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1、The Essential 4 Ps for AI MonetizationA Comprehensive Guide to Success in Your AI Go to MarketPublished jointly by:Building a future we can all trustThe Essential 4Ps for AI Monetization Guide2Table of ContentsIntroduction 3Overview of AI offers today 3Determining the monetization method4Productize

2、your new AI capabilities 5Standalone AI offer-Build a separate business 5Adding AI to existing products-Incremental business 5Considerations unique to AI productization 6Protect your IP investment 7Model poisoning and transfer learning attack 7Input manipulation and output integrity attacks 7Model t

3、heft/leapfrogging lost revenue 7Packaging for direct monetization 8Packaging as a means of differentiation 8Operationalize packaging with software licensing 85 widely used packaging models 9Price your offer for profitability 10The costs of bringing AI offers to market 10Earning returns on AI investm

4、ent via cross-selling and upselling 11AI efficiency impacts user-based pricing 11Revenue instability 12Pricing sensitivity 12Evaluating pricing effectiveness 13Turn investments into profits 134Ps Implementation:Checklist for Success 14About the authors 15Simon-Kucher 15Thales 15The Essential 4Ps for

5、 AI Monetization Guide3According to research by Simon-Kucher at the end of 2023,94%of software companies were either developing or have already augmented their product with AI features.Everyone wants to offer this new technology to avoid the risk of being left behind.Yet,42%of companies that have re

6、leased AI products or features are not currently monetizing them,and even fewer are showing ROI(Source:Joe Floyd,Beyond Benchmarks 2024,Emergence(05/22/24).IntroductionThales Software Monetization and Simon-Kucher have teamed up to bring you this practical step-by-step guide to making your AI softwa

7、re offer profitable.Rooted in collective experience of 70 years in consulting and operationalizing pricing and packaging strategies,the experts from both organizations present the dos and donts of successfully bringing your AI offer to market.42%of companies that have released AI products or feature

8、s are not currently monetizing them,and even fewer are showing ROIOverview of AI offers todayHow many emails do you receive offering you an AI application for your personal or business use?Today,its impossible to avoid encountering a variety of Artificial Intelligence(AI)products or support systems

9、when browsing online.In fact,tech research and advisory company Gartner mapped it on their 2024 Hype Cycle,positioning AI at the“Peak of Inflated Expectations”(see below).At this stage of the cycle,both the industry and the media are caught up in excitement over AIs potential,envisioning the prospec

10、tive offers while often overlooking its drawbacks.Indirect monetizationwithout price increaseDirect monetizationwith separate feewith price increaseDirect monetizationABCMonetizationModelsAiMonetization strategy considerationsAbility to monetize indirectly(e.g.,in existing usage-based metric)Relevan

11、ce of your AI capability(i.e.,value for niche or mass)Variable cost(i.e.,high or low)The Essential 4Ps for AI Monetization Guide4Determining the monetization methodDetermining the appropriate monetization strategy for AI involves assessing whether to charge directly for the AI features or to monetiz

12、e them indirectly by offering AI for free and expecting returns in another way.This decision largely depends on three factors:the ability to monetize indirectly,the perceived value to the customer,and the variable costs associated with AI.If you find yourself like the 42%of AI vendors with a product

13、 but no ROI to show for it,this guide will help you determine relevant course corrections and maximize revenue by focusing on the 4Ps of MonetizationThe 4Ps of AI MonetizationAIMonetizationProductize01020304PackageProtectPriceThe Essential 4Ps for AI Monetization Guide5Productize your new AI capabil

14、itiesStandalone AI offer-Build a separate business New AI-native companies,such as Lunit and AiMedic,offer products entirely centered on AI capabilities.Existing companies can also opt to market standalone AI products under a separate AI-native brand.For example,Waymo,a subsidiary of Alphabet,operat

15、es under its own brand name and is a leader in AI-driven transportation.When creating an offer,you must consider what problem it solves,to establish differentiation from the inception.Pros:A standalone entity provides greater flexibility in structuring the company and marketing the product.Pricing a

