AI FOR PRICING AND REVENUE MANAGEMENT: THE ERA OF COGNITIVE COPILOTS PDF Free Download
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AI FOR PRICING AND REVENUE MANAGMENT:
THE ERA OF
COGNITIVE COPILOTS
WHITEPAPER
Artificial Intelligence (AI) Trends Overview
From Workflow Automation to AI and Cognitive Copilots
AI Adoption Challenges
5 key Steps for Cognitive AI adoption
Our Special Offer
The New Era of AI: Cognitive Copilots
TOP 10 Business Cases for Cognitive Copilots
Cognitive Copilots in Commodity Trading
Cognitive Copilots for CPG & Retail
Data Democratization with Cognitive Copilots
Examples of Cognitive Copilots
Table Of Content
Dear valued readers,
We are proud to present the second edition of our Whitepaper on the state of AI in Pricing
and Revenue Management. In this paper, we will discuss the latest trends and developments
in the field, building on the predictions we made in 2020 that have since come to fruition.
One of the most significant trends we are seeing is the growing interest in building up in-
house AI capacity or outsourcing this task to external providers. This shift towards AI-driven
revenue management is being driven by the need for greater efficiency, accuracy, and agility
in managing revenue streams.
At the heart of this trend are emerging Generative AI/ Copilots, which are designed to
support a wide range of revenue management tasks. These Copilots are becoming
increasingly sophisticated, with the ability to learn and adapt to specific business needs,
providing personalized insights and recommendations that can help drive revenue growth.
Another key trend we are witnessing is the move towards AI Transformation, where
companies are leveraging AI to fundamentally change the way they manage revenue. This
involves the integration of AI into every aspect of the revenue management process, from
forecasting and pricing to promotions and customer engagement. Here, the use of AI
techology is increasing the speed of Digital Transformation in areas where it was not
pervasive before, enabling faster and more drastic automations and interventions in the field
of pricing and revenue generation.
In addition to these trends, we have also identified several key developments in the field,
based on our conversations with clients and industry experts. These include the growing
importance of Process Mining and integration, instant and digital purchasing, and AI-driven
promotions.
At Revenue.AI, we are committed to staying at the forefront of these trends, and we are
excited to share our latest portfolio of AI-driven Revenue Management solutions. We believe
that these solutions will help companies of all sizes unlock the full potential of AI, drive
revenue growth and competitive advantage in the years ahead.
Thank you for your interest in our whitepaper, and we look forward to discussing our latest
insights and expertise with you.
Best regards,
~ Istvan Czilik,
CEO of Revenue.AI company
Introduction From Our CEO
The trend toward smaller and more efficient models continues, driven by the need to reduce
resource consumption and democratize AI. Open-source models like LLaMa 3 and new
techniques like Low Rank Adaptation (LoRA) make AI more accessible and efficient, enabling
smaller players to compete with larger companies.
Businesses can gain a competitive edge by developing custom AI models tailored to their
specific needs. However, as AI capabilities grow, so do concerns about misuse, such as security,
privacy, and copyright issues. The rise of shadow AI, where employees use generative AI tools
without official approval, poses risks to businesses. Companies need clear AI policies to manage
these risks and ensure responsible use of AI.
As 2024 progresses, the responsible and strategic adoption of AI will be crucial. Businesses
must stay informed about emerging trends, balancing innovation with ethical considerations to
harness AI's potential while mitigating risks.
In 2022, Generative AI captured public attention, and by 2023, it began to establish its presence
in the business sector. This upward trajectory continues into 2024, marking a shift towards
democratizing AI across various industries. Gartner separates AI into two high-level categories:
Everyday AI, which enhances productivity, and Game-changing AI, which disrupts business
models and industries. Let’s highlight the trends that make a difference.
It becomes clear that, from Q2 of 2024, the next
wave of AI advancements will focus on multimodal
models, which process multiple data types and
enhance applications' versatility. For example,
users can interact with AI using natural language,
images, or videos. AI applications become more
intuitive and practical.
