I have now written two 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.
This third blog completes the AI Leadership Brain Trust – Strategy Framework and identifies 10 strategy skill domains, with corresponding questions for board directors and CEOs to answer with their leadership teams and help advance sustaining the last mile of AI. 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.
There are 40 overall skill domains in the AI Leadership Brain Trust Framework, and to see the full roster, see my first blog.
The art of growing lies in the quality of the dialogue across different organizational stakeholders. AI governance cannot be delegated to only be in the stewardship of CIOs alone, multiple disciplines are required to build an effective and sustaining AI operating infrastructure, reaching across all organizational functional lines. The companies that think deep and wide and concentrate on fueling value-add use cases will increase organizational confidence to invest in AI over the long-term and will stay ahead of those that cease to experiment.
If there was ever a time to push harder to innovate – it’s clear now. Covid-19 has accelerated the movement to the cloud and the majority of companies have now recognized the critical need of advancing digital literacy to not only navigate global pandemics – but more importantly to get ahead and have more foresight.
Too often, I see leaders being far too narrow in their AI approaches and not recognizing that AI is a fundamentally new set of skills that they must learn to be relevant to their ever- changing operational roles. There is a new language to learn. Just like we had to learn the language of total quality and appreciate terms like root causal analysis, understand the value of process re-engineering techniques, like 6Sigma, etc., and learn new terms like: can Ishikawa diagram or a Pareto chart, etc. With AI, leaders need to learn the diverse types of AI and appreciate that problems require a diverse type of AI methods to solve the problem type. For example, if I am solving a future forecast prediction, I may consider using a predictive method, and apply a random forest AI technique vs simpler linear regression methods.
Note: A random forest is an ensemble learning AI method for classification, regression, and other tasks that operate by constructing a multitude of decision trees at training a model and outputting the class that is the mode of the classes (classification) or mean/average prediction (regression) of the individual trees.
Below there are 10 strategic skill domains that require board directors and CEOs to seriously examine and reflect on the questions highlighted.
- Vision And Strategic Orientation (Discovery): Where does AI fit in your organization? Where could AI take your organization in terms of new opportunities? What are your competitors doing in AI? Could AI disrupt your current business model, if so what are the risks?
- Customer Centricity Focus (Discovery): What problems are your customers trying to solve? Could AI help them solve their problems? How could you bring AI knowledge to help your customers modernize to use AI? What new products/solutions could you develop to help your customers achieve their goals?
- Channel /Partner Relationship Management: (Discovery): Are your channel and partner relationships clearly defined, segmented and prioritized? Of the channels that are prioritized, do you understand what their AI strategy is to advance their organization? Have you developed a relationship with your channel partner’s leaders of advanced analytics or AI to share best practices and ensure your organization is demonstrating value and vice versa? Do you understand what AI use cases that your tier one channel partners are solving? Are there areas that your organization can bring additional skills and competencies to bear to strengthen your channel and partner relationships in using AI to create a unique competitive advantage?
- Innovation Maker (Incremental And Breakthrough) (Discovery): Given there are two forms of innovations, incremental (building off existing capabilities, often referred to as continuous improvement), or breakthrough (developing a unique, large scale, and potentially disruptive approach to your current business model, practices, etc.), have you stratified your AI use cases into incremental innovations (smaller scale) versus breakthrough (larger scale)? Are your innovation initiatives evenly distributed or skewed or are they synergistic supporting AI advancements from different perspectives which over the long term will yield higher value outcomes? Have you recently evaluated your current talent pool to determine their skills in innovation (both incremental and breakthrough)? How strong is your culture in embracing new technologies that are more difficult to understand? How patient is your organization supporting iterative exploration and accepting failed experiments? Do you formally measure innovation as a core leadership behavior? Is innovation one of your corporate values? Are you investing in ongoing training and development to support the growth of innovation capacity and skill development? Are you measuring the results of your AI programs in terms of their innovation value contributions?
- Talent Builder (Attract, Develop, Retain) – Do you recruit (attract) talent based on their baseline entry skills to ensure they are digitally literate, value curiosity, and are agile? Are they skilled in the math and sciences, especially in: statistics and analytics (note: AI skills need foundations in these disciplines for more effective business engagement with deeper AI/computing science/data science skills)? Do you train all employees in analytics and statistics as a core leadership skill to operate in a more data-centric and digitally intelligent world? Does your talent have access to analytical BI tools to help them perform and develop their skills (ie: Looker, PowerBI, Qlik, SAS, SISENSE, Tableau to name a few)? Does your talent have access to build stronger analytical skills in areas like data sciences, computing sciences, artificial intelligence so they can see clear career progression and have access to learning and development programs for additional certifications, or supporting them on taking advanced degrees? What learning and development incentives do you have in place for your talent to understand the importance and value of data management? Are you building data management knowledge skills in the importance of being diligent and ensuring data is well documented, data is clearly connected to process workflows (inputs, outputs) and has clearly defined process owners (swim lanes), and an overall end to end (E2E) process owner clearly identified?