16、nd packaging may also be more flexible if the entity is separate from the parent company.Adding AI to existing products-Incremental businessMany companies are also embedding AI features into existing offerings.Building on the medical example,lets look at what may happen when a CT scan manufacturer a

17、dds a cancer screening AI feature to their scanner.Pros:This approach can create synergies,such as leveraging the existing customer base to enhance the overall value proposition and make the product more appealing to customers.It can also tap into existing marketing campaigns to reduce promotional e

18、xpenditure.If your current pricing and packaging model is flexible,it may be easy to monetize the offer.Cons:Establishing a separate brand can be costly and may reduce operational synergy with the core business,affecting both operations and customer engagement.It will also require a separate means o

19、f communicating with customers under a different domain name.Cons:However,if the new capability is not recognized as truly innovative,it could fail to impact market positioning,and the additional cost may not be offset by increased direct revenue.Secondly,features which are dependent can impose cons

20、traints on teams and delay planned features or enhancements.Today,AI is commonly commercialized either as a standalone product or as an addition to an existing product Each has benefits and drawbacksProductize01The Essential 4Ps for AI Monetization Guide6Understanding and communicating your value dr

21、iver Customers can easily find AI offers that promise cost-savings,better insights,personalization,collaboration,and innovation.In the rush to join the GenAI trend,companies fail to sufficiently differentiate between the free chatbots or affordable premium versions(e.g.ChatGPT,Claude,etc).Solution:T

22、hink about the problem your existing software solves.Determine how your AI addition relates to that original purpose or addresses a related challenge.Only then will you add value to your existing product and have a compelling pitch to promote to your customers.Maintaining regulatory complianceEmergi

23、ng regulations,such as the EU AI Act,impose product costs and uncertainty on AI providers.These regulationsforce companies to implement robust risk management systems,transparency measures,and ethical guidelines.Solution:To ensure adherence to changing regulations,ongoing legal counsel will keep you

24、 on top of the limitations and standards for specific use cases(e.g.,HR/recruitment rules).Software licensing tools also play a role in compliance by maintaining accessible records of who is using which software or feature,and when.Data privacy,IP rights,and output accuracyTraining your model with d

25、ata that is protected under privacy laws or where IP ownership lies with third parties exposes you to risks of penalties and litigation.Crackers manipulating your AI application or model can lead to false or even dangerous outputs,which can harm your customers,your reputation,and put you at legal an

26、d financial risk.This risk is particularly high if your AI application is deployed in private clouds or on-premise.Solution:Carefully scrutinize your input data to be certain it meets privacy laws and doesnt infringe on others copyrights.To safeguard your output,utilize protection technology(e.g.obf

27、uscation and encryption)to secure the integrity of your AI application and model.Operational integration AI involves unique processes like data collection,model training,and deployment which can differ significantly from traditional business workflows.The resulting integration of your AI product can

28、 cause friction with existing operations which can affect your operational efficiency.From a customer perspective,an inadequate or poorly designed UI can negatively impact user experience(e.g.,having a separate log-in for the AI feature).Solution:Ensure that the AI features seamlessly integrate into

29、 your existing workflows and processes to optimize your operational efficiency.Standalone AI products should also mesh with your customers workflows for a better user experience.By addressing these considerations,you will conceptualize an AI product that stands out from the competition,appeals to us

30、ers,and meets government regulations.Considerations unique to AI productizationWhether you create a standalone offer or add AI features to your existing product,the key to productization is to differentiate your offer based on value drivers.Jumping on the AI bandwagon can backfire in that you may fi

31、nd that your costs exceed the benefits.It is important to consider these AI-specific issues when productizing your offer.Productize01The Essential 4Ps for AI Monetization Guide7Protect your IP investment(private cloud,on-prem or on the edge)ML models are costly to develop,making them attractive targ

32、ets for hackers and IP theft While cloud deployments often rely on the providers security,those with private cloud,on-premises and device deployments face numerous vulnerabilities Heres a look at three:Model poisoning and transfer learning attack Both model poisoning and transfer learning attacks re

