Use Case Prioritization Framework for AI Merchandise

Firms turning to synthetic intelligence (AI) for enterprise options face a tricky resolution: Which use instances will convey probably the most enterprise worth? AI adoption is usually costly and complex, putting organizational leaders at odds with each other on which use instances to prioritize. In response to Gartner, 73% of CIOs say their organizations are growing AI funding in 2024, whereas 67% of CFOs report that AI initiatives have underperformed expectations, revealing a harmful fault line within the AI panorama.

To assist AI product managers and firm stakeholders attain a consensus on AI options that meet or exceed expectations, I’ve developed a new use case prioritization framework referred to as the Gen AI Strategic Alignment and Influence Framework (GSAIF). Because the title suggests, I designed the framework for generative AI (Gen AI) use instances particularly. Frequent frameworks (e.g., the impact-effort matrix or the cost of delay model) are usually rooted in monetary metrics and battle to seize the distinctive traits of Gen AI, such because the potential for exponential progress, the speedy tempo of technological change, and novel moral concerns.

GSAIF entails a two-phase prioritization course of that begins with a qualitative screening adopted by an in depth multicriteria analysis utilizing a weighted scoring mannequin. For instance how the framework features, I current a real-world case examine alongside a step-by-step rationalization of the tactic I developed. The case examine focuses on a small e-commerce enterprise that was trying to find AI options to challenges that impeded the corporate’s progress and skill to interact and retain clients successfully.

This text offers an in depth walkthrough of the impediments the enterprise confronted, the potential AI options recognized by stakeholders, and the GSAIF assessment that enabled us to guage and prioritize options. Because the case examine reveals, the answer we chosen for product improvement yielded a huge effect for the corporate, growing its buyer retention price by an element of three. I encourage product managers and different enterprise leaders who’re dealing with the identical AI adoption challenges to make use of the GSAIF template accessible on the finish of the article.

Step 1: Establish the Enterprise Drawback

The e-commerce firm on the heart of this case examine is predicated in the US and provides greater than 5,000 merchandise throughout a number of classes, together with attire, electronics, residence items, and sweetness merchandise. On the outset of its AI adoption course of, the corporate recognized 5 key areas requiring enhancements to reinforce the shopper expertise, streamline operations, and, finally, enhance profitability.

The challenges included:

• Lack of a personalised buyer expertise: The enterprise struggled to supply a personalised purchasing expertise, resulting in a buyer retention price of solely 20%, considerably under the trade common of nearly 30% for e-commerce manufacturers.
• Inefficient customer support: The enterprise’s customer support mannequin was not outfitted to deal with queries effectively, leading to a mean wait time of 10 minutes for buyer help through channels like chat or cellphone, resulting in a buyer dissatisfaction price of 40%.
• Suboptimal product descriptions: Based mostly on an Search engine optimisation evaluation, employees recognized that product descriptions on the e-commerce platform had been too generic and failed to interact potential patrons. Solely 25% of product pages had descriptions that successfully matched search queries.
• Insufficient stock administration: The corporate confronted challenges in precisely predicting inventory ranges, resulting in overstock conditions for 30% of its stock and stockouts for 20% of its hottest gadgets.
• Overly broad advertising efforts: Advertising methods weren’t sufficiently focused, resulting in a decrease ROI on advertising spend with a return of simply $2 for each $1 spent, in comparison with a standard industry benchmark of $4.

Step 2: Brainstorm Potential Generative AI Options

To deal with the enterprise challenges the corporate had recognized, I used to be introduced in as a strategic product advisor to steer a three-hour brainstorming workshop with enterprise stakeholders. Throughout this collaborative session, we explored the potential of generative AI to remodel the corporate’s operations, making an allowance for its current know-how infrastructure and knowledge availability.

We recognized the next potential product initiatives as related to every enterprise problem:

• Personalised suggestion system: A system that leverages AI for buyer knowledge and generates customized product suggestions on the webpage might improve the person expertise and improve gross sales.
• Chatbots for enhanced customer support: AI-powered chatbots might present instantaneous, 24/7 buyer help and customized purchasing help, thereby enhancing buyer satisfaction and operational effectivity.
• Content material technology for product descriptions: Generative AI instruments may very well be used to create distinctive, participating, and Search engine optimisation-friendly product descriptions, which might assist enhance product visibility and conversion charges.
• Stock administration: AI algorithms may very well be carried out to precisely forecast demand and optimize stock ranges, lowering the incidence of overstock and stockouts and enhancing provide chain effectivity.
• Personalised e mail advertising campaigns: AI may very well be used to investigate buyer conduct and craft extremely focused advertising methods by means of e mail drip campaigns, thereby growing the effectiveness of selling efforts and enhancing buyer engagement.

