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GenAI has broad applicability across the marketing cycle and will have a significant impact on how future customer experiences will be designed, delivered, and scaled.

Future value exchanges between customers and brands will be premised on data and customer insights. In turn, the imperative for earning and sustaining customer trust increases while accountability for the security of customer data and respect for customer privacy unequivocally falls on the enterprise. 

As customer demands and expectations continue to rise, additional pressure is placed on relational aspects of the experience life cycle, such as delivering empathetic customer outcomes, proving customer value, and mitigating churn risk. By allowing better personalization and contextualization in customer-facing content, GenAI has the potential to improve — and create — the experiences customers have with businesses.

35% of customer experience (CX) executives agree that the acceleration of innovative capabilities such as GenAI and Web3 will most impact future CX strategy.

The C-Suite – particularly CMOs – must start with an assessment of their business needs before considering which technology or product to adopt through GenAI consulting services. The aim of these consulting services is to help organizations understand the potential for GenAI technology to reduce operational costs, cut time to market for new products and services, grow existing revenue streams, and identify and drive new revenue streams. They can help with ideating, prioritizing, triaging, piloting, and later scaling GenAI use cases across the organization.

IDC predicts that by 2026, 45% of the Global 2000 will use AI/ML to elevate context and nudge customers into unfamiliar and novel experiences that simultaneously improve sentiment metrics and brand upselling potential, and GenAI will play a role in this transformation.

Some of GenAI’s potential uses — and risks — are still being worked out; CMOs should paradoxically take an adoption approach that is both bold and cautious. Bold in the sense that the organization should experiment with something that is immature and has the potential for misuse, financial damage, and even brand degradation. Cautious in the sense that this experimentation should be done with close oversight and strong guardrails.

Because GenAI is a new and disruptive technology, ready-made uses cases may not be available, or they may have little short-term and medium-term performance data to validate the likely return on investment for the enterprise (let alone any long-term data). In these cases, adoption may start from a different — and more ideational and experimental — perspective, as organizations ask what the business benefits of the new technology could be.

Two of the biggest benefits of GenAI to enterprises are cost reduction and speed. Because GenAI is still maturing as technology and is in the nascent stages of adoption by enterprises, metrics are not standardized and formalized.

For these reasons, it’s a good idea to seek advice, project management, and implementation expertise from business and IT consultancies that have experience with AI and organizational change.

In terms of cost, GenAI obviously has the potential to reduce spending on human activity in parts of the marketing cycle, notably data-heavy research, reporting and analysis, and content generation and management. GenAI’s greatest potential benefit for marketing may be its ability to empower organizations to update existing content and re-factor it for new contexts of new distribution channels.

In terms of speed, GenAI’s nature as a relentless, always-on technology with massive processing power gives it the potential to significantly shorten delivery cycles for data-intensive work, such as market research, reporting, and analysis and for the production of creative content in draft form.

As with any new technology, there are benefits, but there are also risks. There are two forms of risk for GenAI: generic risk associated with any business and technology change project and risks specific to GenAI.

Generic risks include the possibility that the business case for GenAI may be miscalculated, that the technology may not perform as it should, is not configured properly, and that the “human side” of the project is not addressed adequately. For example, if training is inadequate, the purpose of the project is not explained to ground-level users of the new applications, or stakeholders do not buy into the project, they will not have success.

Risks that are specific to GenAI relate in part to its combination of immense processing power, unpredictability, and its ability to mimic human communication.

