Part 1 – Transformational Crossroads

As we wrap up 2024, we know that Artificial Intelligence (AI) is no longer an emerging technology. We have used AI for years: Apple’s Siri, Google’s Navigator, Meta’s Advertising system, and more. There are many ways AI is utilized: classification, search, ranking, product personalization, and pattern recognition, to list a few.

Over the last few years, Gen AI, with the introduction of platforms like Chat GPT, has evolved exponentially, leveraging past advancements in machine learning, deep learning and language mastery. Today’s AI explosion relies on large language models (LLM), Natural Language Processing (NLP), and Machine Learning (ML), and it is this advancement that is reshaping the workplace.

Advancements in computational power, the ability to process massive amounts of data, and the lower costs of training AI models have contributed to the explosion of Gen AI and other AI predictive, communication, and creative tools. Companies are also pouring in hundreds of billions of dollars to win the AI race, which has accelerated progress.

Leaders and technology professionals are now tasked with navigating this evolving landscape while shaping how AI integrates into their organizations. In this series, I provide a few necessary steps to consider on your AI journey.

AI Duality

It’s no secret that the C-suite consistently seeks opportunities to streamline operations, enhance decision-making, and drive innovation. Along with those advancements, traditional workflows are disrupted, risks increase, and each innovation demands an ethical purview. Technology professionals must wrestle with this duality of benefits vs risk.

Amplifying Efficiency through Automation:

From service management to driving predictive insights for the finance department, workplaces find that automated solutions are changing how people work, workforce numbers and the skills companies focus on. The caution here is not to drive efficiency over effectiveness. Efficiency might look great on a spreadsheet but may be disastrous to customer experience and productivity.

Job Displacement and the Skills Gap

A new workplace reality has emerged from the impact. Job losses due to technology are nothing new; we no longer have pin-boys resetting bowling pins or elevator operators. More recently, data entry clerks, bank tellers, and travel agents have taken a significant hit in job opportunities. These changes are valid for most important technological advancements, and jobs are being displaced. The goal is to look at these shifts in jobs and roles strategically, not paying heed to the hype or the catastrophizing where the predictions of the end of the world of work gain traction because fear sells. Are we entering a world without work? No, not according to the World Economic Forum.

Companies that focus on skills assessment and development using a skills and competency framework such as SFIA (Skills Framework for the Information Age) to identify, develop and nurture skills for the digital world will fit into a global standard and assist employees in building their strategy.

Employees who approach their future with a realistic view of how their jobs can be automated or displaced must take steps to shape their careers. They must realize that company roles will be reinvented and adjusted as advising, creative endeavours, coding, automating, and security put many jobs at risk of being absorbed by AI solutions.

Ethical Implications of Bias, Privacy, and Decision Transparency.

Everyone has a role in critically evaluating AI outputs. According to the University of Saskatchewan, companies must hold AI developers and organizations accountable and focus on transparency of their algorithms, data sources, and potential bias. Committing to periodic audits and diversity in training datasets will become increasingly critical as safety, privacy, and security issues emerge out of unintended consequences.

Wise Use of Power

Balancing the benefits and risks of using AI solutions requires commitment and care to avoid negative consequences. Shaping your organization’s skill development, focusing on upskilling and retraining employees lessens the costly churn of talent in and out your door. On the personal side, build your career plans around filling the anticipated skills gap and don’t wait for your company to do that for you.

The ethical implications of privacy, skewed outputs, bias, and transparency of data collection in the use of Gen AI and AI solutions are complex and, when managed poorly, will lead to unintended consequences. Decision errors, ethical breaches and diminished oversight result from an over-reliance on technology and ignoring the balance of automation with accountability. Sustainable growth demands the wise use of power to mitigate organizational risk.

Continued in Part 2 – Organizational Agility

 

Part 2 – Organizational Agility

Continued from Part 1 – Transformation Crosswords

To leverage AI in various forms, the leading companies have dynamic environments, anticipate market changes, adapt fast, and deliver personalized solutions. Organizations need digital leaders who support agility, can leverage collective intelligence (human with non-human), know how to motivate in hybrid environments and succeed at leading human creativity alongside machine precision.

Digital Leadership

In Part 1, we mentioned digital leadership; let’s discuss that. Digital leadership involves the approach, actions, and attitudes necessary for leading competent, high-performance teams in today’s dynamic digital business environments.

A dynamic organization’s leaders curate a culture that focuses on three core areas: agility, data-informed insights, and customization at scale.

Agility: Merging human intuition with AI capabilities

Relying on technology without considering causal understanding and good judgement that comes with experience, emotional intelligence, and human creativity has consequences. To be agile, merging humans and machines into a collective, collaborative intelligence is the sweet spot.

Leaders must know that their handy conversational bots may drive away consumers who do not feel they are getting the immediate attention a human used to give. Being agile means being flexible in all approaches and focusing on the goal, not the technology – what are you trying to achieve, and does your solution benefit from the merge of human and non-human intelligence?

Data-Informed Insights: Intelligent data, industry knowledge and experience.

I rarely use the words data-driven. Poorly collected and categorized data is limiting and comes at the expense of industry knowledge and causal understanding (the relationship between more than one event and the practical results). We want our decisions to be well-informed from carefully curated data. However, depending on data alone and not paying attention to nuance and context means you are failing to consider the decision from both quantitative metrics and qualitative insights. The organization needs digital leaders who understand collective intelligence’s critical importance and how it will lead to better decision-making.

