From the Data and AI Expert Labs: Merge hard and soft skills to drive outcomes
IBM’s Data and AI Expert Labs help guide organizations on their AI journeys. Expert Labs consultants represent years of data and AI expertise and offer best practices and business strategies to meet each organization’s unique needs.
Previously in this series, I discussed the formula we use at the Expert Labs (Outcome = Technology + Skills + Methodology), and how you can use it to focus on what matters. In this final part of our series, I’ll explain how the skills component works with technology as agile and robust as AI.
Achieving success with AI is a broad initiative that will stretch across your entire organization, so the skills you need to rely on aren’t just about data science. Organizations need to be flexible about changing the role of the people who manage their technology.
You may think that the onus of driving AI projects lands squarely on your data scientists’ shoulders. But the truth is that every person in your organization will need to know how AI will affect your business and how their skills can assist in the effort. AI is excellent at breaking down the silos that exist today between functional areas of your organization, be it between line-of-business workers and IT, or between specialists and application developers. To see real success in your organization, you must understand how AI will impact all aspects.
Wherever you’re applying AI and whatever industry you’re serving, there is indeed a shortage of people with the right technical knowledge, industry domain expertise, and skills to develop AI. One of the strengths of AI and Watson, in particular, is that you’ll want your specialist employees to train the application once development is complete. As employees become accustomed to these new methods, it accelerates adoption within your organization. As your employees work more with AI, they will become more comfortable with its processes and develop a strong buy-in because they’ve had a hand in its creation. It’s not just for data scientists because it’s straightforward to train, especially for your line of business employees. As your employees become comfortable, they’re likely to take on upskilled positions. For example, an entry-level customer support agent can easily transition into a knowledge worker who can directly help customers solve cognitively tricky or sensitive customer issues.
This linking of AI technology with business skills demands that multidisciplinary teams consisting of IT, business, operations, and conversational design bring their expertise into this new space. Those with technical backgrounds like data science and data ops help to drive machine learning and AI’s enduring value. But being able to bridge the gap between hard and soft skills will bolster your workforce and set your organization up to be future-proof.
Here at the Data and AI Expert Labs, our advice is always to start small and start with the data you have. We’re ready to help you operationalize solutions of varying complexities and provide your organization with the tools you’ll need to scale and accelerate the value of both your AI solution and your in-house skills.
In the next series of blogs, my colleague Toby Capello will explore the different reasons organizations seek out AI adoption and how to best prepare your systems and teammates for this technology’s transformative nature.