Accelerate AI Transformation with Simulations: 5 Key Advantages to Consider
Experiential learning has been around for a long time, used in various scenarios: learning to ride a bike, drive a car, or speak another language. Without practical application, however, the learning would be incomplete. Take, for example, military training: picture 30 military recruits, covered head to toe in mud, running from one challenge to the next. These exercises, aimed at demonstrating the importance of teamwork, communication, navigation, etc. would hardly have the same resonance if they were delivered in a classroom setting, on a Zoom video call, or through a PowerPoint presentation. Building an experience to demonstrate the objective is more valuable and more memorable than simply disseminating information.
Experience design is nothing new, yet it’s only recently that its value has been explored in the realm of corporate training and AI adoption. While training and development is a critical factor in corporate strategy and investments, there is a growing realization - supported by data - to show that traditional training is not getting the necessary results.
Learning is experience: everything else is just information.
Traditional training isn’t working
The challenge that companies face, in improving how effective and impactful training and development initiatives are, is figuring out how to design experiences that inspire teams towards action with AI and that surface real team dynamics. They must also manage the fact that many teams are geographically dispersed, adding the necessity for virtual access to experiential training.
Unlike traditional learning experiences, which are content-centric, experiential learning is context-centric. Instead of focusing on the information that is to be passed from one person to another (the content), experiential learning focuses on how that information is transmitted, with a goal of making it more relevant, more meaningful, and more memorable. The question that drives the training moves from ‘What do we want them to learn?’ to ‘What do we want them to be able to do with AI?’
Experiential learning is largely structured around simulations. But a simulation that doesn’t challenge the learner - where everything goes to plan, and the learners glide along with the experience - erodes the value of an engaging, active learning experience. To have value, a simulation must provide a space for things to go wrong. In designing a simulation, we have to design an experience where:
- Communication channels break down
- Clarity is lost
- Data and information silos exist
- Language gaps exist and alignment falls
- Teams make choices to overcome challenges
The hero of the story has to be the team working with AI. This is how you build an effective Team code to unlock value.
Industry insights
How are AI simulations different from traditional learning?
Passive vs. Active use of AI in teams
Traditional learning is a passive occurrence, where knowledge is transferred from trainer to learner in a largely one-way channel. Experiential learning, on the other hand, is an active experience by design requiring engagement, analysis, and active participation by the learner. Passive learning is ineffective in changing behaviors, adopting new technology, transferring knowledge to job functions - the primary goals of corporate training and development. When learners are actively challenged to use AI during training, they can surface real obstacles and identify the fastest path to performance on their own. Creating the answer themselves, rather than being told what the answer is, has multiple benefits: it becomes memorable, valuable, and easier to apply to real-world situations.
Content vs. Context of AI adoption
Traditional learning programs start with a topic; for example coaching, communication, negotiation, leading innovation, and so on. Training, therefore, is tied to the delivery of content, and success is measured by whether or not the content has been delivered to the learners. This approach over looks the more important question: what is the goal of the training? Experiential learning is driven by context, not content. What is the individual, team, or organization trying to achieve with AI or how can AI help us deliver the business mission? Do employees need to improve a specific area, such as first-time project management, running meetings effectively, or applying AI to decision-making?
First, pinpoint the tasks that people are trying to achieve with AI and then run simulations designed around those tasks. This approach brings both challenges and behaviors to the surface for analysis and engages learners to devise different ways to find a faster path to performance with AI.
Demo vs. Practice using AI
Demos are good - especially when it is well-researched and supported by data. However, there is an assumption that if a demo is good, the technology will be adopted to change real-world behaviors. This assumption is flawed: a demo alone isn’t the answer to challenges that organizations and teams face in the business environment. Teams must practice using new applications and apply them to practical situations, and see for themselves how AI can be applied to real-life situations.
Translating AI capabilities to practice is about achieving a strong affective context or intrinsic motivation to change.
When learners are actively challenged during training, they can surface real obstacles and identify the fastest path to performance with AI on their own.
What are the benefits of Simulations in AI Transformation?
AI to support Adaptability and Resilience
AI to support Adaptability and Resilience
Our ability to adapt and respond to new situations has never been more important - and we believe that AI is going to support us to do this better. For teams, individuals, and businesses: the most successful groups are those that are agile in response to a highly competitive and rapidly-changing environment. To build this adaptive capacity in teams, and help them optimize agile responses to change, companies need to design experiences where individuals are tested and asked to adapt on the fly using AI.
Safe Space to Practice, Safe Space to Fail using AI
Safe Space to Practice, Safe Space to Fail using AI
Before military teams start to use a new bit of kit - from new boots to a new vehicle or communication system - they practice. Then practice again. And again. This allows military teams to fail in a safe environment to analyze performance and make corrections so that when it counts, the team is completely optimized to work with the new capability when it counts.
Bridge the Gap Between the Promise of AI and Results
Bridge the Gap Between the Promise of AI and Results
Even though one study after another has shown the value of AI in improving business results and productivity; and that AI-enabled teams generate better financial performance, the results of these studies are often not utilized by companies to improve their chances of success. There is a gap between theoretical knowledge and practical application.
Growing Revenue
Growing Revenue
Every day, sales leaders in Business to Business (B2B) face a multidimensional and fast-moving environment. They must navigate changing consumer trends, shopper behaviors along the path to purchase, omni-channel dynamics, competition for shelf space in a fast-moving category, and all of this can vary by geography and channel. The leader must integrate all of the retailer context and their priorities into a coherent story.
Improve Training ROI
Improve Training ROI
By simply communicating information, traditional corporate training is ineffective. It fails to impact behavior or improve information retention. Learners may go on a training event for 2 days, have a great time networking with colleagues(what they rank as the biggest benefit to the training), interact with great content, and then when they return to their desks, revert to old behaviors.
Tell me and I will forget, show me and I may remember; involve me and I will understand.
How can you apply Simulations to you AI Transformation?
To accelerate you path to AI performance, training initiatives must advance beyond the traditional. Instead of sharing theories and presentations about AI to a passive audience, training must actively involve learners and build challenging experiences for our teams.
In overcoming challenges with AI together and improving results in a safe environment, the team will not only improve the ways they work together, but they will also change behaviors to improve real-world results with applied learning. Experiential learning uses simulations to set a performance challenge to teams that asks them to align themselves to a shared goal, formulate effective questions, and use AI and make better decisions virtually. In the simulation, they will experience first-hand where they can improve and draw a clear picture of what they need to do to win in this new AI enabled world.