AI is presenting amazing new opportunities for organizations. But it also adds new layers of complication to planning, managing, and engaging workforces. Eric Shepherd suggests employee engagement centers can be the answer.
For organizations to stay competitive and relevant in the 4th industrial revolution, they will have to automate to catch up and get ahead. In the age of advanced analytics, data analysis, robotic process automation, IoT, and other technological advancements, AI (artificial intelligence) has now become a must-have tool. Leadership is critical to make the most of this opportunity to maximize its full potential. To achieve this, in-house capability building, and reskilling programs are the best ways to prepare the workforce.
There are of course real obstacles to adopting AI. Not the least of these are the changes to the organization’s thought process and work culture. Leaders, managers, sales teams, manufacturing teams and more all have to think in new ways. Instead the new watch words are interdisciplinary, data exploration, analytics and agile development if we want to deploy AI.
In the long run, organizations must transform to be successful. History shows us that this cannot be achieved with ad-hoc solutions. Hiring capable employees will give the newly formed AI practice a necessary boost. But for organizations to keep up, AI capabilities must be built across all practices encompassing all employees. And this is where training existing employees come in. Third-party training courses and knowledge bases may deliver an overview of the subject, but they cannot provide organization-specific learning experiences that allow for rapid scaling, lasting cultural changes, cross-functional collaboration, and agile development.
Adoption strategies
In-house training is one of the most powerful tools an organization can use to encourage this change. It enables you to build on repeatable, cohesive practices to allow employees to dive deep into the subject matter, learn from others, and achieve the organization’s objectives. Some organizations have incubated new styles of training centers to provide learning environments as part of their adoption strategies. We can think of these new training centers as ‘AI Employee Engagement Centers’ – an environment designed to provide forums for discussion, collaboration, learning, and training. The advantage of this approach is that it can be baked into the organization’s structure, work culture, and needs. Such training centers might soon become one of the core elements of an organization-wide AI transformation.
One of the primary responsibilities of these engagement centers is to bring AI to scale as rapidly as possible. This requires engaging and reskilling employees who are impacted by the transformation. As AI becomes widely accepted, a critical challenge will be to provide job security for employees whose tasks will be automated.
For an AI deployment to be successful, three critical building blocks must be developed. An AI Engagement Center can educate employees regarding AI and create the means to support these building blocks.
Shared Vision
All training and reskilling efforts must align with a shared vision, common objectives, protocol, and language. This enables everyone to be in sync when it comes to the core elements. It also helps everyone understand each other’s roles and responsibilities and utilize the same methodology while looking for and implementing a solution.
This alignment across the organization paves the path for a successful AI deployment. This process also facilitates retrospectives from previous AI deployments and curating this knowledge for the organization. This cross-fertilization of ideas and alignment gives leaders more information on the business, talent, and training needs, which enables them to deploy the right people into the correct positions.
AI Employee Engagement Centers
In-house employee engagement centers can customize content based on the organization’s objectives, starting position, industry context, cultural roadblocks, and skill gaps. Learning programs can be designed to align with the organization’s transformation road map. Engagement programs can lay out the required skills to achieve the transformation and provide the means to learn these skills.
Employees are provided with technical know-how to achieve what the leadership has envisioned. Engineers, data scientists, and analytics experts hone their skills via collaboration to work alongside other business teams. This allows stakeholders to focus on actual business problems and create value for the organization. Employees whose jobs become AI-driven learn how to make the transition from older practices to new AI-based work practices.
Hands-On Experience
AI Engagement Centers also provides a forum for employees to mix their classroom knowledge with real-time hands-on expertise. This allows them to transition from being a learner to a practitioner who can deliver capabilities and ultimately develop into an expert who can lead a function.
Factors to Consider to Build an AI Employee Engagement Center
AI Engagement Centers will differ in structure based on the organization’s vision, objective, and phase of AI transformation. While there are differences, the successful centers will have things in common:
Building relationships and improving the organizational design, not just learning new skills.
Synchronizing the academy’s activities with the strategic objectives of the organization.
