[Get your Whitepaper] Latest AI Trends, Cognitive Copilot capabilities & Roadmap to AI Success

AI For Pricing and Revenue Management: Critical Insights to Win Big with Cognitive Copilots  

Artificial Intelligence (AI) is taking the world by storm with multiple ML (Machine Learning) models and LLM (large language models) coming to life, transforming the way businesses operate. It is estimated that 85% of business leaders will have adopted Generative AI for low value tasks this year (2024). Joining the AI race has now become a must to survive.  

 

The clock is ticking for those newly embarking on this journey. And the correct guidance to AIis needed to save resources and assure success. Below are some key findings from Whitpaper AI For Pricing and Revenue Management: The Era of Cognitive Copilots, which highlights influential AI trends and best practices for businesses for successful AI adoption. 

Generative AI brings about various benefits, together with great concerns

The release of OpenAI’s ChatGPT late 2022 has started the era of GenAI. With capabilities to generate quick, detailed, and creative outputs, this product and similar LLMs (Large Language Models) are being used widely for data browsing, visual and text content generation. For organizations, business users start to use GenAI solutions, such as ChatGPT, Google’s Gemini, and Microsoft Copilot, to automate multiple tasks. These include administration tasks, or data analysis. 

It can be seen  that from Q2, 2024 on, multimodal models has become a trend. These models can process different data modalities, allowing more intuitive and practical use of AI applications. Users can expect to interact with these models using natural language, images, or videos. 

Download Whitepaper to learn more about AI trends 

AI opportunity radar

Gartner puts Everyday AI and Game-changing AI on two sides of the axis. Source: Gartner 

Despite the benefits, GenAI comes with challenges. The whitepaper lists out 3 biggest obstacles in GenAI adoption: 

  • There is concern about LLMs generating false and misleading answers when employed as Copilot for Pricing and Revenue Management. Since this process requires absolute accuracy, “AI-generated” data can be a huge risk and failure. 
  • Shadow AI is referred to employees’ unsanctioned use of GenAI. This poses the risks of security and copyright infringement to businesses. 
  • Implementing a Large Language model (LLM) directly into a workflow can often lead to non-optimal and uncontrolled costs. To avoid these issues and improve the ROI from AI adoption, organizations should employ smart strategies for LLMs usage. For example, using a single LLM without smart optimization can cost significantly more—up to two to three times more—compared to using a Hybrid LLM via a robust AI platform like Revenue.AI’s, which optimizes usage and manages costs effectively. 

Cognitive Copilot is the next step to achieve operational excellence

While Microsoft Copilot refers to a GenAI chatbot, Cognitive Copilot is a different term and an advanced concept of AI agents. 

Cognitive Copilot refers to an AI cognitive tool that understands commands, both typed and voiced, automatically looks for insights and dashboards, and replies with data-backed answers. The data source utilized by Cognitive Copilots can be from both internal and external sources. Therefore, businesses can relieve the concerns of misleading or false answers. Cognitive Copilots can be programmed to send automated alerts and reminders, to serve different business use cases.  

Revenue.AI spotted the Cognitive AI trend 5 years ago. And throughout our journey, we have been supporting Fortune 500 businesses in achieving maximized productivity and efficiency. 

Users can easily browse information by asking Cognitive Copilot 

“Where should we start with AI and Cognitive Copilots?”

Many companies are facing the same issue in AI adoption – figuring out the starting points. To identify the critical process/ task to start with AI, businesses can guarantee quick win and big win.  

In the whitepaper, we listed several use cases where Cognitive Copilots can make disruptions. The list is formulated based on Revenue.AI’s experience supporting Fortune 500 companies to successfully implement Cognitive Copilots to streamline their operations. 

Let’s take a look at AI for Commodity Trading, where businesses have to adjust in a timely manner to market conditions. Relying on manual processes in the CT’s middle office can prevent real-time visibility into trade positions and risk exposure evaluation. With the use of Cognitive Copilot, commodity traders can enjoy better productivity and enhance data transparency.  

Read more: Managing Price Volatility and Seizing Arbitrage Opportunities with AI 

For CPG and Retail industries, the rigid pricing strategies can make it hard to keep up with the market’s rapid changes, especially when omnichannel retailing is on the rise. This is where Cognitive Copilots can bring about transformation. The AI tools also support pricing landscape navigation during inflation times and discover cost-saving opportunities. 

Roadmap to success: 5 Steps for Cognitive AI adoption

Formulated by our experts, the 5-step AI adoption journey has inspired and supported many of our clients to start strong, win big, and accelerate in the AI race. 

According to Gartner (2023), estimating and demonstrating AI value is among the top obstacles of AI implementation (agreed by 49% of surveyed leaders). Therefore, educating teams with fundamental AI knowledge, its capabilities, and potentials to create disruptions is vital in this roadmap. 

Check out the upcoming Free Training to equip your team with AI Fundamentals. 

Revenue.AI’s 5 step journey to successful AI adoption

During this journey, it is advisable that businesses have chances to develop customized AI approach and build their own Copilots to solve a specific use case. It is important that organizations estimate their project ROIs to have a clear vision of what a successful AI project looks like. 

The last step is working with experts to come up with the ultimate AI strategy. This involves specifying data governance policies and setting metrics to measure success. 

The AI landscape is changing fast, opening up various opportunities for businesses to boost their operation efficiency. For more AI in Pricing and Revenue Management insights, check out our Whitepaper today and schedule a demo with our expert! 

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