Different concepts emerged throughout the brainstorming session, however we chosen these 5 Gen AI use instances for additional analysis based mostly on their potential to instantly tackle the recognized enterprise challenges.

Step 3: Conduct the Preliminary GSAIF Screening Section

Now that we had recognized 5 promising Gen AI use instances for the e-commerce firm, a brand new problem arose: We would have liked to find out which of those product choices to prioritize. It is a widespread dilemma when assets are restricted—and the incorrect selection will be expensive.

The preliminary screening stage of the framework’s two-phased method was carried out throughout a portion of the centered workshop during which we assessed the use instances qualitatively, evaluating the next elements on a scale of low, reasonable, or excessive:

• Feasibility: What’s the ease of implementation given our present know-how and assets? Are there any main roadblocks or limitations, together with compliance with rules and moral concerns?
• Value: What’s the estimated price of growing and implementing this answer? Does it match inside price range constraints?
• Influence: How a lot potential does this use case should affect the enterprise positively? Will it transfer the needle on key metrics?
• Alignment with enterprise targets: Does this use case instantly tackle strategic targets? Does it align with total imaginative and prescient and mission?

When screening use instances at this stage of the strategic prioritization framework, the significance of every issue can differ relying on the particular context and the group’s strategic targets. As an illustration, the stability between price versus affect might differ from enterprise to enterprise. Is it higher to have a moderate-cost, moderate-impact answer? Or would it not be higher to concentrate on one that’s excessive price however excessive affect? There’s no one-size-fits-all reply, as the perfect stability relies on a number of elements, together with:

• Funds constraints: Organizations with restricted budgets might prioritize lower-cost options with reasonable affect, aiming for fast wins and tangible outcomes.
• Threat tolerance: If a corporation is comfy with larger threat, it might put money into high-cost, high-impact initiatives with the potential for vital returns.
• Strategic targets: The alignment of a use case with overarching strategic targets can typically outweigh price concerns, particularly if the potential affect is transformative.
• Time horizon: Assembly short-term monetary targets would possibly privilege lower-cost initiatives, whereas implementing long-term progress methods would possibly justify larger up-front investments for doubtlessly higher long-term affect.

To account for these concerns, I collaborated with stakeholders on the e-commerce firm to find out the relative significance of every issue within the decision-making course of. This screening ensured that we centered assets on product improvement alternatives with the best potential for fulfillment and alignment with the enterprise’s long-term targets. Based mostly on this complete analysis, we collectively recognized three of the 5 use instances for added analysis:

• Chosen use case 1: Personalised suggestion system
• Chosen use case 2: Chatbots for enhanced customer support
• Chosen use case 3: Content material technology for product descriptions

We additionally screened out two use instances that weren’t strategically aligned, ethically sound, technically possible, or compliant with related rules:

• Nonselected use case 1: Stock administration
• Nonselected use case 2: Personalised e mail advertising campaigns

Within the sections under, I clarify the GSAIF screening outcomes for every of those 5 use instances.

Chosen Use Case 1: Personalised Advice System

We chosen this use case attributable to its excessive potential to instantly affect buyer engagement and retention, aligning with the strategic aim of enhancing buyer satisfaction and loyalty. By leveraging buyer knowledge to generate customized product suggestions, the system addressed the problem of offering a personalised purchasing expertise, which is essential for a small e-commerce enterprise seeking to differentiate itself in a aggressive market.

Chosen Use Case 2: Chatbots for Enhanced Buyer Service

We additionally decided that customer support chatbots warranted extra analysis. This prioritization was grounded within the answer’s excessive feasibility, low price, and vital affect on buyer satisfaction. Chatbots might present instantaneous, 24/7 buyer help and customized purchasing help, instantly addressing the inefficiency of the present customer support mannequin. We additionally appreciated that chatbots provided customization choices that may enable the corporate to stick to rules and tackle moral concerns, reminiscent of transparency about AI utilization. Additionally, a customer support consultant might present fallback help, within the occasion that clients encountered points with the chatbot expertise. This answer promised comparatively excessive ease of implementation and instant advantages in operational effectivity.