33、place the authentic model with a modified version or a completely different model and can be accomplished through reverse engineering of the software.Solution:Encrypt the model and allow only the correct application to decrypt and use it.An encrypted model is basically useless without the correct de

34、cryption key(see“Layer 0”below).Input manipulation and output integrity attacks Because AI is an iterative process that continually learns from inputs,receiving and preparing the data for input to the ML model or post-processing of the ML model output are points of vulnerability.Therefore the integr

35、ity of the application needs to be protected.Solution:Safeguard the application against reverse engineering and modification.You can prevent these threats with sophisticated software protection tools found in advanced copy protection and licensing systems.(see“Layer 0”below).02Protect“Thales layered

36、 approach puts IP protection and application licensing at the foundation of an effective monetization strategy.Securing the IP investment enables organizations to more confidently leverage the additional layers to create new revenue opportunities.”Damien Bullot,GM Thales Software MonetizationThales

37、Software Monetization Layers01234Data InsightsDrive customer success and business intelligence initiativesFind new ways to sell,access new markets and customer segmentsCreate new differentiators without product investmentsCopy protection,and revenue protection Protection from IP theftNew Business Mo

38、delsFlexible PackagingLicensingIP ProtectionProtecting existing revenueGaining new revenue opportunities$Model theft/leapfrogging lost revenueOpen Web Application Security Project(OWASP)lists model theft in its top ten attacks on ML models.Uncontrolled ML use exposes your model to being copied and e

39、ven worse,puts you in danger of an extraction attack.A competitor can then use your ML to label their data training set and quickly overtake your competitive advantage.Solution:Protect against model extraction with a strong licensing platform that can also impose rate limiting,making it inefficient

40、for bad actors to use your technology to their own advantage.(see“Layer 1”above).The Essential 4Ps for AI Monetization Guide8Packaging for direct monetizationPackaging as a means of differentiationNot all organizations or individuals will need the same functionalities of an AI offering.Typically,ent

41、erprise companies will require a greater level of privacy and compliance while end users and SMBs may not need a very sophisticated package.Solution:To create valuable packages,analyze your customer base to identify segments that would benefit most from specific AI capabilities and structure their o

42、fferings accordingly.To support this flexibility,a robust software licensing and entitlements platform is crucial for operational efficiency.Operationalize packaging with software licensingCustomers have become accustomed to buying just what they need,demanding flexible packaging from their supplier

43、s.Software vendors aim to meet these new baseline expectations.However,implementing a flexible packaging offer can be a time-consuming and stressful manual process on the backend.Solution:Licensing protects your existing revenue by limiting access to your software in accordance with your commercial

44、terms and conditions.It is also necessary to implement entitlement management which determine the features,functionalities,and services a user can access.Entitlements decouple the license from the product code so you can mix and match different functionalities or even product offers into bundles tha

45、t meet a wide variety of customer needs,without the expense and development time to re-engineer your software for each iteration.Packaging is an effective product differentiator For established products,it is a low-cost way to grow revenue from existing resources There are multiple packaging models

46、that can serve as a powerful tool to maximize value and appeal Package03The Essential 4Ps for AI Monetization Guide95 widely used packaging models All-you-can-eat is common for young companies with few features to differentiate.There is often a trade-off between simplicity and flexibility when consi

47、dering packaging models.Good/Better/Best and Platform+Functional Packages typically strike a good balance between simple and flexible.These two approaches also enable clear communication of the distinct benefits of each package,ensuring customers understand the value at different price points.It sho

48、uld be immediately clear to prospects which package fits their needs.Companies must avoid overwhelming them with choice and avoid technical jargon unless it is appropriate for the audience.Functional Packages works well if the product offers vastly different use-cases,without logical growth path in

49、customer needs.Good/Better/Best is a tiered system that adds features and value incrementally,helping customers understand the benefits at each level,then upgrade as needs evolve.This works well if there is a logical growth path in customer needs.Platform+Functional Packages model works well when th

50、ere is no inherent growth path and offers the opportunity for more customized solutions.Its often chosen if there are common needs across customers,but without a logical growth path beyond that.Build-your-own works well in complex deals with long sales cycles where a lot of customization is needed f

