Over the last few years, artificial intelligence (AI) has gained momentum as “the next big thing.” Some even project that the technology will be making a bigger impact than the introduction of the internet. With systems like ChatGPT, Midjourney, and Bard, it is time to double down on planning and executing AI integration and infusion into how we are operating as individual contributors, teams and enterprises.
The Great Equalizer
In Formula One, the characteristics and qualities of a car make a world of difference. It is not all about drivers competing against each other. It is a team effort, with a high dependency on the car’s design, down-force, engine power, reliability, overall performance and race strategy. There is one thing that can bring lower performing cars and race strategies back to the front row: The rain. When it rains, Formula One drivers’ qualities and experience exceeds the virtues associated with the cars, making sure all teams across the paddock have an equal chance of winning.
In the world of technology, AI can be that rain. It offers a competitive edge that equalizes the engagement and customer experience of traditional services with commercial, direct to customer, and digital advancements that have taken place in the private sector.
Taking Advantage of AI Responsibly
Companies looking to incorporate AI into their practices should follow a three-layer approach that prioritizes quick wins at the ground level, which will generate trust, a sense of capability, and enable lessons learned and calibration before scaling up through the enterprise level. Implementing AI in internal facing processes and in DTC can showcase the value add vs. complexity, and expose hidden challenges that might arise from adopting new technologies (e.g. skills availability, infrastructure gaps, know-how, etc.) These quick wins can be accelerators for improvement of developer experiences, and transition basic manual or rule-based software business processes to be ML-Powered.
Realistic goal setting and milestone markers are crucial for accurately measuring the success of AI implementation within an organization, alongside a clear communication plan. For example, the below infographic highlights a sample goal of helping teams execute end-to-end processes, with the AI solution being customized and calibrated models. By setting these types of goals and identifying areas of improvement, companies can ensure they are getting the most beneficial returns from their technology implementations.
Key Concerns with AI
Despite the potential for growth and advancement, there are risks and considerations to keep in mind and balance, from technical complexity to data privacy and existing skills degeneration, which can increase the skills shortage we’ve experienced in the market since 2020. One concern, at the process level, includes the accuracy of AI. The technology, and its potential, requires a fair amount of direction and regulation – the latter represents a key consideration for companies when determining next steps and breadth of implementation. Another concern includes the safety and security of the AI solution, including the privacy of PII and IP; making sure that company solutions maintain high levels of competitive edge.
AI: the Future of Optimization
Not often do we have a chance to be in a pole position. Companies have spent billions of dollars optimizing their customer experience (CX), DTC (Direct to Consumer), customer success approaches, and so on. We have the opportunity to produce the same effect as those at a fraction of the cost, increasing our ability to engage with clients and delivering software more quickly and safely.