Offensive and brand-degrading content. GenAI does not truly understand human ideas and emotions and because it cannot make the culturally and politically nuanced decisions that humans make, it can create inappropriate and offensive and brand-degrading content.
Over-personalization. If GenAI is used at scale to produce personalized content it is possible that it will flood recipients with content that is both too frequent and possibly too personalized, perhaps even “creepy”.
Environmental cost. Widespread GenAI use may entail significant levels of net-new datacenter and network usage, which would not sit well with any public commitments your brand has made to safeguarding the environment.
Sameness. This is not a cheap technology to produce and train at scale, and it’s possible that enterprises will use GenAI services that are based on just a handful of large language model (LLM) providers globally, which can produce text and images in styles that are recognizably similar.  Consumers will quickly spot GenAI produced content and may come to discount this content, reducing its powers.
Bias. Because GenAI is trained on large volumes of existing content, it can adopt the cultural, racial, or sexual biases of this content, lessening the ability of a brand to communicate with certain social groups.
Legal liability. Because GenAI content is based on large data sets of existing data, there may be legal issues surrounding copyright laws.
Organizational conflict. GenAI has the potential to enrich existing jobs, but it also has the potential to deskill and degrade people’s experience of work and to reduce the perceived quality of the output of their work.

As organizations prepare to deploy GenAI  they must think through the technology change and process, workflow, and organizational structural changes that will be required to make best use of its business potential. It will also require close attention to the “human side” of the changes that GenAI will bring.

Recognize Indirect Partner Value in Indirect Sales Channels

As more vendors and channel leaders recognize and accept how indirect value and demand generation impact the wider ecosystem, measures of partner success are being redefined. Revenue-centered metrics remain critical, however, traditional one-size-fits-all approaches are giving way to more nuanced and intelligent ways of recognizing and rewarding partners. By 2025, IDC predicts that two-thirds of vendors will deploy an incentive stack framework that recognizes different partner roles and rewards them intelligently.

Simply put— separate partners can play specific roles with different levels of value attached at various stages in a single customer journey. As a result, the value partners create is no longer viewed as solely tied to revenue generation; it extends across different spheres of influence, transaction, consumption, and optimization (see Figure 1).

Figure 1: Sample Ecosystem Sales Model

This complexity arises from the multitude of sales motions, purchase models, and solution deployment options available to customers. As a result, incentive stack models must be capable of allocating value across the entire customer lifecycle. The allocation depends on the type of solution, deployment model, partner involvement, and the relative importance of each function within the customer journey.

The Evolving Channel and Incentive Models

As we move towards everything-as-a-service and subscription models, traditional incentive models are losing relevance. Many vendors find themselves in the challenging position of maintaining relationships with partners who have built their business models around the traditional incentive stack while adapting to the changing demands of partners with next-generation incentive stacks.

In this evolving landscape, the concept of partner value will evolve beyond the simplistic assessment of revenue and sales volume and extend into spheres of influence and long-term value creation. Partners who prioritize building lasting relationships with customers will see increasing value attached to their roles as financial metrics within the incentive stack are recalibrated. 

To keep pace with these changes, vendors must encourage and facilitate the detachment of partner roles within the ecosystem. This detachment can lead to improvements in customer acquisition, retention, and overall experience.

Recognizing Influence as a Valuable Role

One of the most significant shifts in partner value metrics is the recognition that some partners influence customer purchasing decisions without desiring the transaction or consumption revenue on their books. This influence role is complex and challenging to quantify. It often involves processes like partner of record and deal registration, which are already surrounded by significant complexity. 

Consider a distributor, for example, acting as an influencer by generating customer demand through partners, while procurement and provisioning occur through a cloud marketplace. The value of SaaS-based Independent Software Vendors (ISVs) within cloud platform partner ecosystems often stems from the indirect demand generation they drive through underlying Infrastructure as a Service (IaaS) cloud consumption. 

Ensure Continuity with Transactionally Focused Partners

Partners that drive transactions and revenue continue to play a pivotal role, particularly in the context of CapEx-based technology spending by customers. However, the shift toward OpEx-based technology spending is becoming the driving force behind the design of “next-generation” incentive stacks. 

Some vendors are currently navigating both worlds simultaneously. Still, there’s a growing acceptance that creating relevant and future-proof partner value metrics is essential for the overall health and vitality of the wider partner ecosystem. 

For vendors, it’s imperative to ensure that the evolution of the partner incentive stack aligns with anticipated or planned changes in customer purchasing and deployment models. Building flexibility into the incentive stack allocation between partners remains crucial as models continue to evolve.

