expanding deployment. It also creates an opportunity to address weaknesses, refine controls, and improve the user experience.
Measure Real-Time Impact
At this stage, businesses begin to understand how AI affects operations in practice. Pilot projects can provide early measurements of productivity gains, process improvements, user adoption, and potential risks.
These findings help the organization refine the technology so it better supports business needs.
Collect Feedback and Modify the Deployment Strategy
Use feedback from pilot participants, managers, IT teams, and other stakeholders to improve the deployment plan before a broader rollout.
This step is essential for making informed adjustments and increasing the likelihood of successful adoption.
Develop Skills and Confidence
Controlled testing gives employees time to become familiar with new tools and workflows. Building competence and confidence during the pilot phase supports a smoother transition when the technology is introduced across the organization.
Plan Future Use Cases
After defining and testing the initial use case, the governance committee should begin mapping additional opportunities. Align future initiatives with the company’s long-term objectives rather than adopting tools simply because they are new or widely promoted.
Beyond 90 Days: Scaling, Iteration, and Long-Term Strategy
Scale Successful AI Solutions
After the initial 90-day period, organizations can begin expanding successful solutions across additional teams, functions, or business processes. Scaling should be deliberate and supported by appropriate integration, training, governance, and performance measurement.
As organizations mature their capabilities, they often expand successful use cases into finance and accounting. The evolution of AI in accounting demonstrates how firms are using these technologies to improve research, automate repetitive work, strengthen decision-making, and increase client value while maintaining professional oversight.
Continuously Evaluate the AI Strategy
The organization’s strategy should be reviewed regularly after the initial implementation period. This may include long-term planning and a more comprehensive assessment based on the NIST AI Risk Management Framework.
Ongoing evaluation helps ensure that deployment remains secure, compliant, and aligned with changing business priorities.
Plan for Scalability and Continuous Improvement
AI integration is not a one-time project. It requires ongoing refinement as business requirements, risks, regulations, and technology capabilities evolve.
Build scalability into the strategy so successful solutions can grow without creating unnecessary operational or security problems.
Establish Feedback Loops and Performance Assessments
Conduct regular performance reviews and gather feedback from users and stakeholders. This helps ensure that tools remain effective and continue to support strategic objectives.
Performance assessments should consider business outcomes, user adoption, data quality, risk, compliance, and return on investment.
Iteratively Enhance AI Capabilities
Continue improving capabilities as new technologies, business needs, and user feedback emerge. An iterative approach allows the organization to introduce enhancements without losing control of governance, security, or strategic alignment.
Why Security and Risk Management Are Critical to AI Implementation
Incorporate Risk Management from the Beginning
Risk management should be part of the implementation plan from the start. Introducing new tools and data connections can create security, privacy, compliance, and operational concerns.
Continuously monitor deployments to identify vulnerabilities, address emerging risks, and maintain compliance with relevant laws, regulations, and industry standards.
Embed Security Standards
Security standards should be incorporated directly into deployment, including safeguards that help prevent unauthorized access and data breaches.
Perform regular security assessments and updates to ensure systems remain protected as new threats emerge.
Use the NIST AI Risk Management Framework
The NIST AI Risk Management Framework provides a structured approach for identifying, evaluating, and managing AI-related risks. It can help organizations improve the reliability, security, transparency, and accountability of their systems.
Address Ethical and Cybersecurity Requirements
Security and ethical considerations should be evaluated before, during, and after implementation. AI systems can be compromised, misused, or applied in ways that create unintended consequences.
Organizations can reduce these risks through strong security policies, regular assessments, approved-use standards, clear governance, human oversight, and appropriate protection of confidential information.
Take Action: Secure and Optimize Your AI Implementation
A strategic framework can help your organization use AI to improve performance while building the flexibility needed to adapt as technology changes. Work with experienced advisors who can provide implementation guidance, risk-management expertise, and ongoing support.
A comprehensive plan can help maximize your investment and establish a repeatable standard for effective adoption across the organization. Incorporating security, governance, and risk management from the beginning can prevent costly mistakes and reduce disruption.
Success starts with a strong foundation. A structured approach helps organizations not only deploy new tools, but also manage associated risks and realize their long-term business value.
If your organization is ready to move beyond experimentation and begin implementing AI with confidence, LBMC’s Data and AI services help organizations assess readiness, prioritize high-value use cases, establish governance, modernize data, and deploy solutions that deliver measurable business results.
Contact LBMC to begin building your organization’s AI roadmap.
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