51、or every customer.123These approaches typically allow for better acquisition and monetizationAll-you can-eatFunctional PackagesGood/Better/BestPlatform+Functional PackagesBase PlatformBuild-your-ownLeast flexible but simpleMost flexible but complexSimple ProductDoesnt monetize differences needs and

52、willingness to payAligns with needs of distinct use casesNo upsell pathServes different segments with simple up-sell pathFixed up-sell pathBroad product that allows for cross-sellMost complexMaximum flexibilty to serve different needsMost complexCommon Packaging Models45Package03All-you-can-eatFunct

53、ional PackagesGood/Better/Best Platform+Functional PackagesBuild-your-ownThe Essential 4Ps for AI Monetization Guide10Price your offer for profitabilitySolution:Choosing the direct monetization approach charging directly for the product rather than relying on incremental business through cross-sales

54、 and increased usage fees from existing products offers a better option to cover the direct costs for a new offering.However,there are instances where indirect monetization may be more effective.Computing Fixed and variableTraining complex machine learning models often requires significant one-time

55、CapEx investment in high-end computing hardware,such as GPUs and TPUs.However,the ongoing OpEx expense for cloud services like AWS or Azure will continue to increase with usage of your offer.Energy Fixed and variableAccording to Alex de Vries,a Dutch Data Scientist who spoke to The New Yorker on the

56、 subject,a single search with AI integration will consume 10 times more energy(3 KWh)than a regular search.Model optimization Fixed and variableOptimization involves improving response accuracy,training the model,and fine-tuning it.This represents a large upfront and ongoing cost necessary to ensure

57、 that the models deliver the expected performance.Licensing LLMsVariableUtilizing existing capabilities like OpenAIs GPT models can become costly.Almost all the largest GPTs follow a consumption pricing model,where your expenses increase with higher user adoption.1 The costs of bringing AI offers to

58、 market AI is flipping cost structures upside down.Traditionally,R&D intensive industries,such as software,have high fixed costs and relatively lower variable costs.Companies that sell GenAI solutions are surprised to find the opposite higher variable compared to fixed expenses.Here are the typical

59、costs associated with creating an AI offer.Rushing to market without planning for the unique challenges of pricing and packaging can lead to pricing below costs and therefore lead to significant financial losses Here are 5 key challenges to consider:Example of Direct Monetization:Intercom Fin-AI-pow

60、ered customer service botIntercom Fin is an AI-powered customer service bot add-on that automates and resolves inquiries by providing instant answers from various sources It supports multiple languages and escalates complex issues to human agentsPricing is based on a per-resolution model,charging$09

61、9 for each successfully resolved query,in addition to fees for other products In the first half of 2024,17%of product purchases reportedly included the Fin add-on”Source:https:/ high-quality,relevant data sets for ML training can be expensive.Depending on your product,you might need to purchase acce

62、ss to labeled data or dedicate resources toward in-house data labeling.For example,Bloomberg invested over$10M to develop a large language model(LLM)tailored for financial tasks based on its own curated data and third-party data.Data acquisition Fixed and variable04PriceThe Essential 4Ps for AI Mone

63、tization Guide112 Earning returns on AI investment via cross-selling and upsellingIndirect monetization occurs when AI features enhance the overall product offering,leading to increased conversions through cross-selling and upselling,or higher usage-based pricing.Achieving success requires a variabl

64、e cost structure that allows high-intensity usage to remain profitable.Beyond the immediate financial returns,this method can help you remain competitive and relevant in a rapidly evolving market.Solution:It is important that indirect monetization be approached as a deliberate strategy rather than a

65、s a by-product of adding an AI capability.Pros:Usage-based pricing overcomes price as a barrier to entry as customers find paying for what they use is fair and transparent.Vendors are compensated for the value their AI delivers and ensure high variable costs dont cut into profit margins.3 AI efficie

66、ncy impacts user-based pricing A key concern with AI adoption is the potential reduction in user numbers if AI automates tasks traditionally performed by humans.This will directly impact revenue under a user-based model,as there are fewer users to bill.Solution:Usage/Consumption-based pricing This m

