I have written three blogs framing the critical need for a Board Director and CEO to build a focused AI Brain Trust and leadership program to build stronger skills and competencies to advance AI successfully in their organizations.
Over 80% of the companies investing in AI do not drive ongoing operational practices, rather AI is an investigative approach to often answer difficult questions, and then sponsorship of evolving the AI models atrophy and in many cases simply die off.
I have identified over 40 overall skill domains in the AI Leadership Brain Trust Framework, and you can see the full roster, see my first blog.
The second and third blogs define ten strategic skills from a strategic perspective with over 50 key discovery questions that a board director or a CEO can engage in to advance their organization’s strategy and stakeholder alignment regarding designing and implementing AI programs effectively, and they can be found at the end of this article.
This blog identifies 10 business skills and provides questions to engage in a dialogue to advance an organizations’ business skills in relationship to an AI perspective.
Business Skills:
- Customer Orientation: One of the easiest ways to advance AI in your organization is to identify all your core customer operating processes and being clear on their data density dimensions (volume, size, type, quality, etc.). Having one PowerPoint that your enterprise defining your organization’s end to end (E2E) customer operating processes and ease of data access are all key data sourcing questions that are key to getting your arms around your customer data. Key customer process areas usually ripe for AI are often founded in customer relationship management systems, where customer contact history can be easily tapped to solve use cases like identifying the best sales leads or sales opportunities for sales coverage, predicting sales forecasts based on historical data sets, identifying which customers with the highest odds of becoming a VIP (very important person) customer, identifying which customers have higher churn (loss) risks, identifying a customer’s personality type or culture fit to correlate with your sales coverage strategy, rewriting sales or marketing emails, or marketing campaigns, so the style of communication is intelligent enough to fit with your customer buyer profile, or even AI coaching on how to alter your voice to create a more emotional connection with your customers, or having AI chatbots answer customer questions, and even take complete customer order. There are hundreds of AI customer use cases that can add value to the customer experience; what’s key is identifying a challenge or problem area and then advance the use case that can add the most value for solving the specific customer challenge. There is nothing more important than customer growth and increasing customer share of wallet.
- Problem Solving Orientation: Most leaders do not know that the most important part of an AI journey is identifying the business problem clearly that you want to solve, and ensuring the problem has diverse stakeholder’s support. The problem must be significant as the journey to solve the challenge maybe months, or years, either way, building a sustainable operating process around the AI insights, and observations requires ongoing nurturing. AI is not like other problem-solving approaches, as once a transparent model is created it must be supervised, and it is like a small child. The model is always hungry and wants to learn more, hence more food (data in this case) is needed. So continual reflection on how to improve the AI model, with refreshed or new data or adding new methods to strengthen the predictions is an ongoing responsibility of the data scientists. AI models will easily atrophy if not maintained, and organizations must understand and plan for this or their AI models will be like abandoned floating code going nowhere. Rather, if the AI models are nurtured with data nutrients and care, promising growth may have blossomed propelling your organization to undiscovered new heights. The journey is like a discovery process, and the richness and clarity of the problem definition and its associated context is key to getting underway successfully.
- Analytical And Research Rigour: AI is an intensive analysis and research discipline and many model experiments need to be performed to find the strongest predictive accuracy to help answer the identified problem. Keeping track of the AI model type, research method(s) used requires careful note-taking and data scientists need to be evaluated on the quality of their documentation skills. New machine learning operations (MLOps) software are most helpful in this regard, and this is a rapidly growing market, as it helps to bring integrated AI model lifecycle management providing model versioning controls, model performance monitoring, model discovery (research), and model security and robust history so models are not orphans. Some of the leading AI market players include: Amazon, IBM, Microsoft Azure, and smaller emerging market leaders like: Data Robot, Dataiku, Data Splunk, H20.ai, Modzy, SignalFx, to name a few. According to Forrester, the MLOPs market will be over $4B by 2025, and five years ago this software category barely existed.
