How pioneers achieve AI outcomes despite skills gaps: A Forrester Research leadership conversation with Bell Canada
Adopting AI is all about organizational change. That was the message conveyed in this Forrester-led conversation of women AI leaders, who discussed how their organizations have achieved business-critical AI outcomes in the face of known skill gaps.
Moderated by Srividya Sridharan, a vice president of Research at Forrester, the panel featured Mara Reiff, Vice President, Strategy and Business Intelligence, Bell Canada, and Ritika Gunnar, IBM’s Vice President, Expert Labs.
Watch a 0:50 summary above of a leadership conversation with Forrester Research and Bell Canada, and watch a video of the full conversation (22:20) here.
At IBM, we know from experience and work with our clients that talent shortages are a significant barrier to adopting and infusing AI across the business. Focusing solely on tech talent and hiring data scientists isn’t enough to drive successful outcomes for AI. Business stakeholders, product management, and all other contributors need to develop an expanded language and way of working to make it happen. Driving change at the enterprise level requires some unique strategies and is really the only way to infuse AI into the business.
Panelist Mara Reiff described it perfectly:
“AI is continually evolving and changing with the life cycle of the business. It needs monitoring and constant feeding and you need to build the support for that.”
This means that AI requires that the whole organization experience some change, not only the technical teams and contributors.
In the panel, Ritika Gunnar discussed three major strategies for adopting AI and organizational change: Go from the bottom up and top down at the same time; address the talent scarcity; and finally, start small and then build out and up. Each area has a set of tactics necessary for every business serious about taking on AI.
Oftentimes, organizational change is a top down approach. Executive leaders identify where adjustments need to be made –including ways of working, business strategies, and products and services. When it comes to AI, however, a top-down-only method doesn’t work. There has to be a bottom up approach at the same time to drive change across the organization. Being practical and pragmatic is necessary because support teams make AI innovation happen. Having champions across the business and in each line of business is necessary. There needs to be support for experimentation, leveraging agile methodologies and lots of communication about and with data across the business.
Considering what talent you bring into AI projects tends to be top of mind for leaders and requires a multi-pronged approach, including new hires and upskilling current employees. Many universities across the country teach data science and AI in the classroom, so targeting new graduates can be a smart strategy, alongside competing for experienced AI and data science talent working at other tech companies.
Upskilling current employees should not be overlooked. Why? Because they already have affinity for customers and knowledge of the business – so providing learning experiences to help employees develop skills is low-hanging fruit.
By driving a culture of learning and providing learning experiences you can focus on how AI helps current team members leverage their skills and knowledge of the business in new strategic ways. Ultimately, AI that is infused into the business will require everyone to learn the domain of data and how to communicate it –and continue to learn on a regular basis to remain tuned to the corporate direction.
Taking on AI projects can be intimidating, but like eating an elephant, one bite at a time is a smart strategy. Identifying and prioritizing business challenges, targeting current teams and talent on those challenges, and developing good practices to share will help ensure early wins and methods that will work in your company.
At IBM, our Centers of Excellence (COEs) are absolutely critical to infusing AI across the business environment. These COEs bring the best talent available to solve problems, modernize architecture and curate data collection and management processes that can expand to other organizations. COEs embrace experimentation and help develop institutional knowledge that can be leveraged as other organizations and teams adopt AI.
At IBM, we’ve learned mastered the knowledge and skills that help clients achieve AI outcomes. Our AI Leadership School enables client leaders develop strategies and skills to lead their organizations to successful adoption of AI.
Our Data and AI Expert Labs teams help accelerate the adoption process by working alongside client tech teams while offering meaningful skill development experiences along the way. The journey to AI is truly an organizational change process –achieved through learning that involves everyone.
Learn how leaders are overcoming the challenges of data, talent and trust in this Forrester study, Overcome Obstacles To Get To AI At Scale.