Acc to a 2017 study, even though 81% of IT leaders are currently investing in or planning to invest in AI, only 39% of all companies have an AI strategy in place. This clearly depicts that there is a major confusion amongst the executive circle on how they can take the first step towards the enterprise AI strategy. In order to develop practical AI solutions executives must have profound understanding of enterprise AI and ability to craft its adoption strategy. Here, in this article we suggest some tips that might help C-suite executives in enterprise AI adoption strategy:
1. Understanding the AI landscape:
The first step for CXOs or senior level decision makers is to get themselves equipped with the latest knowledge about available technologies, vendors and AI jargon.
2. Discovering “right” business use cases:
While AI is on the lips of almost every C-suite executive and board member, most of them are still unsure of identifying and analysing the right business use cases that matter the most and can potentially be solved with AI. Also, the technology’s nascence in business settings makes it less clear how to profitably employ it. This essentially needs deep knowledge and research on current AI trends, solutions and heavy brainstorming in board meetings to find the correct AI technologies which have some actual business value to the company.
3. Industry and competitor analysis:
CXOs need to deep dive into industry and competitive best practices and benchmarks to thoroughly analyse what is currently possible, what will be possible, what and how different competitors and industries are doing with AI technologies . This would give them a fair idea of success rates and failed routes and help to embrace AI technologies in the right way.
4. In-house development vs choosing a data science vendor:
Image Source: Gartner
Since most of AI expertise is already hired by technological giants, most companies today lack in-house expertise to create, deploy and manage AI technologies within the organization. In such cases, there is a strong growth potential for organisations to begin with pilot AI programs through a combination of buy, build and outsource efforts.
5. Start small:
Instead of falling into the trap of primarily seeking hard outcomes CXOs need to look for low-hanging fruit and start with a proof of concept project to build strategic understanding and successes. In general, it’s best to start AI projects with a small scope, aim for ‘soft’ outcomes, such as process improvements, customer satisfaction or financial benchmarking and trumpet early success. Such quick wins help bring confidence to technological change and recruit grassroots support that ultimately determine whether an organization can apply AI effectively. Finally, evaluate the results in the light of clearly identified criteria for success.
6. Augment human resources, not replace them:
AI can bring big technological advances in an organisation which eventually gets associated with reduction in staff head count. From business perspective this cost reduction looks lucrative but it creates resistance from those whose jobs appear to be at risk. So, in order to be more productive organisations need to enable employees to pursue higher-value activities letting AI-powered decision support enhance and elevate the work they do very day.
7. Plan for knowledge transfer:
Since most organisations lack the expertise and internal skills in data science, they are not well-prepared for AI implementation. Hence, they plan to rely to a high degree on external providers to fill the gap. While this is perfectly fine in the short term, it’s not an ideal long-term solution. Therefore, ensure that early AI projects help transfer knowledge from external experts to your employees, and build up your organisation’s in-house capabilities before moving on to large-scale projects.
8. Choose transparent AI solutions:
While involving external data science vendors for AI projects, getting the right answer is not the only concern. It’s is also important that CXOs should for some insight into how decisions are reached and make this a part of any service agreement. This helps them to develop the reasoning when the solution is effective and offer insights when it’s not. For models where possibility to explain all the details is limited, executives should ask external vendors to at least offer some kind of visualization of the potential choices.