67、odel charges customers based on their usage of AI services,such as the number of API calls,data processed,or computational resources consumed.The caveat is the“taxi meter effect,”which causes customers to be conservative in their usage for fear of paying too much.Operationalizing usage-based softwar

68、e licensing can help mitigate this issue.Users can purchase a set amount of software license tokens upfront and use these to“pay”for different AI functionalities.The usage metering license then tracks the number of tokens consumed by each customer.As customers deplete their bundle of tokens,theyre o

69、ffered the option to buy more,allowing vendors to generate recurring income streams without putting undue pressure on clients.Cons:One challenge in executing this model is setting up a system to track actual usage in real-time to ensure customers do not exceed their token limit.Example of Indirect M

70、onetization:B-AI Trip PlannerBookingcom aimed to increase revenue through cross-selling additional services alongside accommodations The online travel agency launched a travel planning tool that,with the help of GenAI,encourages consumers to book additional services,such as flights or rental cars th

71、rough the platformThis chatbot tool provides personalized travel suggestions,answers destination-specific questions,and helps plan entire holidays,including transportation and additional activitiesNo fee is charged to the user as revenue is generated through the backend04PriceThe Essential 4Ps for A

72、I Monetization Guide124 Revenue instabilityRelying solely on usage-based or outcome-based pricing can lead to significant revenue volatility,particularly if customer usage patterns or the outcomes achieved with your product fluctuate widely.This unpredictability can create challenges in financial fo

73、recasting,budgeting,and resource allocation,hindering your ability to plan for growth and investment.Solution:A hybrid pricing model offers a way to mitigate the risks associated with revenue instability by combining elements of both fixed and variable pricing.This approach allows you to:establish a

74、 predictable revenue base.The fixed component,such as a monthly subscription fee or a minimum commitment,ensures a consistent stream of income,even during periods of low usage or outcomes,providing a safety net for your business and facilitates better financial planning.Pros:The hybrid pricing model

75、 allows you to meet customer flexibility demands and still maintain a consistent revenue stream.Cons:With complexity comes possible customer confusion if not presented clearly.Internally,it may incur administrative costs to track usage,calculate variable charges,and communicate billing details to cu

76、stomers.5 Pricing sensitivityThere are two types of AI prospective customers who may be hesitant to pay for the offer.The first group are those who have used a free AI service and might expect all AI products to be offered at no charge.The second group may have difficulty grasping the potential valu

77、e of an AI product.Solution:Value or outcome-based pricing is growing in popularity among AI providers as a way to address these two groups.It overcomes resistance to higher AI pricing by aligning the providers success with the customers success.However,because success differs between customers,dete

78、rmining the right metrics can be challenging and therefore measuring performance may be difficult.Certain industries lend themselves more readily to showing measurable metrics that can be agreed upon at the outset.These metrics include increased revenue,cost savings,and productivity improvements.Fle

79、xible,segment-based packaging allows you to adjust price points according to the perceived value in different customer segments.Pros:This model demonstrates clear value and provides a rationale for higher pricing.Cons:The challenge lies in effectively implementing and accurately measuring the necess

80、ary metrics.Imagine a customer support platform that charges customers strictly per query Customers may be pleased to only pay when they are using the platform,but the ISV will find that during peak periods(eg product launches)inquiries will surge,leading to a spike in revenue However,during quieter

81、 periods,usage may dwindle,causing a sharp drop in income Such fluctuations can make it difficult to sustain consistent cash flow and maintain financial stability Hence the appeal of a hybrid pricing modelAI-powered customer supportExample of Revenue Instability:04PriceThe Essential 4Ps for AI Monet

82、ization Guide13Evaluating pricing effectivenessBecause AI is in the nascent product development stage,offerings and market forces will quickly change.Pricing should be flexible enough to reflect this dynamic environment.Both product-led and sales-led companies will need to make evolving pricing deci

83、sions based on tracking a range of KPIs,doing frequent pricing tests,and conducting customer research.Sales-led companies have the additional advantage of leveraging valuable insights from their sales teams,such as win/loss data and discounting patterns,to further refine their pricing strategy.A stu