As demand for AI capabilities grows, GPU
shortages and rising cloud costs will push
innovators to develop more affordable and efficient
hardware solutions. For example, Intel recently
launched a new AI CPU that promised to bring deep
learning inference and computing to the edge. This
has sped up the race between giants like Intel,
Google, Nvidia, etc.
AI Trends Overview
Revenue.AI
We are at the beginning of
the New Industrial
Revolution, which is a
revolution in the production
of intelligence.
~ Jansen Huang,
Founder & CEO at Nvidia
“
The trend of utilizing AI-powered Copilots represents a significant development in
the field of automation.
This shift is fueled by rapid advancements in Generative AI (LLM). With the release of OpenAI's
ChatGPT in November 2022, showcasing enhanced capabilities in generating detailed, accurate,
and creative responses, the era of GenAI (LLM) began. Products like Google's conversational AI
tool Gemini, Microsoft Copilot, and ChatGPT started to be adopted by business users to
automate various complex tasks, such as administrative work, data analysis, and content
generation.
When it comes to pricing and revenue management, however, there is a notable drawback of
employing GenAI as a copilot for this process - the potential of LLM models offering misleading
information. There's nothing more detrimental than finance reports containing inaccurate
("generated") data.
This is precisely the challenge that Cognitive Copilots are poised to tackle.
LLMs
Landscape
Revenue.AI
From workflow automation to AI and
Cognitive Copilots
Cognitive Copilots leverage advanced AI algorithms
to analyze vast amounts of data, identify patterns,
and generate actionable insights. By understanding
user queries and context, these intelligent assistants
can provide personalized recommendations,
automate tasks, and streamline workflows.
The Next Step:
Cognitive
Copilots
The simplest illustration of today’s Cognitive Copilots is the way ChatGPT 4o is reacting to human
interactions, remembering what was being discussed, changing tone of voice, and doing other
adjustments to environment requests.
One of the key drivers behind the rise of Cognitive Copilots is the exponential growth of data and the
need to extract meaningful insights from this data. Cognitive technology enables businesses to
harness the power of data by analyzing complex datasets, identifying trends, and predicting future
outcomes.
The trend for Cognitive technology was spotted by Revenue.AI more than 5 years
ago, and in response, we developed a family of personalized Cognitive Copilots. This
aims to support and augment the decisions of humans in various roles and across
various industries.
Figure 1. From Workflow
Automation to Cognitive
Copilots journey: Embracing
New Technologies
Areas where Cognitive Copilots can
disrupt operations around Pricing &
Revenue management and
connected fields
Real-time visibility into trade positions and risk exposure is now more critical than ever. The
incapability to perform this task can lead to suboptimal decision-making and missed
opportunities. Furthermore, the significant data flow resulting in delays in risk exposure
evaluations, which could lead to financial losses, is a noteworthy concern.
This is why there's a great opportunity for automation and modernization in middle office with
Cognitive Copilots. With Cognitive Copilots, business can mitigate complications associated with
human errors, optimize operations, increase productivity, and enhance real-time data visibility.
While various Commodity Trading (CT)
businesses have been using Artificial
Intelligence to streamline operations, improve
efficiency, and make better-informed decisions,
some fail to catch up. Many CT organizations
still rely heavily on manual processes and
outdated systems in their middle office. This
prevents them from timely adjusting to the ever-
changing market conditions.
Cognitive Copilots
in Commodity
Trading
By automating processes such as
price quality checks and
reconciliation, Cognitive Copilots
can not only save time but also
ensure accuracy and reduce the risk
of errors in trading.
To control price volatility,
Commodity Trading organizations
utilize various tactics. These
include demand management,
supplier strategies, financial
hedging, maintaining a strong
control environment, employing
sophisticated trading and reporting
systems, and implementing
suitable sustainability practices.
While traders have traditionally relied on these tactics, the landscape is rapidly evolving.