Chosen Use Case 3: Content material Era for Product Descriptions

Based mostly on the GSAIF screening standards, we chosen automated content material technology for product descriptions because the third use case for added analysis. We acknowledged its potential to reinforce product listings with distinctive, Search engine optimisation-friendly descriptions at a low price, instantly addressing the problem of suboptimal product descriptions. This use case goals to enhance conversion charges by offering compelling product data whereas aligning with the GSAIF’s emphasis on innovation and aggressive benefit. Furthermore, the use case seemed to be fairly possible, additional supporting its choice.

Nonselected Use Case 1: Stock Administration

We decided that AI-enabled stock administration didn’t warrant extra analysis, regardless of its potential to enhance provide chain effectivity by precisely predicting inventory ranges. For a small enterprise, the numerous funding required for implementation and the challenges related to integrating superior AI algorithms into current stock programs outweighed the instant advantages, as did the necessity for compliance with knowledge rules and moral stock practices.

Nonselected Use Case 2: Personalised Electronic mail Advertising Campaigns

Though customized advertising campaigns are helpful for concentrating on particular buyer segments and enhancing advertising ROI, we decided that this use case can be much less instantly impactful in comparison with different choices, reminiscent of automated content material technology for product descriptions. A multifaceted evaluation additional solidified this resolution. Whereas each use instances held promise, automated content material technology provided a extra instant and direct answer to the urgent challenge of suboptimal product descriptions, aligning intently with the corporate’s in-the-moment wants and useful resource constraints.

Step 4: Conduct the GSAIF Detailed Analysis Section

Following the preliminary three-hour workshop, during which we recognized three use instances that promised to have the most important affect on the e-commerce firm’s wants, my e-commerce shopper and I started the second section of GSAIF. This section entails in-depth analysis, knowledge assortment, and asynchronous enter from stakeholders to populate a multicriteria scoring matrix that considers elements like scalability, innovation potential, and market viability.

The detailed analysis permits for a complete evaluation of every AI use case to make sure that the last word prioritization would align with the group’s targets and ship tangible, moral, and compliant worth.

This prioritization matrix used a scaled scoring system (1 to 10) based mostly on the affect degree for eight important standards:

1. Person demand and worth proposition: What’s the market demand for the proposed Gen AI answer, and what worth does it supply to the person base? Does it align with buyer wants and preferences?
2. Value and ROI evaluation: What are the monetary implications of implementing the Gen AI answer? What are the event, deployment, and upkeep prices? What’s the potential return on funding by means of elevated effectivity, income, or different advantages?
3. Knowledge availability and high quality: What’s the accessibility and high quality of the info required to coach and function the Gen AI mannequin? Are there potential challenges in acquiring or sustaining high-quality knowledge?
4. Scalability and integration: How simply can the answer be built-in into current programs, and the way effectively can it scale because the enterprise grows?
5. Aggressive benefit: Does the use case supply a novel worth proposition or aggressive edge available in the market?
6. Operational affect and effectivity: How considerably will this answer enhance operational effectivity and streamline processes?
7. Threat evaluation: What are the potential dangers related to this use case, and the way can they be mitigated?
8. Market developments and buyer insights: Does the use case align with present market developments and tackle buyer wants and preferences?

Every issue was then weighted in line with its strategic significance for the particular state of affairs. The weights assigned to every aren’t mounted and might differ relying on the character of the enterprise and its particular strategic targets. As an illustration, a consumer-facing enterprise would possibly assign extra weight to person demand and worth proposition, given buyer suggestions or person expertise insights. In the meantime, a B2B group would possibly contemplate operational affect and effectivity to be extra vital.

On this case, we chosen the next weights:

• Person demand and worth proposition and aggressive benefit had been every weighted at 15%, reflecting the significance of selecting an answer that resonated with clients and differentiated the enterprise available in the market.
• Value and ROI evaluation and operational affect and effectivity had been additionally weighted at 15% every, emphasizing the necessity for a financially viable answer that would enhance the corporate’s operations.
• The 4 remaining standards—knowledge availability and high quality, scalability and integration, threat evaluation, and market developments and buyer insights—had been every weighted at 10%, acknowledging their significance however giving them much less precedence in comparison with the elements talked about above.

Thus, if we assigned a rating of 10 for person demand and worth proposition, it might contribute extra to the general rating than a ten assigned for threat evaluation.

The dedication of those weights was a collaborative course of, involving discussions with stakeholders to make sure alignment with their priorities and the general enterprise technique. It’s vital to notice that whereas the weighted scoring mannequin supplied a quantitative framework, the task of scores and weights concerned each quantitative knowledge evaluation and qualitative judgment calls, knowledgeable by stakeholder enter and unbiased analysis.

