We had the joy of bringing together leaders in manufacturing and industrial for a good breakfast and even better conversations. The co-hosted event by Reaktor and BCG focused on the transformative role of AI in commercial operations.
Technology, and especially artificial intelligence, is reshaping how we buy and sell, but the journey from pilots to scaled solutions can be surprisingly long. Even with all the hype and potential around AI, companies still grapple with the same challenges: how to get the right data, move beyond the pilot phase, and ensure that people remain at the core of the change?
By addressing these core elements – data, culture, and scaling – companies can tap into AI’s potential and shape the future of commercial operations.
1. Focus on data
The role of data was a recurring theme throughout the discussions. Many companies are focused on improving their data quality, but there is no shortcut to success. In many cases, issues related to data availability and quality prevent the project from being implemented or make it unprofitable.
Over breakfast, we discussed how to ensure that data is error-free, comprehensive, and up-to-date to support AI applications. One observation was that it is not only a matter of data experts. Many people in the company play a crucial role as data producers and in ensuring its quality. It is important for employees to understand the connection between the data and the AI solutions that rely on it.
High-quality data is crucial in sales, purchasing, and pricing decisions, as errors translate directly into financial losses and poor customer experience. Still, waiting for perfect data can slow down AI solution adoption. It’s essential to recognize when the benefits of an AI solution outweigh the risks of data imperfections.
2. Involve everyone
The change requires more than just implementing new software – it demands a cultural shift. Teams must embrace new workflows and let go of old routines, a process that rarely succeeds without leadership support. Often, after the initial excitement and pilot phase, enthusiasm fades, and teams revert to their old ways of working. Leaders at all levels must set an example, supporting employees to avoid getting stuck in old routines.
AI can spark concerns, as employees fear it may replace them. However, successful AI implementation relies on employee buy-in and satisfaction. The event speakers stressed the importance of leading AI-driven change beyond the technology itself.
3. Break out of the pilot phase
Transitioning from a pilot to a full-scale implementation is a significant hurdle. In the pilot phase, it is relatively easy to get early adopters on board. But when scaling solutions, a broader user base and leadership commitment become essential.
Reaktor and ABB have collaborated to develop an AI-powered sales productivity tool aimed at enhancing the efficiency of ABB’s sales teams. In the event Reaktor and ABB showcased the tool which is a great example of starting from a pilot and taking steps towards productised solution that scales across the company.
Although pilots can be cost-effective, scaling a solution requires substantial investment, especially to integrate AI into a broader infrastructure and refine the data used. And not to forget the time and resources required to facilitate the change process across different parts of the organisation. To make smart investment decisions, leadership needs to understand the benefits and costs of an AI-assisted solution.
Tips from the event participants:
- Start with simple, high-volume use cases that promise clear benefits.
- Measure and demonstrate pilot impact to get buy-in from leadership and teams.
- Ensure your data infrastructure and processes support scaling.
Fact: BCG’s research reveals that 40% of companies have yet to start an AI project, 50% are in the pilot stage, and only 10% have scaled solutions.
4. Explore AI opportunities in pricing
Many companies leverage AI in their sales and marketing efforts. In pricing, the use of AI was seen as an intriguing new opportunity, even though algorithm-based pricing solutions have been around for years. None of the participants mentioned that their company is utilizing a production-level AI solution in their pricing.
AI can streamline traditional pricing strategies or open new pricing possibilities, thanks to its ability to analyse vast datasets to inform pricing decisions. Additionally, AI-supported real-time pricing adjustments based on demand, supply data, competition, and market shifts can enhance dynamic pricing. In value-based pricing, AI presents an exciting challenge in quantifying customer value.
The event shed light on why companies should explore AI-enabled pricing:
- Even slight price optimizations in high-volume areas deliver quick results.
- AI can help detect biases in sales representatives’ pricing and identify instances where customers may pay more.
- AI-optimised pricing also accelerates quoting processes, boosting the hit rate.
In the end, it all comes down to the business benefits. What are the most valuable business cases, and what kind of benefits can you achieve in your commercial operations? Is it faster time-to-quote or reducing manual work? Could your hit rate be improved, or could you prioritize different sales activities? By identifying the valid drivers for your AI solution, you make sure that it won't be "just another tech implementation" but an actual transformation.
