Amid the AI frenzy, one might assume that many forward-thinking businesses have already automated their pricing strategies entirely. However, the reality, at least for now, paints a different picture.
Let us address the elephant in the room: while the current AI revolution is indeed producing remarkable outcomes, particularly in creative realms like visual, audio, and textual generation, it is not without its hiccups. Despite notable advancements, such as the ability to generate content, AI still grapples with issues like “hallucinations” (read more on this phenomenon on the IBM site). Nevertheless, the pace of technological advancement remains staggering.
On the pricing front, although major pricing software providers have integrated some AI functionalities, many of these capabilities are mere extensions of previous offerings (i.e., calculations of willingness-to-pay or price versus volume matrices). For AI to truly revolutionize pricing, the focus must shift from “generative AI” to more “computational AI,” empowering machines to define, model, test, and scale pricing approaches. This evolution is inevitable but will unfold over years rather than months.
So, how can companies position themselves to harness the benefits of these technological advancements? Here are six actionable recommendations:
- Clean your data: As the age-old adage goes, “garbage in, garbage out.” This principle holds true in the realm of AI. Take the time to meticulously clean and map out your data inputs. While machine learning can mitigate some issues, starting with clean data will save headaches down the road.
- Streamline Your Processes: AI has proven extremely useful at replicating and automating numerous tasks which historically belonged to “knowledge workers” only. Several examples have been posted on how LLMs have been trained on patent process and international tariffs. However, to expedite the “training” process of the models, having clear process steps, documented procedures and typical exceptions can dramatically accelerate the process.
- Develop your change management muscle: there is certainly a learning curve that every organization will need to handle, adapting to a new worker & machine collaboration. Help your employees understand how AI can aid make the company better instead of undermining (or completely replacing) their work.
- Cultivate Early Adopters: Identify and support individuals within your organization who are eager to embrace AI initiatives. Their successes and insights will inspire others to follow suit.
- Help the rest of the organization follow along: while early adopters will pave the way, do not forget to involve and educate the rest of the organization. Lack of understanding and fear will quickly alienate a portion of your enterprise.
- Strengthen your Project Management: Many organizations struggle with managing AI and data science initiatives effectively. Despite the allure of new technologies, robust project management practices are essential to avoid diluting your efforts. Prioritize initiatives wisely, focusing efforts on pivotal areas for effective implementation.
By leveraging these recommendations, businesses can position themselves to navigate the AI landscape effectively and capitalize on its transformative potential. If you need some additional help, please reach out to me.