The result of the second section of the GSAIF assessment is a rigorously quantified rating for every use case. This single rating permits product managers and different decision-makers to prioritize an AI answer with the best strategic worth and market relevance, making certain the ultimate resolution is well-informed and aligned with the enterprise’s long-term targets. On this case, the analysis course of allowed us to rank the three use instances chosen throughout the screening section within the following order:

• Highest analysis rating: Personalised suggestion system
• Intermediate analysis rating: Chatbots for enhanced customer support
• Lowest analysis rating: Content material technology for product descriptions

Within the following sections, I stroll by means of the analysis outcomes for these three potential approaches.

Highest Analysis Rating: Personalised Advice System

The AI-driven customized suggestion system earned an combination analysis rating of 8.15, reflecting its sturdy alignment with market calls for for tailor-made purchasing experiences. This method additionally provided vital aggressive benefits by differentiating the enterprise in a crowded market; we additionally decided that the potential for elevated gross sales and improved buyer retention would justify the reasonable preliminary prices. Nonetheless, challenges reminiscent of sustaining knowledge high quality and privateness and the necessity for steady system updates would current ongoing concerns for the product technique.

Intermediate Analysis Rating: Chatbots for Enhanced Buyer Service

With an combination rating of seven.15, this use case got here in second, highlighting the significance of modernizing buyer interactions. We acknowledged that AI chatbots would enable the corporate to reply to the excessive demand for immediate help in digital environments, enhancing person satisfaction by means of speedy question decision. Ease of implementation and scalability throughout varied platforms makes chatbots a cheap answer for enhancing service effectivity. Nonetheless, the variability in knowledge high quality and the potential for technical failures would require cautious administration to keep up service reliability and personalization.

Lowest Analysis Rating: Content material Era for Product Descriptions

We decided that utilizing AI content material technology for product descriptions might capitalize on the excessive demand for participating and detailed on-line content material essential for attracting and retaining e-commerce clients. But the mixture rating of 6.85 for this use case was the bottom ranking amongst our three contenders. Whereas an automatic content material technology system would supply vital operational efficiencies by automating content material creation, the return on funding would rely closely on the content material’s affect on gross sales and Search engine optimisation. We acknowledged that the corporate might face challenges making certain the accuracy and relevance of the generated content material, provided that the method would necessitate ongoing supervision and integration with current e-commerce programs.

Step 5: Implement and Consider Outcomes

Based mostly on the detailed analysis utilizing GSAIF, the e-commerce firm elected to pursue an AI-driven customized suggestion system, given it had emerged as probably the most appropriate selection for addressing the enterprise’s challenges with buyer engagement and retention. This prioritization was not solely based mostly on its combination rating of 8.15—the best of the three finalists—but in addition on the great evaluation and collaborative discussions facilitated by the GSAIF analysis course of.

Armed with a transparent understanding of the issue and a well-defined product imaginative and prescient, the corporate’s improvement workforce launched into a proof-of-concept implementation of the AI-driven customized suggestion system. The outcomes had been outstanding, with the shopper retention price growing from 20% to a whopping 60%—double the trade common. This dramatic enchancment underscores the effectiveness of GSAIF in figuring out high-impact options that align with each enterprise targets and buyer wants.

A distinct consequence would doubtless have occurred if the corporate had relied on conventional prioritization frameworks. Frequent strategies usually concentrate on short-term monetary metrics and may need led to a unique selection, reminiscent of prioritizing chatbots for his or her instant price financial savings, perceived effectivity beneficial properties, and ease of implementation. Nonetheless, the great and multifaceted method of GSAIF enabled the corporate to acknowledge the transformative potential of customized suggestions in driving buyer loyalty and progress over an extended timeframe.

GSAIF Template: Attempt This Strategic Prioritization Framework

The success of this real-world instance showcases the facility of GSAIF as greater than only a use case prioritization framework. The GSAIF course of fosters a shared understanding of the issues and potential of AI amongst various stakeholders. This alignment, coupled with the readability supplied by the detailed analysis course of, empowers firms and product managers to confidently navigate the complexities of AI adoption and put money into options that yield tangible and significant outcomes.

To activate these insights and leverage GSAIF’s full potential in your group, I invite you to obtain the comprehensive GSAIF template. Use the template to start your journey towards strategically aligning and prioritizing your Gen AI initiatives.