This blog is a continuation of the Building AI Leadership Brain Trust Blog Series which targets board directors and CEOs to accelerate their duty of care to develop stronger skills and competencies in AI in order to ensure their AI programs achieve sustaining results.
In this blog series, I have identified forty skill domains in an AI Leadership Brain Trust Framework to guide board directors and CEOs to ensure they can develop and accelerate their investments in successful AI initiatives. You can see the full roster of the forty leadership Brain Trust skills in my first blog.
Each of the blogs in this series explores either a group of skills or does a deep dive into one of the skill areas. I have come to the conclusion that to unlock the last mile of AI value realization that board directors and CEOs must accelerate building a unified brain trust (a unified set of leadership skills that are hardwired in relevant digital and AI skills) to modernize their organizations more rapidly. Knowledge is key and if you locked up a room of board directors and CEOs in a board room and asked them
- What steps are required to build a successful AI Strategic Plan and Journey Roadmap – what do you think would be the outcome?
- Where are your AI Investments and have you inventoried them or audited them?
- What is the difference between a computing scientist, a data scientist, and an AI scientist – would their digital literacy skills be sufficient enough to lead and guide their organizations forward?
Sadly, I think we would find some very serious operational execution gaps.
Thus far in this series, we have addressed 10 strategic skills, 10 business skills, 10 emotional and social intelligence skills. The next series of blogs will breakdown the technical skills required for attracting, developing, and retaining technical skills in an AI or data sciences team. Last month, I discussed the importance of research methods literacy. In this blog, I will focus on Agile Literacy and explore its importance to board directors and CEOs to ensure they feel a call for action to ensure that their organization’s leadership teams are sufficiently agile to meet the constantly changing realities of competing in an increasingly smarter world.
Below is a summary list of the ten technical skills required, and in this blog, we will discuss the importance of agility and its relevance to artificial intelligence maturity success.
Research Methods Literacy
Agile Methods Literacy
User-Centric Design Literacy
Data Analytics Literacy
Digital Literacy (Cloud, SaaS, Computers, etc.)
Sciences (Computing Science, Complexity Science, Physics) Literacy
Artificial Intelligence (AI) and Machine Learning (ML) Literacy
Agile Methods Literacy
Last month, we discussed that skilled AI data scientists needed to have solid research methods across their data collection, data preparation, data modeling, and data value realization life cycle phases. The focus on research is important as it lays the foundation for agile, as AI projects require agile investigative methods that are resilient and require executives to have confidence in the exploration process, at the same time, agility to continually provide value outcomes is equally key to building sustainability confidence. So a balance between demonstrating short-term value is equally as important as accumulating longer-term value outcomes.
What Does Agile Mean?
Agile is an adjective and is defined as a ready ability to move with quick and easy grace. Agile in the context of software development denotes a method of project management, used especially for software development that is characterized by the division of tasks into short phases of work and frequent reassessment and ongoing adaptation of plans.
Another key context of agility is developing agile leadership behaviours given the fast paced nature of our world.
Many organizations have recognized the importance of agile leadership and define different observable agile attributes. Few would argue that agile leaders have a sense of urgency, create engagement opportunities across an organization, and are constantly curious in striving to ensure innovation is advancing. However, at the core of this sense of urgency, agile leaders must have a core foundation on integrity as speed can never compromise authenticity, transparency, and trust.
In the field of AI, trust is highly dependent on the ability to understand AI – and therein lies a trust-making gap, and resistance to executives approving ongoing AI budget requirements.
Why Is Agility Important To Cultivate In The Context Of Artificial Intelligence?
First, quality AI is a highly iterative experimentation, design, build and review process. Organizations that are aspiring to build strong AI and data sciences competency centers will flounder if their core cultures are not building agile skills into all operating functions, from top to bottom.
Given the incredible speed and uncertainties of everything becoming more digital and smarter, the imperative for all talent to continually adapt, reflect, and make decisions based on new information is a business imperative. Leaders do not have the luxury to procrastinate too long before acting on the new insights and making decisions with confidence. Sometimes, cultures can build a capacity for inaction versus action-oriented behavior. Agile leadership demands rapid precision, involving diverse stakeholders, which in turn, yields more positive change dynamics (momentum) and more importantly innovation capacity grows as a result of this energy force.
In a recent Harvard article, the authors pointed out that, “If people lack the right mindset to change and the current organizational practices are flawed, digital transformation will simply magnify those flaws.”
Truly agile organizations are able to capitalize on new information and make the next move because they have what we call the capacity to act. They have a willing mindset and are able to change when faced with new insights—and do it quickly and confidently. But while new information may provide the impetus for change, many organizations struggle with actually acting on it. Execution is a key skill for driving results, and as discussed in prior blogs, the last mile of AI programs is very hard to achieve.
Let’s appreciate though that employees do not act consistently with agility unless their organizations have a healthy corporate culture, and right at the core is trust. Trust is the agility lever; the more trust and collaboration dynamics at work, organizations simply are more agile.
With many leaders being apprehensive about AI simply due to their lack of skills, experience, and knowledge, the opportunity for AI to grow strong agile roots remains a problematic dynamic in many mid to large enterprises.
Trust is at the center of any healthy corporate culture and it is imperative to cultivate AI practices and methods to flourish.
Unfortunately, many machine learning and algorithms that result from using AI are inherently difficult to understand, and humans don’t trust what they can not understand. Most leaders recognize this challenge. In a survey by PwC, 67% of CEOs say AI and automation will affect trust levels in the future.
Dale Carnegie also did recent research that surveyed more than 3,500 full-time employees across eleven countries on their attitudes about artificial intelligence in the workplace, and their research found that only 30% strongly agreed that their organization has the capacity to act in response to new information.
In summary, the imperative is to design and build stronger digital literacy organizations and AI is an absolute game-changer. Hence, board directors and CEOs must feel a sense of urgency and be more agile to learn the new language of AI.
Three key questions that board directors or CEOs could ask in their respective board meetings relating to agility are:
- In regards to the company’s core values, how is agility integrated into the core value statements? If not, why? Is building agility capacity a focused skill development area and is it adequately funded? How is it being measured?
- Is the company measuring trust and investing in trust-building leadership skills for all employees?
- What percentage of AI projects that have been funded have been successfully operationalized? Of those that were not operationalized why, and was lack of trust or speed to proven results factors impacting execution success?
Board directors and CEOs simply cannot lead in the new digital world, without ensuring their company’s are building healthy corporate cultures where trust, transparency, and agility skills are being developed and improved upon. Only then will new AI use cases have a chance to achieve successful outcomes and be sustainable.
An AI journey is not just about data, or data science methods.
Successful AI is equally about ensuring the foundations for successful collaboration are in place, and without trust, competency maturity exhibited by board directors, CEOs, and executive leadership teams, organizations will continue to have a great deal of difficulty building mature AI centers of excellence.
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.