Therefore, it is essential to integrate cutting-edge technology like artificial intelligence along with
a proper data strategy to ensure continued success in Commodity Trading. Cognitive Copilots
are at the center of this change, leveraging their monitoring market data capabilities and
actionable insights to enhance the efficiency of all Commodity Trading practices.
AI Copilot tools like Zeta are revolutionizing Commodity Trading. Zeta offers a variety of benefits,
ranging from real -time data visibility to automated insights, aiming at streamlining operations and
enhancing efficiency across various commodity markets.
WATCH THE VIDEO
In area of Demand Management, Cognitive Copilots are able to provide real-time insights and
notifications, optimizing inventory management and reducing dependency on volatile
commodities. Real-time market data analysis, enhanced by Cognitive Copilots, involves
scanning different markets simultaneously to identify pricing disparities and inefficiencies. This
enables traders to capitalize on arbitrage opportunities. Once these opportunities are identified,
Cognitive Copilots provide actionable insights to traders, enabling humans to execute arbitrage
trades effectively. Whether it’s exploiting price differentials between future contracts and spot
markets or taking advantage of geographical arbitrage opportunities, Copilots offer real-time
recommendations to maximize profitability.
Introducing
ZETA
Commodity
Trading
COPILOT
Cognitive
Copilots in
CPG & Retail:
AI emerges as a highly potential technology for
Revenue Management in CPG and Retail sectors,
compared to the commonly used Business
Intelligence (BI) software and Machine Learning
algorithms. While GenAI can generate numerous
new opportunities in forecasting and
predictive/prescriptive analytics, AI Cognitive
Copilots stand apart with their numerous unique
features and functionalities.
Cognitive Copilots can help businesses manage their revenue more effectively by providing real-
time insights into financial performance (such as revenue, expenses, and profit margins) and
accurate forecast of the financials measures. With the capability to enable retail execution, Cognitive
Copilots allow field team to take pricing decisions and provide access to strategic pricing and client
inf ormation on the go. The accurate performance management becoming a reality and no longer
just a dream of generations of Sales Managers, now that they have AI Copilots.
Many CPG and Retail companies find it challenging to keep up with the fast pace of online retail
because their traditional pricing strategies are too rigid and slow to adapt to changing market
dynamics. However, Cognitive Copilots are revitalizing these outdated business models. The AI tools
leverage advanced ML Algorithms to analyze large datasets and create pricing strategies that can
quickly adjust to market changes, and business goals. Furthermore, by delivering insights directly to
business users and decision-makers through intelligent alerts and notifications, Cognitive Copilots
accelerate business processes and enhance the precision of pricing decisions. Cognitive Copilots
can also assist in navigating the pricing landscape amidst inflation and identifying cost-saving
opportunities within organizations, thereby mitigating the impact of inflation on overall expenses.
With millions of simulations running in the background, Cognitive Copilots help businesses manage
negative mix effects, which can significantly impact revenue. Negative mix effects arise when
changes in product prices and quantities lead to a revenue decline.
Promotion planning and execution with Cognitive Copilots reduce complexity and improves ROI. It's
a stark reality that Revenue Management teams often have limited capabilities. Analytics activities
are mostly ad-hoc in nature and discussed in silos across the Revenue Growth Management (RGM)
team. Historical promotion effectiveness analytics is also conducted ad-hoc by RM team members.
However, with Cognitive Copilots, any member of the Finance, Marketing, or Revenue Management
teams can directly talk to the Promotion Calendar and Promotion Analytics, gaining insights in a
matter of seconds without the need to initiate ad-hoc analytics from the Revenue Management
team.
Revenue Management
and beyond
Additionally, with the ability to automatically aggregate multiple datasets (sell-in, sell-out,
online/offline), Cognitive Copilots ensure zero manual effort in data preparation and cleansing.
The implementation of Cognitive Copilots for Promotion becomes essential when organizations
lack insights into whether promotional activities yield expected business results, as neither ROI
estimation nor regular post-mortem analysis for each promotion folder is in place.
Examples of Queries from Business Users to Cognitive
Copilot in CPG & Retail Organizations
Explore new trends in RGM from GenAI maturity: Synthetic Customer Data and
Generative Pricing.