Providing learning experiences for all stakeholders for the board room to the workroom.
Engaging employees in the benefits of digital and talent transformations in the context of their role.
To extract the best value from AI-driven analytics, some new practices need to be developed. Without them, the speed, scale, and depth of value derived from analytics are bound to suffer. Organizations that plan in terms of engagement rather than training tend to fair better.
Without coordination, the organization may not be able to identify the right mix of learning materials, group activities, and real-world problem-solving.
Transformation Centric
AI Engagement Centers must integrate their programs with the AI transformation road map of the organization. This will ensure the right people, skills, and talents are engaged to prioritize the critical activities for AI adoption.
Once the leaders are armed with an understanding of AI and the opportunities, they can drive better support for use cases aligned with their organization’s goals. Analytics teams can get a broader and more in-depth understanding of the business challenges that enables them to look at the scenario from a holistic perspective rather than a technical viewpoint. Leadership must understand AI concepts so they can lead efforts in the right direction.
It's important that all stakeholders are engaged: An effective engagement center focuses on educating the organization’s stakeholders irrespective of their designations, seniority, and role. AI Engagement Centers should plan to engage executives, managers, business analysts, translators, technical developers, business teams, and business functions.
Going Beyond the Technology
An AI Engagement Center is more than a technical training center. Training centers will surely cover AI foundational courses, and how to adapt to the technology changes. Employee engagement centers may also focus on the cultural and organizational changes needed to build the AI solution to scale. These centers help create a knowledge repository and introduce best practices that allow the re-engineering of critical use cases.
Here is some elements of a successful AI Employee Engagement Center:
Leaders learn how to drive value, restructure the organization, and ensure a shift towards a data-driven culture.
Developing best practices helping everyone identify opportunities, test the readiness of teams, implement the use cases, and codify the learnings.
Soft skills enable effective communication, which is critical for interdisciplinary teams. Business processes and challenges have to be explained to AI experts for them to define and prioritize the automation.
Interdisciplinary collaboration is key to implementing and creating automation necessary for success.
Effective change, program, and project management are essential for successful AI adoption.
Leaders learn to converse with each stakeholder to ease use case adoption.
Combining Training with Real-world Experience: Successful AI Engagement Centers combine classroom learning, e-learning, and workshops to brainstorm, define, and prioritize solutions. These centers can oversee the successful transition of a learner to an expert. In the classroom, learners might be provided with real-world business challenges. They are required to come up with business initiatives to combat these challenges. By learning in this way, trainees are more likely to adopt the solution that will create the highest business value.
Introducing currently active use cases and guiding how to solve them, and learners grow through problem-solving. Learning is taken to the next level via social and community engagement.
AI Engagement Center Keep Evolving
AI as a technology is still far away from realizing its full potential. AI-driven solutions are changing and evolving at rapid rates. As graduates handle real-time challenges, new talents are hired, and interdisciplinary collaboration improves, more and more knowledge is gathered within the organization. It is important to feed this knowledge back into the AI Engagement Center. Centers put into place the following:
Top performers from the first batch of learners become faculty. As learners mature and gain more experience, they can lead to learning sessions and workshops. Based on their experience working and teaching, these performers are given more and more responsibilities, eventually becoming senior facilitators.
AI Centers create a team of individuals to continually monitor and improve the curricula and activities based on real-world experiences and changes in AI technology.
Involving the C-Suite to visit. When executives visit, it creates unprecedented excitement and highlights the importance of learning.
As the implementation of AI becomes a necessity, capability building has emerged as the prime requirement. Many organizations have understood that merely building the technical platform, leveraging opportunities, and hiring data scientists is not a surefire way to success. Instead, upskilling and reskilling all stakeholders, starting from seasoned managers to fresh-faced end-users, is critical for bringing AI-driven automation to scale. AI Engagement Centers will not be static enters of training but dynamic organizations that will evolve with the change in technology, use cases, organization culture, and feedback from real work experiences that will be the cornerstone for AI implementation success.
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