- Communication /Channel Relevance (social, written, voice, etc.): Using AI in business requires an effective governance communication plan and thinking through the best methods of communication channels to reach the stakeholders that will be impacted by the AI enablements. Too often AI projects are left, with only technology-centric resources who squirrel away delighted with their data nuts to munch on, and do not communicate clearly the evolution of the model approaches with clearly defined performance metrics in order to gain leadership confidence that their AI model investments are adding value to the organizations’ business goals. Whether clearly defined communication channels are in weekly or monthly management review meetings, it is imperative that board directors or CEOs ensure their AI program teams are responsible for developing a clearly defined communication plan, leveraging relevant channels to the audiences they need to reach. Do your AI programs have skilled communication resources engaged to support your operational requirements as new solutions or processes are developed as a result of your AI investments?
- Ethical Robustness (Transparency, Trust, Bias, Privacy): Do you have an AI ethical and data bias expert in your AI programs? How are you monitoring your AI programs against AI risks to ensure you are developing trusted AI practices? If there is an area that board directors and CEOs need to worry about in their AI programs, it is ensuring that your AI programs have quality ethical reviews. Even the type of questions that your organization chooses to explore will have ethical boundaries. For example, putting in an image monitoring system measuring the number of times an employee blinks or looks away in a Zoom call, could alert management that you may be bored, disinterested, or not paying attention. This sounds like surveillance cameras – on collaboration software and if you don’t know you are being monitored as new systems are now in the market that is attempting to evaluate your interest in new AI employee engagement systems. Is this the type of solution you want to invest in? These are increasingly going to be decisions that leaders will need to think about as AI is advancing into employee recruiting systems attempting to detect if you are lying, or avoiding questions if you blink, etc, yet you may well have a nervous eye tick and miss the job screening as the AI is not smart enough to understand this nuance unless you build in more pre-screening questions for more health accuracy. More concerning is ensuring the data set that you are using is representative of the population and the problem type you are trying to solve. Many data sets are data bias and draw inaccurate conclusions which can have legal consequences. The regulatory environments are in their infancy on AI ethical robustness, but 2021 will bring increased AI regulatory guidelines, from IEEE Standards. In the meantime, every board director, or CEO should be aware of two sources: the Organization for Economic Co-operation and Development’s recommendations for responsible stewardship of trustworthy AI, which forty-two nations have co-signed, and the European Union’s High-Level Expert Group’s Ethics Guidelines for Trustworthy AI. Ensuring that your AI program has an AI ethics review at the problem definition stage, and at the data sourcing and data methods stage are key risk management review gates to help ensure the outcomes (outputs) are not a legal or reputation risk to the company. IBM has software to detect data bias types, called AI Fairness 360, which includes a comprehensive set of metrics for data sets and models to test for biases, explanations for these metrics, and algorithms to mitigate bias in data sets and models. In larger organizations, we will see new leadership roles like AI Data Bias Risk Officers who will be like auditors in large scale programs. They will have a quality assurance responsibility to review and sign off on AI models, or companies will engage an audit firm, with expertise in AI risk management. EY is one of the most progressive global leaders in AI trust sense-making and has built a strong AI evaluation methodology on TrustedAI. Although KPMG, and PWC and many boutique firms also have AI audit practices, EY has a comprehensive TrustedAI framework that is one of the most comprehensive that I have seen to date.
- Program and Project Management
- Process & Data Management Orientation
- Measurement – Key Performance Indicators (KPIs)
- Finance and Business
- Sustainability Robustness (Environment, Human and Societal Well Being)
In my next blog, I will complete the 6-10 business skill competencies and discuss each of them from an AI perspective to complete the business skills framework discussion. I encourage you to review the first three blogs that are advancing the AI Brain Trust Leadership Framework.
originally posted on forbes.com by Cindy Gordon
About Author: Dr. Cindy Gordon is a CEO, a thought leader, author, keynote speaker, board director, and advisor to companies and governments striving to modernize their business operations, with advanced AI methods. She is the CEO and Founder of SalesChoice, an AI SaaS company focused on Improving Sales Revenue Inefficiencies and Ending Revenue Uncertainty. A former Accenture, Xerox, and Citicorp executive, she bridges governance, strategy, and operations in her AI contributions. She is a board advisor of the Forbes School of Business and Technology and the AI Forum. She is passionate about modernizing innovation with disruptive technologies (SaaS/Cloud, Smart Apps, AI, IoT, Robots), with 13 books in the market, with the 14th on The AI Split: A Perfect World or a Perfect Storm to be released shortly. Follow her on Linked In or on Twitter or her Website. You can also access her at The AI Directory.
