Retooling Talent- Gaining a Competitive Edge with AI
By Nora Osman, believer in people as the key to innovation and disruption.
I’ve always believed that people are an organization’s most valuable assets. Especially now, with the transformative tide of AI and digital technologies, the true competitive edge lies in an organization's ability to retool its most critical asset—its people. It seems organizations have been under an equal amount of pressure to find ways to gain competitive advantage with AI. Hence, they need to be ready, not just with the technology needed to innovate, but also with the people and the processes that will enable that transformation.
Over the past 18 months, no topic has gained more attention than AI, and specifically generative AI with numerous panels at conferences focused on the theme of preparing the workforce for the changes due to AI, including retooling the talent. As I became more curious, I started to arrange follow up meetings post-conference, with various senior leaders across different verticals, asking them more probing questions. I was looking to gain insights that I could share in a presentation with a broader audience, to assist in perspective gathering as well as access to tangible action items leaders could capitalize on out of this presentation.
Before starting to discuss AI and the need to transform, let’s unpack the term “digital transformation” and the why. According to the Institute for Digital Transformation, the definition “Digital Transformation enables organizations to drive outcomes that align with the organization’s purpose…(digital transformation) should lead to metamorphic change among an organization’s products, services, systems, operations, and culture. The breadth and depth of that transformation is amplified by the innovative capabilities of digital technologies, but it is not digital capabilities alone that drive the transformative result.” The simplest way to say this is to find creative ways to make them better, faster, and cheaper. Since knowledge (or intelligence) is power, AI becomes a form of superpower as it helps efficiently and effectively deliver that formula.
Next, let’s look at some of the reasons why companies are being urged to focus on talent as a significant part of that formula. According to McKinsey (Jobs report) “As many as 375 million workers—or roughly 14 percent of the global workforce—may need to switch occupational categories as digitization, automation, and advances in artificial intelligence disrupt the world of work. ” If you haven’t read this report or other industry analysts’ prediction, just watch this short McKinsey video for this insight. McKinsey's 2030 prediction video. The urge to digitize and transform began decades ago, however it has been emphasized more so post-pandemic, as organizations pivoted away from crisis mode in 2020/21, to now more of a “normal” business cycle, looking to drive revenue and profits to their ideal levels.
Fundamentally, my findings from 4 months of extensive research highlighted a few themes, and these were relatively consistent across most industry verticals as well as leadership tiers.
Encouraging a people-first mentality- to uplift individuals and internalize AI capabilities.
Leaders shared this mindset of cherishing the internal talent they had and wanting to make them part of the solution, not part of the problem. They were all approaching this from the angle of how do we leverage AI to help our talent in different ways and not from the approach of we need to simply hire talent with AI experience and reduce/replace existing headcount.
Emphasis on learning “ability” versus current skills.
Another repeat question was “what skills are you hiring for now in technology, and are they different than before?” While for some time now we’ve been hearing of the large demand for data scientists, programmers, and analysts, what became apparent was not the inherent skill a person has, but what they are willing to learn. How “trainable” a person is was deemed more important by these organizations than what they currently know. Considering the massive demand for talent on the AI platform, there’s certainly a huge gap in supply. Organizations are aware that finding this talent is going to be difficult and expensive. The. alternative is to cultivate that experience by training and growing the talent internally. With that comes the institutional knowledge benefit and the increased engagement of employees as they take on more exciting tasks and roles.
Employees focus on “meaningful tasks” rather than busy work
Automating boring tasks should free them up to spend good buckets of time on things of higher importance. This includes getting into a state of “flow”, from which higher value output comes. Without busy work clogging up the calendar, projects and initiatives make progress at higher rates, and teams start to feel momentum building. The feeling of not just knowing your own and your team’s purpose but feeling like the work you do really matters and you’re making steady progress, is perhaps the biggest catalyst for sustaining momentum.
The gain of an hour a day in productivity through AI allows more “human time.”
Undoubtedly there’s a major drive towards using AI initially to improve employee productivity, namely their efficiency. Looking to employees to take charge of automating tedious and mundane tasks has consistently been seen as an approach leaders are taking to get their talent excited about AI. Microsoft’s Co-pilot, for example, allows for the capture and transcription of meeting content into summaries, shaving off valuable minutes/hours that otherwise regularly got allocated to that task. Other tasks such as event/meeting scheduling, data gathering, content editing/revision are all examples of administrative tasks that can now be expunged from employees’ workday. The hope is that with at least one hour’s worth of time back, employees would now engage more actively with each other, collaborating and innovating.