This is where LLM (Generative AI) introduces a new opportunuties. This cutting-edge technology
can be utilized by Cognitive Copilots not only for conversations, but also for two significant cases
in Revenue Management. These include Synthetic (Generative data) production to simulate user
behaviors and enhance predictive analytics, as well as Generative Pricing. Gartner predicts that by
2026, 75% of businesses will use Generative AI to create synthetic customer data, up from less
than 5% in 2023.
Synthetic data is not a new concept, and it has gained significant momentum with the new
opportunities presented by Gen AI. On the other hand, Generative pricing, similar to Generative
images, texts, or codes, is a completely new concept that still needs to be tested in the real market.
Data
Democratization One of the powerful functions of Cognitive
Copilots, which has drastically transformed
data processing and accelerated data
democratization, is they enabling users to
converse with business software, analytics,
dashboards, and knowledge in a human-like
manner.
Talk to enterprise knowledge
base, business software or
dashboards as you talk to
your colleagues
Despite a very promising and rapid start, and
huge interest from business, it became clear
very quickly that in most cases, AI applications
such as Microsoft Copilot, Perplexity, GPT 4, or
any open-source LLM are not sufficient for
Enterprise usage.
The Cognitive Copilots are utilizing Generative
AI to enhance conversational abilities, which is
just the tip of the iceberg; the complex
processes remain hidden. This is fundamental
from a business perspective because
technology can drive transformations and
changes when it genuinely helps people and
makes a difference.
In this emerging reality, with users increasingly engaged with ChatGPT and
similar AIs, we're confronted with a bold statement:
SEARCH BECOMES OBSOLETE.
Cognitive Copilots can understand user queries, extract insights from enterprise data, and deliver
accurate, personalized results in real-time. This enhanced information retrieval capability
empowers employees to make informed decisions and drive more precise business outcomes.
More significantly, Cognitive Copilots can personalize user experiences and follow enterprise
security guides and access restrictions rules. This is the reason why Cognitive Copilots should
not be confused with GenAI. Whether engaging in conversation with users or accessing reports,
dashboards, and data, Cognitive Copilots tailor all outcomes based on user preferences, queries
and behavioral patterns, as well as role/responsibilities and access levels.
By 2027, more than 50% of the
GenAI models that enterprises
use will be specific to either
an industry or business
function — up from
approximately 1% in 2023
~ Gartner.
“
Yes!
Revenue.AI offers a suite of Cognitive Copilots designed to streamline
insights discovery and enable timely decision-making for businesses.
A Cognitive Copilot, which is a multi-
purpose virtual data analyst that
streamlines insights discovery to
enable timely decision-making.
Are there a Cognitive Copilots
already working for businesses?
RAI Base
The Copilots work by connecting diverse company sources and inputs into a single
resource library, including company files, analytics, raw data, and more. The AI
pipeline then executes resource understanding via metadata extraction, natural
language processing, and document understanding to provide relevant answers or
information in a user-friendly way.
It is designed to work seamlessly with existing tools and workflows, providing
real-time assistance without disrupting the user's work process.
Revenue.AI
RAI Price with expertise in pricing,
proactively monitors all market
activities and assists in pricing
decisions.
RAI Charles a promo expert, which
provides alerts for risks and
opportunities, drives promo calendar, ROI
calculations and simulators, ensures the
effectiveness of all promotions.
RAI
CHARLES
RAI PRICE
Designed specifically for commodity
trading. It simplifies tasks and automates
manual work for traders, risk managers,
and middle office analysts. The Zeta
Copilot is powered by a generative AI
engine that ensuring smooth and easy
natural language communication.
ZETA
Rai-Dex a virtual data steward, it
able to clean and enrich your
data in real time.
Allie is an Agile Copilot developed by
Revenue.AI. This AI assistant designed
for a particular use case—Agile Projects
—and has several key differences that
set it apart. Allie has a detailed
understanding of Agile Methodology,
Agile ceremonies, and the Agile
Manifesto that guided its design.
