Key Takeaways:Using AI and predictive capability is not new but generative AI brings AI to the mainstream due to its human-like communication and ease of accessibility to companies of all sizes. For boards to properly oversee AI risk, they need to require management to establish a model risk management process along with a model risk governance framework around AI implementation that includes standardization of algorithms, governance and ethical considerations as well as validation of data inputs and outputs.Moving to AI changes the human role and underscores the need for a strong risk culture and exercising of healthy skepticism to contend with potential bias, complexity and lack of integrity within both data sets and AI algorithms.Underlying data sets must be of high quality and representative of population they are intending to serve – free from bias, properly labeled and annotated.Boards, management and auditors will need to continue to stay apprised of what is sure to be a continually evolving standard setting and rule-making environment as technologies continue to evolve.

Key Takeaways:Leveraging data analytics for strategic decision-making: Use of data analytics in the boardroom to is becoming increasingly important to help directors make informed, evidence-based decisions that drive growth and efficiency. By incorporating data-driven insights, businesses can better understand their customers, optimize operations, and identify new opportunities.Overcoming challenges in implementing data analytics: Organizations need to be prepared to face significant challenges when adopting data analytics, such as data quality, integration, and fostering a data-driven culture. Listen to best practices and practical advice for overcoming these obstacles and ensuring a successful analytics implementation.Board’s role in promoting data-driven culture: The board can play a critical role in promoting a data-driven culture within the organization. By asking the right questions, setting expectations, and ensuring alignment with business objectives, boards can support management teams in harnessing the full potential of data analytics for the organization’s success.

Boards need to work with management team to continue to identify and prioritize opportunities in automation to enhance all aspects of operations, product development and service delivery. Challenges in implementing automation need to be identified and incorporate into a strong change management program to ensure enterprise-wide acceptance and adoption.Boards can play a significant oversight role in the automation process by helping management establish guardrails – policies, protocols and controls – and require reporting back on progress as an accountability mechanism in driving change. Become and remain familiar with emerging automation technologies and methods to be competitive and optimize the business’ capabilities.

Key Takeaways:1. Approaching embedding AI into a service delivery model requires: Identifying and prioritizing AI business processes and use cases that align with your business objectivesAssessing the feasibility and potential impact of each use caseDeveloping a detailed implementation plan that contemplates the steps, resources, and timelines required for successful AI adoptionKeeping cybersecurity at top of mind as well as quality assurance approaches. 2. Adoption and use of AI requires encouragement of curiosity and innovation tempered with a good understanding of the risk and challenges AI poses to current methods of obtaining and authenticating data.3. Compliance with regulatory standards needs to factor into AI change management along with corporate culture, policies and procedures regarding the use of AI.4. Auditors are currently embracing AI in measured approaches – e.g.: Leveraging digital assistants as a gateway to demystify technologies and to bring the talent pool up to speed on the basics – what it is, how they work, and general best practices.Using generative AI environments to streamline and further standardize the firm’s service delivery models and build capacity.Embedding AI into existing tools to surface unusual and irregular transactions that the humans may otherwise miss as quality focused.Considering AI-powered image recognition in verifying physical assets and reconciling with financial records.

Key Takeaways:Use of technological automation in the audit can yield increased efficiency, improved accuracy, consistency and quality. Complexity, data quality, lack of flexibility, resistance to change and over-reliance on technology are challenges to automating auditing procedures that need specific consideration.Auditors are intentionally and systematically focusing on automating aspects of the audit that build capacity and streamline systems but that also focus on feedback and enablement of adoption.Board members should be asking auditors about automation use cases and recommendations for improvements to apply to their own financial accounting, reporting and controls areas.