- Governance And Cross Functional Stewardship – Does your organization have cross-functional governance stewardship operating group responsible for the development of an organization-wide AI strategy, ensuring compliance to privacy, regulatory and ethical matters? Has your governance leadership team, if in place, been trained in Artificial Intelligence and Data Science Disciplines, so they have a strong baseline understanding of these disciplines. Are there external/third party coaches or internal coaches assigned, so leaders can build stronger AI governance leadership skills? Are there representatives from all major functional lines engaged in the governance of AI in particular from customer relationship areas (i.e.: sales, marketing, customer service, etc.,) human resources, legal, privacy, analytics, information technology, finance, etc.). Is there a clearly defined AI strategy and roadmap with key performance indicators (KPIs) to measure the effectiveness of the AI governance stewardship group? Are the KPIs clearly defined with regular CEO and or board reviews to ensure the guideposts are based on best practices?
- Strategic Process Value Chains – Does your organization have an enterprise process architecture clearly identifying the process value chains with representations of workflows, and data attributions to bring clear visibility to the underlying data architectures “at work” supporting your organization? Do you have a data dictionary in place that easily enables you to decompose process logic, connect data logic, and clearly identify ownership levels to support AI modeling practices?
- Financial Value Realization – Does your organization has clearly defined financial value realization metrics connected to the AI strategy and governance operating practices to ensure financial metrics are clearly defined and are the KPIs supported by financial experts? note 1: examples of metrics: (lifetime value of a customer (LTVC) could be determined from predictive AI methods, reduction in customer churn could be determined by the emotional language (voice or written, or declining communication interaction patterns – what is the cost of a loss of a customer to the business, etc.)? note 2: PWC has gone of record indicating that the CFO is in a stronger position to oversee all KPI metrics that relate to AI vs other stakeholders to ensure an overall governing framework is in place for KPI measurements.
- Strategic AI Transformation Management Maker – Does your organization have a senior executive that is responsible to lead the AI strategic transformation to support the program requirements for E2E (end to end) thinking (business strategy, information technology, AI, data sciences, analytics, change management, leadership know-how)to help you advance your AI strategy and implementation requirements? Is the resource assigned as a strategic AI transformation management maker a role model communicator that can influence your organization to evolve its current thinking and ways of doing things? If the transformation maker is an external new hire, how are you supporting the change agent with trusted leaders to help mentor him or her to ensure his/her integration is successful?
- Focus Management (Prioritization Rigour In The Age Of Distraction). Is there a clearly defined AI strategy roadmap with clearly defined execution journey management milestones that reinforces that the AI program is focused? Is your organization investing in diverse skill development programs to support attention deficit disorder behaviors, reinforce the importance of calmness, and managing stress levels, given the declining rates of productivity due to technology over-stimulation or often called “the constant zoom fatigue”? Is your company investing in AI software tools that specialize in cognitive sciences to prioritize workload activities, complete administrative, customer service, or sales activities using intelligent bots in areas like administrative email assistants (i.e.: Doodle, X.ai, etc.), sales workload /opportunity prioritization (i.e.: SalesChoice, TopOpps, etc.), Call Center Churn Risks (i.e.: Affectiva, Cognitec, CrowdEmotion, etc.). Other tools like Apple Siri or Amazon Alexa are also tapping into the conversational AI market to provide prescriptive analytics or directional communication support in literally every business process domain.
This blog has identified ten AI strategic skill areas and over fifty questions to help board directors and CEOs have a discovery dialogue with their leadership team(s).
AI has been referred to by many as the new oil, but I like to refer to it as the new oxygen as it will permeate everything we know of in the business, home, and in our world. There is not a single industry that will not need to transform due to some new AI possibilities. We need to embrace AI and the exciting new skill sets where AI can help us rethink our business models and value chains. AI will leave nothing unmarked or untouched.
The big question is as a leader: how are you helping your organizations move forward with clarity, conviction, and confidence in using AI for your competitive advantage?
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.