RAI DEX
Allie
MORE COGNITIVE COPILOTS ON OUR WEBSITE
Revenue.AI
Typical challenges faced by
companies today while adopting AI
Companies are increasingly turning to Artificial Intelligence (AI) to enhance their
operations and stay ahead in the competitive landscape. However, integrating AI into
businesses comes with challenges. A major hurdle is the lack of clarity on AI's purpose
and its potential to streamline business processes. This lack of understanding can lead
to a lack of support for AI initiatives, making it difficult to secure the necessary
resources and funding to move forward.
To overcome this barrier, companies must first educate themselves about the potential
benefits of AI technology. This involves understanding how AI can be used to automate
tasks, improve decision-making, and gain insights into customer behaviour and market
trends. By understanding the potential benefits of AI, companies can build a business
case for AI adoption and secure the necessary resources to move forward.
However, even with a clear understanding of the benefits of AI, companies may still
struggle to integrate AI into their operations. This is because AI technology can be
complex and difficult to implement, requiring specialized skills and expertise. To
overcome this barrier, companies must ensure that they have access to the necessary
skills and expertise to implement and manage AI technology effectively.
Figure 2:
In the upcoming chapter, we aim to introduce you to our unique system, crafted from our extensive
experience in AI adoption and transformation projects with clients across industries such as CPG,
Retail, Consumer Services, and Commodity Trading. This system will assist you in initiating AI
adoption within your organization by providing a clear roadmap.
And last but not least, we've all heard about the cost implications of various LLMs, which, if
not used smartly, can skyrocket rapidly. This issue, quickly apparent to all companies
interested in integrating LLM into business processes, becomes a primary obstacle. Because
any investment, regardless of the promise and advancement of the technology, must carry an
ROI calculation.
In comparison to other solutions, Revenue.AI platform offers a
hybrid solution of LLM (GPT 3.5 T & GPT 4 T) + RAI Copilot,
which is more cost-effective than not optimized LLM usage
for GPT 4 Turbo, Gemini Pro, and Claude 2. The hybrid solution
is priced at $23,040 for global usage, while the not optimized
LLM usage for GPT 4 Turbo, Gemini Pro, and Claude 2 is
priced at $126,000, $88,200, and $100,800, respectively.
The calculations are based on prices as of April 15, 2024.
Revenue.AI
SAVE A SEAT
5 key steps for Cognitive AI adoption
The journey described below has been successfully validated by several of our
clients in recent years, with high satisfaction. This unique approach includes five
key steps that are essential for successfully integrating AI into business operations
and maximizing its benefits.
The first step in the AI journey is to educate the team on the potential of AI technologies and
demonstrate quick wins. This involves introducing the concept of AI, and its applications in
business, and showcasing tangible examples of how AI can drive revenue growth. By
highlighting the immediate benefits of AI adoption through quick wins, teams can gain
confidence in the technology and its potential to transform business processes.
Showing several example cases is a critical step in this journey to remove friction.
EDUCATING THE TEAM WITH QUICK WINS
STEP 1
FREE EDUCATIONAL TRAINING SESSIONS ON
AI FUNDAMENTALS FOR ENTERPRISES.
In the learn and design phase, teams dive deeper into the functionalities AI solutions and begin
designing a customized approach to AI integration. This phase involves identifying key use
cases for AI implementation, defining success metrics, and outlining a roadmap for AI
deployment. By learning about the intricacies of AI technologies and designing a tailored
strategy, businesses can set the foundation for a successful AI journey. Some
key topics we need to address are:
What can we get out of DIY AI? How far can we get?
Best tools to work with & sample scenarios rundown (with prepared content)
Managing RISKS, i.e. What not to Expose / Data & Legal recommendations
AI-assisted journey design
Best tools to work with & algorithms selection
DIY-AI build of MS copilot for a specific job-role automation workflows
Once the team is educated and onboarded, the next step is to enter the discovery phase. This
involves understanding the capabilities of different types of AI tools, their potential impact on
business, and how they align with the organization's goals and objectives.