84、dy by Simon-Kucher demonstrates that companies making frequent incremental course corrections to their pricing(e.g.small adjustments on a quarterly or bi-annual basis)typically achieve higher growth than those that do not.Turn investments into profits There is no doubt that AI offers enormous opport

85、unities.At the same time,there is also increasing pressure from investors.Gartner predicts that“at least 30 percent of GenAI projects will be abandoned after proof of concept by the end of 2025 due to poor data quality,inadequate risk controls,escalating costs,or unclear business value.”To mitigate

86、your risk of falling into that 30 percent,ensure you build a sustainable business model.Number of usersNumber of instancesFeature set includedNumber of usesNumber of transactions processedCompute/storage consumedMeasurabilityRevenue stabilityAligment to valueAbility to ofset costs MeasurabilityReven

87、ue stabilityAligment to valueAbility to ofset costs MeasurabilityRevenue stabilityAligment to valueAbility to ofset costs Flat Rate Pricing ModelConsumption Pricing ModelValue BasedPricing ModelStatic MetricsUsage Metrics Incremental revenue generatedSavings from cost efcienciesNPS improvementOutcom

88、e MetricsHow To Choose A Pricing Model For Your AI OferIdentify the unit you are charging for Define the pricing model Consider business implications 04PriceThe 4Ps of AI MonetizationAIMonetizationProductize01020304PackageProtectPriceThe Essential 4Ps for AI Monetization Guide144Ps Implementation:Ch

89、ecklist for SuccessProductize01Add value and be different-dont be a“GPT wrapper,”but differentiate yourself with AI-driven value driversUnderstand the regulatory issues and establish strict privacy protocols from the beginning02ProtectEncrypt your ML model to avoid reverse engineering and malign cra

90、ckingUtilize strong licensing to prevent software from purposeful or inadvertent overuseEnsure your price reflects a well-articulated value and covers the unique AI costsPlan for flexible pricing models to evolve with market conditions and capture wide market reach,whether they are usage-based,outco

91、me-based,or hybrid models04PriceConsider your packaging structure as a means of differentiation,balancing simplicity and complexityOperationalize your packaging with a robust software licensing and entitlement management systemPackage03The Essential 4Ps for AI Monetization Guide15About the authors T

92、hales Todays businesses and governments depend on the cloud,data and software to deliver trusted digital services.That is why the most recognized brands and organizations around the world rely on Thales to help them protect sensitive information and software wherever it is created,stored or accessed

93、 from the cloud and data centers to devices and across networks.As the global leader in data security,identity&access management,and software licensing,our solutions enable organizations to move to the cloud securely,achieve compliance with confidence,create more value from their software and delive

94、r seamless digital experiences for millions of consumers every day Simon-KucherSimon-Kucher is a global consultancy firm with over 2,000 employees in 30+countries focused on unlocking better growth that drives measurable revenue and profit for our clients.With nearly 40 years of experience in moneti

95、zation topics of all kinds,we are regarded as the worlds leading pricing and growth specialist.Contributing Authors:Sara Yamase is a Partner and Head of the Software,Internet,and Media practice at Simon-Kucher.Her consulting work focuses on new product pricing,product positioning and commercializati

96、on strategy,and price optimization for software and internet companies.David Klemperer and Gregor Biljardt are experts in Simon-Kuchers Software,Internet,and Media practice in the US.They help power value creation,growth,and long-term profit for clients.Contributing Authors:Michael Zunke is the Chie

97、f Technology Officer for Software Monetization at Thales where he is currently focused on how to secure intellectual property in machine learning.With over 30 years of experience in Software Protection and Licensing technology,he holds several valuable patents in software protection and reverse engi

98、neering.Johanna Rose heads up Thales Software Monetization global product marketing team.She works with product and account management to articulate the benefits of Thales Sentinel.For more information visit:Master GenAI monetization for sustainable growth(simon-)For more information visit:Software Monetization Solutions by ThalesContact usFor all office locations and contact information,please visit Thales-November 2024MB V14