Vendors must also inform existing transaction-focused partners of proposed changes to the incentive stack and create pathways that enable them to transform their businesses in alignment with shifts in customer behavior and preferences. The intelligent allocation of the incentive stack, based on partner function and the value this provides to both customers and vendors, is the cornerstone of an adaptable, flexible, and future-focused ecosystem-based partnering approach. 

Inform Your Incentive Models with IDC

The future of partnership strategies for enterprise tech vendors is rapidly evolving. As vendors look to gain a more nuanced understanding of how changing partner activities create value across customer lifecycles, it is critical to examine partnership dynamics both internal and external to your own ecosystem. 

IDC’s Channel Partner Ecosystem (“CPE”) provides an in-depth view of the channels and partners of tech vendors, giving you insight into competitors’ partnerships, areas of technology coverage, industries served, geographic location data, and much more. Learn more about IDC CPE here.

While the journey toward redefining partner value metrics is already underway, it’s one that all enterprise tech vendors should be prepared to navigate. To help get you started, be sure to check out IDC’s Starter Guide: Modernize Your Incentive Stack for Channel Partners.

Without a doubt, 2023 has been the year of generative AI (GenAI). Its different applications offer every part of every organization the opportunity to be more productive. This is especially true for emerging and expanding tech startups and entrepreneurs, who often juggle many balls with way too few resources.

“As industries transition to use GenAI to automate or augment every process, project, or interaction, the measure of success will be in improving productivity and reducing the level of effort to complete the task in a secure, compliant, and trustworthy manner.”

(Marci Maddox, Research Vice President, Persuasive Content and Digital Experience Strategies, IDC)

GenAI is not just a tool but a game-changer for emerging tech vendors, offering a multitude of ways to assist and empower startups. In this blog, we’ll explore how GenAI can transform your startup and help you succeed in 2024 and beyond.

Market Research and Analysis

Understanding your target market is vital for any startup. It’s the foundation upon which your entire business is built. GenAI can be a startup’s secret weapon in market research and analysis, and a recent IDC survey showed that 45% of digital native businesses (DNBs) are already investing in GenAI technologies, with another 34% exploring its use cases for their companies.

In a world awash with data, GenAI can efficiently sift through vast amounts of information to identify trends, customer preferences, and potential market gaps. By processing user-generated content, social media data, and industry reports, GenAI can help you make data-driven decisions that are backed by thorough and up-to-date insights. This not only saves time but also provides a deeper understanding of your market, enabling you to adapt and respond swiftly to changes and opportunities.

Content Generation

Content is the lifeblood of digital marketing, and in today’s fast-paced world, maintaining a consistent flow of high-quality content can be challenging. GenAI can be your content creation partner. From generating product descriptions and blog posts to crafting social media updates, this technology can save your startup time and effort. By using machine learning and natural language processing, GenAI can create content that is not only informative but also engaging. You can focus on the bigger picture while your content needs are met with precision, ensuring that your messaging remains cohesive and professional.

Marketing and Advertising

Digital marketing is a critical component of a startup’s growth strategy. GenAI can enhance your marketing efforts by creating targeted ad campaigns, optimizing SEO, and even developing marketing strategies based on competitor analysis. With its ability to process and interpret vast amounts of marketing data, GenAI can provide insights that drive effective marketing campaigns. By optimizing your digital marketing initiatives, you can reach your target audience more effectively, maximize your marketing ROI, and stand out in a crowded marketplace.

Financial Analysis

Financial management is a fundamental aspect of startup survival. GenAI can assist startups in managing their finances with precision and foresight. By using historical financial data and advanced algorithms, GenAI can help with financial forecasting, budgeting, and expense tracking. With this technology, you can have a clear and data-driven picture of your startup’s financial health. By maintaining a solid grasp of your financial situation, you can make informed decisions, secure funding, and ensure your startup’s financial stability.

Competitive Intelligence

Keeping an eye on the competition is essential for a startup’s success. GenAI can play a vital role in competitive intelligence. By monitoring and analyzing competitor activities, this technology helps you stay informed about what your rivals are doing in the market. It can provide insights into their strategies, product launches, and customer interactions. Armed with this information, you can identify opportunities or threats in your market more effectively. GenAI doesn’t just help you keep pace with the competition; it empowers you to lead and innovate within your industry.

