By: Sid Bhatia, Vice President and General Manager, Middle East and Turkey, Dataiku
The region’s artificial intelligence industry is heating up. Not only has the United Arab Emirates already appointed a Minister of State for Artificial Intelligence – and has become the first country to do so – but other nations are now making AI history. Smart city projects are underway and PwC estimates the Middle East as a whole could be home to a US$320 billion AI market by the end of the decade.
Numbers like these are signs that AI has been accepted. The need to compete, following the region’s duel with the COVID pandemic, has resulted in many more project launches. But are the right moves being made? To what extent do companies succeed or fail? The UAE’s attention to state-level AI is an indicator of the promise of some technologies to provide some kind of creator economy, but innovators should be wary of throwing the technology on challenges and to expect her to stay.
Here, we look at a sample of best practices that can support the successful adoption of AI. We will see how AI is successful “everyday AI” and that a culture must be created to support the integration and exploitation of technologies that can be so easily misunderstood and misapplied.
The short term: high impact and return on investment
For those with high expectations and little understanding of the underlying technology, AI may disappoint. So, IT leaders hoping to create an AI culture must combine managing expectations with delivering a series of quick wins that excite, or at least intrigue, business leaders. Computers don’t have years to do it. In the few months since declaring that the company is now on its way to AI, they have to convince critics, turning distrust into acceptance and skepticism into belief.
Quick wins come from selecting a small sample of use cases that can be high impact but are relatively simple to implement. This can be tricky if an organization is late in the AI game, as many easy-to-implement use cases won’t have an impact if they’ve already become the norm in the AI world. industry. It is therefore essential to quantify, to the extent possible, the level of impact that an implementation will have, in order to manage expectations.
If you succeed quickly, you will have the opportunity to build on these solutions and reuse them across business functions, creating business impact and winning hearts and minds. If done well, the company’s early adopters become AI ambassadors, sharing their successes and encouraging others.
The long term: transformation at scale
Once the culture is established, IT leaders can look to the future, identifying other opportunities for business impact. When we talk about scaling with AI, we mean AI projects need to sync with business strategy. Companies that reach this stage have left experimentation behind. The company has already adapted to the presence of AI technologies and AI has proven to be a reliable tool for making an impact.
Now is the time for AI to be harnessed as a driver of long-term value. To do this, people and data must come together to ensure that everyone in the hierarchy can add value at all times using the everyday AI they have come to know and trust.
Robust AI governance will be important when an organization reaches this level of maturity. This does not just extend to its data, which must be properly catalogued, in terms of responsible parties and what they are allowed to do with it. Security, compliance, data quality, data architecture, and metadata management are all important, but data science, machine learning, and AI present a range of use cases that may not not be covered by existing data governance standards. AI governance should cover responsible AI, which is as much about algorithms and their results (and actions emanating from those results) as it is about the data itself.
Successful AI implementation will involve finding ways to make AI governance and agile AI culture co-exist on a day-to-day basis. And part of the culture change will be the adoption of new methodologies that inject governance and politics into the development process. MLOps, for example, is gaining traction because of its ability to reduce risk and oil the wheels of implementation.
learn and grow
Of course, none of this happens overnight. Users must learn, developers must learn, managers must learn. Everyday AI requires a lot of information from an organization at all levels of its operations. Training should be as thoughtful as culture building. Each employee must learn what they need and no more, to avoid wasting time and resources. Courses should be personalized and staggered and include instruction on governance standards, culture, and soft and technical skills. Non-technical staff should emerge ready to design data projects and even implement them.
Few successes happen by chance. They usually require shrewd planning and a clear vision of the goal. By breaking down the journey to everyday AI into short- and long-term phases, stakeholders can focus on what AI can do for them today rather than worrying about unrealistic ambitions that have little chance of success.
The right use cases at the right time can put a business on the right path. The right story in the right ear can also be a boon. Applying this approach as a roadmap means not wasting time and resources trying to overcome the realities to achieve the impractical. Patience and strategy will win.