Key Takeaways:Service delivery models demand that auditors demonstrate their efforts are directed principally to potential risks and anomalies and areas of higher risk.Boards that derive the most value from audits that leverage data analytics are those that have pretty specific views of what constitute outliers or anomalies for the business and that understand the auditors’ demonstrations of how their analytic tools can quickly surface and identify risks and patterns that may not be immediately apparent through traditional audit methods. Boards have potential opportunities to enhance both the value of the audit as well as the reporting information provided by management – e.g., address disparate systems, enhance limited data hygiene and inexact controls around data. Greater scrutiny of information, including relevant controls over data and data integrity, requires auditors to focus on modernizing their professionals’ experience, as well as audit technology development, as they are the agents of change in enhancing audit quality. Boards should be asking their auditors to show them the value that data analytics is providing and become better informed as to: how difficult is the data to extract; how much cleansing is necessary; does it reconcile; how well is it controlled; etc.

Key Takeaways:Building corporate sustainability is not about “being responsible for” but rather recognizing “how to work in concert with” a broader group of stakeholders to reduce friction and produce better results.Innovation in a staid industry requires: - Creation of an environment to embrace and enable change – first speak about what will remain the same and season in what the change will be.- Recognition that incumbency is NOT powerful (i.e., faulty assumption that formulas in the past will work in the future)- Innovate by embracing what is evolving – e.g., technologyAdopt a simple "Measure/Report/Reduce” framework – allows managements to act and the board to make informed decisions based on data-driven metrics to drive accountability and achieve whatever the “reduction” goals are – e.g., emissions, talent attrition, customer loss, use of resources, barriers to entry, etc. Don’t make the mistake of focusing heavily on low hanging fruit instead of looking broadly at both risk and opportunityOversight tool: Instead of asking management “How do we make this?” consider the asking “How do we make this better?”Resources:Book: BOOM! Deciphering Innovation: How Disruption Drives Companies to Transform or DieResource Center: BDO ESG Center of Excellence

Key Takeaways:The NEED: Data governance programs exist to manage change in data assets overtime. The WHAT: Data is an intangible asset that is constantly evolving and has a host of characteristics that make it difficult to manage compared to traditional assets. THE WAKE-UP CALL: When surveyed, 95% of respondents felt data governance programs were unsuccessful.The PITFALLS: Poor data governance stems from: poor data quality, lack of enforcement of policies and procedures; and data security and privacy practice that don’t evolve with hacker sophistication.The HOW: - Board directors should be asking corporate management the following: - Does the company have a data governance program? - Is the data governance program working? How do you know? - What assistance does the data governance program need from the board? Directors should encourage management to decompose data governance problems into smaller initiatives and ask for stratification of data governance decision-making so that people at the right level are making decisions about data.Resources:Data Privacy and Governance Checklist for the Board

Key Takeaways:The basic tenants of succession planning require continuous focus and consensus and can provide an outline for preparation and vigilance. These include:Agreement on short- and long-term company goalsNeeded skills for the position - Keeping the job description of the upcoming vacancy current Keep a pipeline of candidates that span an array of various disciplines and experiences Boards should build in process and policy around board refreshment.Board composition should be built for purpose - Consider directors with multiple skills and experiences so there is overlap existing in the boardroom.Succession doesn’t end with appointment – it further requires a robust onboarding system for the new director. Expanding the board to accommodate new board members before the departure of a retiring director can efficiently provide transfer of institutional knowledge and avoid “reinventing the wheel.”A board member with shareholder engagement skills and investor understanding that can help management articulate their story is the best defense against activists. Resources:  “What ‘Succession’ Gets Right (and Wrong) About Business Continuity Planning [Spoilers]Think Like an Activist

Key Takeaways:Funding for the CHIPS Act has been signed into law. This act provides tremendous opportunities for semiconductor industry organizations to expand production in the U.S. and additionally, provides many other benefits to the economy. This podcast discusses:Opportunities for national economic initiatives for organizations that can pass threshold question – How does the project enhance national economic security? Under the Act, lessons have been learned from PPP funding during COVID and the need for attestations to counter fraudulent/inappropriate use of funding, which came back to haunt many companies who could not support their use of PPP funds after the fact. Resources:Archive Webinar - The CHIPS Act Has Passed: Now What?What Semiconductor Organizations Need to Know about the CHIPS For America ActSemiconductor Industry Association