We need to be very clear and strict with ourselves to make sure that the work we're doing in artificial intelligence is aligned, embedded with a business strategy as well as an execution plan for positive transformation within the business. Which means that we're thinking not only about the artificial intelligence but the change impact on the business process, the employees, and the surrounding and enabling technology systems. We're really reshaping individual and organizational mindsets about the role of AI in our business. And what this really means is that we're redrawing the line between what the computer or the machine can do best and what people can do best. And when you redraw that line you have to recalibrate your processes. You need to recalibrate how you educate and motivate your workforce and you need to recalibrate how your technology systems are supporting the business and supporting your customers.
So what we're really trying to accomplish with artificial intelligence are business outcomes, and the way that we get that is by thinking about where we draw the line between what the computer can do best and what people can do best. When you do that you really start to harmonize the people and the computer for the best possible outcomes for the business. The alignment of strategic imperatives, Data & AI capabilities, and employee motivation is what's helping to create a Data & AI value.
Data & AI-ready culture
Ultimately, the companies that are the most successful with Data & AI focus on use cases that not only aligned to the strategic goals of the company but they also match the technical solution to the skills and capabilities that fit within the maturity level of their own organization. Following strategic clarity, another core lesson is around cultivating the right cultural mindset and how essential that is to driving success with Data & AI. Companies who reported gaining the most value from Data & AI in their organizations have prioritized communication, innovation, employee autonomy, and training to a much greater extent than companies who are struggling to extract value from their Data & AI projects.
There are four key aspects to instilling what I call a Data & AI-ready culture:
- First, is organizations need to be data driven, meaning they need to be able to reason over the entire data estate no matter where the data resides. And they have to have the ability to access that data in real time across the organization so they can make decisions at unprecedented scale and speed.
- Secondly, it's about enabling and including all employees to participate, to contribute ideas, ask questions, so every layer of the organization is represented. Fostering an inclusive approach like this leads to more diverse ideas, better solutions, and more motivated employees.
- Third, leaders have an obligation around implementing Data & AI responsibly and creating a responsible approach to Data & AI that addresses the ethical challenges that Data & AI presents.
- And last, it's so important that this mindset flows through every level of the organization including leadership at the uppermost levels because it needs to be ingrained into the fabric of how decisions are made and how employees are managed and rewarded.
It is this marriage of cultural transformation with the adoption of technology that has a true force multiplier effect. I realize that it was almost more important to realize the culture change you needed in the organization than it was to realize the business problems you were trying to solve, because really what we needed was a motivated workforce. Not just our leadership team or direct reports but every layer of the team needed to feel like they were going to be incented and be valued by their adoption of the technology, by the outputs that allowed them to have. Then we could let each of the teams be empowered to pick their goals. As with culture, responsibility is core for successful Data & AI adoption.
Data & AI innovation must be built in a way that earns trust. Responsible innovation starts by establishing principles that reflect your intentions, your values, your goals. In effect the culture of your organization. And to make these principles real, they need to translate and evolve into practices that enable the management of the entire Data & AI lifecycle at every step of development along with a governance model that operationalizes responsible AI across all organizational functions.
In part 3 and last part of this blogserie we will be discussing the Data & AI Chasm. Stay tuned and learn more!
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