Customization at Scale: Personalizing employee and customer experience.

By analyzing vast amounts of data, businesses can deliver highly personalized interactions across the employee and customer journeys. The customer experience (CX) in the age of AI is constantly improving as the LLM and AI models improve. Genysis reports that Gen AI and other AI solutions are redefining how consumers and business leaders view CX. They highlight that out of the 1000 CX leaders surveyed:

  • 83% believe AI will be a clear differentiator for them in the future
  • 59% expect that adopting AI in CX will lead to increased customer loyalty and lifetime value
  • 70% report that AI is helping their journeys feel more empathetic to the customer
  • 69% say their organization has a plan for ethical AI deployments

Hyper-personalized experiences and customer journey mapping across channels require precise data collection. The data collected must follow privacy regulations, but the benefits are significant for organizations wanting to keep their customers loyal. The high-end boutique hotel industry has known this for years; now, the capability is democratized for businesses large and small, and customer-centric software platforms have incorporated these AI capabilities in a fury over the last three years.

Agility, data-informed insights, and customization are changing the workplace by merging human and non-human intelligence into a collaborative and efficient environment. The goal is for digital leaders to understand how to achieve this by gaining the essentials of digital leadership designed to guide their high-performing teams.

To employ the best talent, leaders must be skilled at motivating, inspiring, and collaborating in hybrid and remote situations. Filling the skills gap and fighting for talent will demand it. We are still in the early stages of the AI revolution, and organizations must close the gaps in old-style management training and build on the essentials of agility and resilience in their leadership styles. Companies need digital leaders.

Continued in Part 3 – Building Resiliency

Part 3 – Building Resiliency

Continued from Part 2 – Organizational Agility

If AI’s positive impact is to outweigh the many challenges and complexities, workplaces must be resilient. Upskilling employees, deploying ethical frameworks, and building a culture of continuous learning are critical components of organizational resiliency.

Continuous Learning

Continuous learning closes the AI skills gap by ensuring the organization is poised to future-proof their hiring, promoting, and training efforts to maintain industry and historical knowledge, the tacit understanding often lost in high-churn layoffs designed to find up-to-skills talent.

  • Provide learning technology for upskilling and reskilling efforts.
  • Champion a skills framework like SFIA to support today’s talent and prepare for the future.

Ethical AI Governance

Although Part 1, Transformational Crossroads, mentioned Ethical AI Governance, let’s go slightly deeper. Ethical AI focuses on policies and practices that employ transparency beyond the regulatory and compliance requirements. You want to exceed expectations, not just meet audits. Ethics require looking past compliance into stewardship and citizenship. How can you leverage experience while also protecting the privacy of your employees and consumers? How can you test and retest to eliminate bias and falsifications within your AI models? These are organizational considerations as you forge forward in your AI journey. Pay close attention to your AI governance:

  • Consider ethical and legal boundaries
  • Document every step in the process
  • Build transparency in your AI model about the data it collects
  • Build in accountability with employee and customer protection
  • Practice fairness by avoiding unintended biases or discriminatory outcomes
  • Protect the privacy and security of user data and track the ever-changing data protection laws.

 Empowering Employees

Viewing Gen AI or AI automation as a cost-cutting effort to reduce labour is a critical error companies make. Focus first on what you are trying to achieve in your business. How will incorporating Gen AI or AI automation contribute to the business outcomes? Take a systems approach to your solutions that considers the human qualities brought to the table and how those qualities can work with your plans. Empower your employees by aligning the technology with organizational outcomes and company values.

Empowering employees is simple, but it takes intentional effort:

  • Allow employees to make decisions at their knowledge level, trusting that they understand their roles and work.
  • Set clear expectations and define the behaviours you will and will not tolerate
  • Prioritize and invest in your people’s professional development by supporting continuous learning. This will increase the initiative’s potential and boost retention and productivity, leading to resilience.
  • Recognize and reward employees’ achievements before moving on to the next exciting project. Appreciation goes a long way toward building resilience.

Understand Potential Risks

Banking, insurance, software platforms, energy, retail, communications, healthcare, and other industries are all shifting, and those organizations need digital leaders capable of managing the risk of an over-reliance on AI. Some of those risks are company risks that boards and executives must invest in, including output auditing and tracking measures with human oversight.

  • Decision errors
  • Ethical breaches
  • Privacy violations
  • Market volatility
  • Hallucinating Gen AI models (making stuff up)
  • Weaponizing automation
  • Biased programming
  • Hidden surveillance

Governance is critical when implementing any AI solution, especially one that involves large language models, public communication solutions, and massive amounts of data processing.

AI Governance is less about mitigating known risks and more about identifying the unknown risks.

We are still in the early stages of widespread AI adoption. During this transition stage of balancing between the hype and reality, it’s critical to ensure you have a Digital Transformation Framework in place for your organization, develop a governance framework around your AI initiative, employ digital leaders capable of leading high-performance teams, and invest in upskilling and training the people with organizational tacit knowledge to help your organization move at the speed of business.

Contact Patti if you want to learn more about the critical role of Digital Leadership in future-proofing your organization.

Tag/s:Business Transformation, Future of Work, Organizational Change, Personal Development, Readiness,