CIOs and other C-Level executives are under tremendous pressure to adopt and scale AI to drive organization-wide efficiencies and gain competitive advantage. They understand that the failure to scale AI can stagnate their growth and put them out of business in the coming years.
In spite of the great enthusiasm to successfully adopt and scale AI, many businesses are falling short. According to a survey featured in Harvard Business Review, only 8% of the surveyed businesses are engaging in practices that support widespread AI adoption. As a result, most businesses are only piloting AI or using it for a handful of business processes.
Although AI pilots and single processes can give some small gains to the business, they are nothing in comparison to the big wins that AI has provided to breakaway companies i.e., the 8% of the surveyed businesses. To achieve the same level of results and successfully scale their AI capabilities at an enterprise-level, businesses need to keep the following key considerations in mind.
Since effective AI scaling takes time, sometimes even years, businesses should realize that doing it right doesn’t mean doing it fast. They shouldn’t focus solely on quick wins. Successfully scaling AI means doing it purposefully, with patience and persistence. It is to be viewed as an investment into a strategic long-term project.
Organizations should shed the mentality that a fully formed idea or a robust business tool which ticks all the boxes is needed to get started with an AI initiative. AI applications are rarely successful in the first attempt.
Businesses should realize that in the process of effectively scaling their AI capabilities, mistakes are bound to happen. However, these mistakes and shortcomings should be looked as sources of discovery, which would foster a culture of innovation. A test-and-learn mindset would enable the workforce to be better prepared and motivated for solving similar problems in the future. An agile approach is needed to get early user feedback and rectify minor issues before they become critical problems.
Effective AI scaling can be achieved only via collaboration between cross-functional teams and departments. The large-scale collaboration can offer a mix of skills, ideas, and perspectives, enabling enterprise-wide AI scaling, not just siloed business issues. This is because the different teams are better positioned to analyze the impact of AI on their operations and suggest specific tweaks to enhance their processes. Therefore, businesses should foster a culture of teamwork across all teams and departments.
AI scaling requires upskilling across all departments. Businesses should, therefore, set up in-house training programs for their workforce for all the critical AI-related job functions.
Furthermore, attracting and retaining the brightest talent is critical to successful AI scaling. Businesses should consider multiple methods to not only attract but also retain their best talent, going beyond monetary compensation. For instance, businesses should have well-defined roles and career paths. Some businesses even have dual-career paths and rotational programs to enhance employee skills and retention.
Transparency is critical for scaling AI. Making business outcomes delivered by AI understandable and explainable is needed to answer both stakeholders who want to know how AI generated its results and suggestions, and compliance authorities. You may not fully explain the nitty gritties of deep neural networks but you should give a certain level of understanding into how AI functions.
A culture of recognition and acknowledgement inspires employees to innovate. Leaders should proactively shine a spotlight on employees who contributed towards the success of an AI initiative. Creating new roles for top performers and giving promotions to people who are key in AI transformation are some more best practices to adopt.
Most companies start their AI journey with a focus on having the right data. And they’re not wrong about it. Several AI projects related to machine learning and Robotic Process Automation (RPA) need high quality data. However, before focusing on data issues, organizations should evaluate whether they have the right business processes in the first place. Most organizations usually skip this step. When organizations first ensure they have the right business processes, it becomes easier to address data issues strategically and structure the information needed for AI projects.
Many businesses are seeking to adopt AI in their operations. They realize that AI can deliver significant ROI. However, only 20% of AI-aware companies are currently using AI technologies in core business process or at scale. This is because they do not have the right people, processes and technologies in place to be able to effectively scale AI. Therefore, before launching more pilots, it is helpful to step back and look into adopting a holistic approach whilst keeping certain key considerations in mind.
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