Approaches to Talent Development
One of the most fascinating approaches I learned about came from Florin Rotin, Chief AI Officer at Avanade. Florin shared that at his organization, they purposely developed a training program that was made available to all 65,000 employees in the organization at all levels, including the C-suite. He shared that his approach was to see AI as a way to help empower people to be the best versions of themselves, to embrace their career, and fulfill their aspirations. Their approach at Avenade is instead of focusing on productivity improvement, automation, and cost reduction, they focus on having people more included in the AI journey, so that they embrace AI rather than fearing it.
Florin shared that he “Now thinks about AI being so more about people and organizational readiness than about the technology.” The training program’s focus incorporated key elements such as prompt engineering and even responsible AI, and according to Florin, 85% of those who underwent the training reported being ecstatic for receiving it and more excited about learning and continuing to grow with the company. When addressing the interview/training question, he confided that there was a time when they used to be obsessed with data scientists, s/w engineers, and cloud architects, etc., however they are realizing that the nature of those jobs are changing, and now they can have less experienced engineers with a superpower (copilot) helping them. He stressed the importance of it becoming more and more about the ability to learn, versus the skills you already have. The mindset they look for is curiosity, the combination of soft skills and tech skills is now key.
Several other approaches I encountered included carving out segments of a day or a full day within a week/month to solving a problem or need with AI. In some organizations, unshackling workers from normal assignments and allowing them to work on an area of unique interest to them using AI ignites the excitement on many teams. They start to experiment with technology, without the pressure of assignment, learning new skills and solving for procedural and technical pet peeves. Along with the self-satisfaction and confidence that employees gain with the skill acquired, leadership in those organizations also make a point to acknowledge the effort, the trials, the success (and even failures) of these efforts. Cultivating that culture of experimentation and of innovation is absolutely imperative to nurturing growth in AI and technical transformation.
Training Opportunities.
While we are still early in the Generative AI learning curve, significant progress has been made in course development in many universities, and most if not all are actively augmenting their MBA and IT specialty curriculum with courseware to support the growth in demand for AI. In my research, I came across some courseware that is available free of charge, for different levels. Here are 10 to get started with.
Beginner Level:
1) Introduction to Generative AI (45 minutes)
Understand the fundamentals of generative AI and its use cases, and gain practical experience of developing generative AI apps.
2) Prompt Design in Vertex AI (5 hours 15 minutes)
Craft powerful prompts, learn multimodal generative techniques, and apply Gemini models to real-world marketing cases
3) Introduction to Duet AI in Google Workspace (30 minutes)
Learn to use Duet AI to streamline your Google Workspace and become more productive
4) Responsible AI: Applying AI Principles with Google Cloud (1 hour 30 minutes)
Learn best practices to responsibly integrate AI into your business operations
Intermediate Level:
5) Conversational AI on Vertex AI and Dialogflow CX (5 hours)
Build, deploy and manage virtual agents to engage with customers and resolve errors.
6) Attention Mechanism (30 minutes)
Understand the basics of attention mechanism and learn to use it to improve standard ML tasks, such as machine translation, text summarization, and answering questions.
7) Create Image Captioning Models (30 minutes)
Use deep learning to create your own image captioning model, train it, and evaluate output.
8) Encoder-Decoder Architecture (30 minutes)
Learn the fundamentals of encoder-decoder architecture and to use it to train models to perform sequence-to-sequence tasks.
Advanced Level:
9) ML Pipelines (13 hrs 15 minutes)
Learn from trainers and engineers who are directly involved in the development of ML pipelines at Google Cloud.
10) Google Cloud Solutions II: Data and Machine Learning (4 hours)
Learn to use Google Cloud to run big data operations and analytics, using the ML practices of Google’s Solutions Architecture team.
Finally, to summarize the approach to retooling talent thus far.
There are varying definitions for digital transformation, in fact if you ask a 100 people, you’ll likely get a 100 different answers. However, what is clear is that people are the most integral to digital transformation, in fact only they can drive it. Despite the misconception of its newness, AI itself has been around for decades, and the hype is about Generative AI. Many are eagerly claiming disruption on the AI forefront, though AI tools & resources are in early development still. That means no one has a monopoly on AI, everyone is learning, experimenting, failing, and sometimes succeeding. The biggest theme that’s evident is that gaining competitive advantage via AI will manifest when leaders prioritize talent development over elimination. The human minds are irreplaceable around ingenuity, so they are uniquely positioned to guide innovation and disruption. From the many voices heard on this topic, the advice is to engage your people in the AI/transformation journey as they will be pivotal in driving the change. You should invest in people, even more than in the technology- and that is where the highest dividends will lie. Lastly, and this is a favorite of mine, only humans can truly understand and influence the “customer experience” directly. It’s best to “automate the mundane and elevate the humane.”