We have built our R-AI Cookbook that
helps as a robust framework designed to
expedite and fortify your AI journey,
offering a systemic approach tailored to
your organization's needs. Session is
composed of four discernible sections to
ensure comprehensive coverage and
optimal utilization of time and resources.
The workshop should include:
Thorough brief utilizing both pre-provided data
and real-time insights.
Comprehensive outline of AI's top outcomes
and vision potential, supported by identified
growth opportunities.
Pragmatic Q&A to uncover strengths and
weaknesses on a strategic journey towards
envisioned results.
Conclusion with actionable recommendations,
pinpoint gaps and tailored steps for the 30-60-
90 day timeframe Book a Complimentary AI
Discovery Workshop
STEP 2 ENTER THE DISCOVERY PHASE
LEARN AND DESIGN
STEP 3
Recommendation: 1-2 hours of focused workshop supported by
AI professionals
Recommendation: 8-10 hours Focused Training
CALCULATE
PRIORITIES
RUN
WORKSHOP #1
RUN
WORKSHOP #2
CALCULATE
ROI
Building your own Copilot is a pivotal step in the AI journey. This involves customizing AI
solutions to meet the specific needs and requirements of the business.
First things First: How to identify and prioritize where to
Implement AI Copilot in Your Organization? How to calculate ROI?
Run workshop #1 about the jobs in question to consider the
impact change would have.
We need to choose which priorities pose the biggest risk to growth or where there is a high
level of staff turnover in a team due to manual tasks. Based on this, pick the top three job
descriptions that could work well with the involvement of a Cognitive Copilot.
Below is a recommended (example) workflow that can assist in deciding exactly which
Cognitive Copilot would make the most impact according to your growth model.
STEP 4 BUILDING YOUR OWN COPILOT
CALCULATE
PRIORITIES
RUN
WORKSHOP #1
RUN
WORKSHOP #2
CALCULATE
ROI
When listing the tasks in the job description, the order of priority should go from the tasks that
require the most interaction between teams to ones that require less (i.e. it is irrelevant who
conducts these functions if they get done). Now you have to assign estimated “effort” values to
the following table to calculate the total yearly effort.
Figure 3: Example table efforts estimation for
Revenue Manager role
Figure #7. Efforts Estimation Table Example (Revenue Manager)
CALCULATE
PRIORITIES
RUN
WORKSHOP #1
RUN
WORKSHOP #2
CALCULATE
ROI
INPUTS
List the number of OPEN positions you have currently that need to be filled according
to role and region or time zone.
List the number of EXISTING positions you have currently that are filled according to
role and region or time zone.
Add a table to list these roles. Then apply effort estimates to the number of roles per
region and calculate subtotals.
Calculate the effort in the given area:
Brainstorm and define the work automation possibilities for each task item. Draft
decision flows and systems that your inquiry team can check and that potentially can
be built into a fast AI engine based on data availability and how well the current
decision flows are.
List the elements you consider to be immediately capable of automation. Then quickly
calculate the effort saved and rerun the exercise to consider new opportunities that you
can devote the time saved to.
List at least five new job tasks that you consider achievable with your current team, but
you never thought you’d have the energy to do them. Add these job tasks to the Excel
sheet used previously and calculate the effort needed to complete these tasks.
Figure 4: Example table for team size estimations, including churn expectations
CALCULATE
PRIORITIES
RUN
WORKSHOP #1
RUN
WORKSHOP #2
CALCULATE
ROI
Getting started on the right path is simpler than initially thought; it only requires taking the
first steps with a couple of straightforward success stories that will gain traction within
the organization.
While Cognitive Copilots can readily be used to tackle challenging situations, it's also
beneficial to assess the setup costs against the work and time saved by having Cognitive
Copilots at the heart of the organization.
Calculating the cost of missed opportunities without a Cognitive Copilot implementation
is nearly impossible, but even a simple calculation on team time savings shows
promising results from an ROI perspective for this initiative.
Explore Automation Results and financial effect
DOWNLOAD
GET THE AUTOMATION ROI
CALCULATION KIT FOR FREE
AUTOMATION RESULTS
TOTAL CHANGE VS ORIGINAL
Figure 5: Example table of automation results (FTE saving with automation)
The final step in the AI journey is to build a comprehensive AI and data strategy. This involves
developing a roadmap for AI implementation, defining data governance policies, and establishing
key performance indicators (KPIs) to measure the success of AI initiatives. By building a robust
AI and data strategy, businesses can ensure the effective integration of AI technologies into their
revenue management processes and drive sustainable growth.
At Revenue.AI, we understand that embarking on an AI journey can be daunting for many
businesses. That's why we are committed to supporting our clients every step of the way, from
educating the team with quick wins to building an AI and data strategy.
We are so committed to helping our clients achieve success with AI that we offer some of our
services for free. For example, we provide free consultations to help businesses understand the
potential benefits of AI and how it can be applied to their specific use cases. We also offer free
training and education resources to help businesses build AI literacy and develop the skills
needed to succeed with AI.
Our goal is to make AI accessible and affordable for businesses of all sizes, and we believe that
by providing free resources and support, we can help more businesses harness the power of AI
to drive revenue growth and improve profitability.
So, whether you're just starting to explore the potential of AI or you're ready to build your own AI
Copilot, Revenue.AI is here to support you every step of the way. Contact us today to learn more
about how we can help you achieve success with AI.
STEP 5 BUILD AI AND DATA STRATEGY
Conclusions
In today's fast-paced and competitive business environment, organizations are constantly
seeking innovative ways to transform their operations and drive growth. One such
transformative tool is the Cognitive AI Copilot, a cutting-edge technology that is revolutionizing
the way businesses operate. By harnessing the power of artificial intelligence and machine
learning, Cognitive AI Copilots are enabling organizations to change at their core, optimizing
processes, enhancing decision-making, and driving efficiency.
Implementing AI in an organization can lead to a fundamental shift in how tasks are performed,
decisions are made, and strategies are developed. These intelligent assistants can automate
complex financial tasks, streamline operations, and provide real-time insights to enhance
productivity and reduce errors. By leveraging AI technology, businesses can optimize their
pricing strategies, identify new revenue streams, and improve cross-functional alignment across
departments.
Figure 6: Transformation example for the role of Revenue Manager
Transforming the Organization at Its
Roots
ROI GUARANTEE
Copilot Pricing:
25K USD
with 90 Days ROI
guarantee
15% OFF - GRAB THE DEAL
SHEDULE A DEMO
15% OFF Build and Implement your
AI Copilot Package
Want to amplify your business?
Let’s build your in-house AI (Copilot)
using our Core RAI Engine and expertise.
Integrate your own algorithms
Connect external or internal data sources
Get support for automatic data feeds
Add Copilot to Teams, WhatsApp or Telegram
Accessible anytime, anywhere, offering convenience for on-the-go decision-makers. You can add it to
your Teams or WhatsApp and start chatting about data, insights, and predictions. The RAI Copilot
can also alert you for important real-time events that need your attention.
Suitable for B2C and B2B companies: revenue managers, pricing managers, middle office, risk
managers, traders etc.
Industries: Commodity Trading, Consumer Goods, Healthcare, Manufacturing and Retail Companies
Bring your own data - we don’t need enterprise access
Data structure and unification architecture
Data transformation
Data upload to the platform
Data safety – strict data governance
Data Services provided along with the Copilot implementation:
About Revenue.AI
contact@revenue.ai
Revenue.AI provides Artificial Intelligence technology for pricing and revenue management in
various industries such as CPG, retail, and commodity trading. Our offerings include an AI-driven
pricing platform, Cognitive Copilots implementation, and AI-adoption advisory services.
Contact Us
OUR SPECIAL OFFER
Suggested for companies looking to quickly implement an AI solution for
better day-to-day efficiency and decision-making.