Personalization

One-size-fits-all solutions are no longer sufficient in the modern tech landscape. Personalization is key, and GenAI can help startups deliver personalized experiences. Whether it’s tailoring product recommendations, customizing user interfaces, or offering unique solutions, GenAI can enhance customer satisfaction and loyalty. By understanding individual customer preferences and behaviors, you can create products and experiences that resonate with your target audience on a personal level. This personalization not only fosters customer loyalty but also increases your chances of repeat business and referrals.

Product Development

Startups often face a race against time to develop and launch their products. GenAI can be a catalyst in this process, helping you streamline and accelerate product development. From generating code and designing user interfaces (UI/UX) to suggesting features based on market demand, GenAI can save time and resources while ensuring your product is well-aligned with the needs of your target audience. This can give your startup a considerable competitive advantage, as it allows you to innovate rapidly and bring high-quality products to market more efficiently.

How Can My Business Take Advantage Of GenAI?

To maximize the benefits of GenAI technology, organizations should assess their low-risk processes, content, projects, communications, or activities and determine whether GenAI tools can improve these tasks. A successful GenAI strategy should consider one or more of the following adoption approaches:

By specific use cases or scenarios (such as knowledge capture, skills training, and employee/customer onboarding).
By content form or format (including code blocks or product descriptions).
By feature or function (for example, metadata assignment/analysis and fostering collaboration between teams).
By availability (build or buy).

Converting GenAI’s potential into use cases that generate business value requires a clear-eyed understanding of current limitations and challenges. GenAI technology is powerful, but it is not fully mature and presents opportunities for misuse.

IDC’s leading-edge expertise and insight into GenAI trends, opportunities, requirements, and challenges help you elevate conversations and better engage with your customers.

Are you ready to meet the challenge? Contact us today to discuss how IDC can help you succeed with GenAI.

Public trust in institutions and national leaders, across the globe, has declined for decades with alarming consistency.

This trend is alarming because trust acts as a sort of bellwether for many critical components of our everyday lives: the perceived moral quality of society, the perceived strength of the country’s government, belief in the competence of national leadership, belief in the possibilities of the future, and in economic potential.

When trust is low, nations collapse, religion collapses, democracy collapses. The youngest two generations of our current workforce, Gen Z and Millennials, are especially untrusting – an unsurprising reality for two generations who are navigating a world without the sense of security that characterized the 1950s and 1960s (and an argument can be made for the 90s), eras marked by family stability and relative prosperity, against which rebellion against authority and a strong sense of individuality could flourish.

In contrast, the GenZ and Millennial experience is marked by precariousness – by the time baby boomers hit the age of 35 in 1990, they collectively owned 21 percent of American wealth. Millennials who this year hit the age of 35 own just 3.2 percent of American wealth. These economic indicators are unsettling for trust researchers because we can anticipate that further declines in trust are coming, and with that anticipated decline in trust are further constraints to economic growth.

So imagine the surprise when one group bucked the trust decline trend: Trust in business has increased for Americans. And only Americans. (Source: 2023 Edelman Trust Survey).

This finding confirms a suspicion that has been brewing, but to fully flesh my thinking out, we have to go back to a seminal contribution to trust research: The Trust Game. The Trust Game is a game theory exercise (related to the Dictator Game) that simulates a trust end-state based on the moves of two individuals who have been given a small sum of money (one coin).

If Player 1 puts their one coin into a machine, Player 2 will get 3 coins. Player 2 can then decide whether to put in one coin themselves which turns into 3 coins for Player 1, or Player 2 can give zero coins and leave with 4 coins, while Player 1 leaves with no coins. The creators of the game complicate the parameters of the Trust Game by adding strategy variations: some players “always cooperate”, some players “always cheat”, and some players simply repeats the moves of the other player, “the copycat”.

If player 1 cheats, the copycat cheats, if player 1 cooperates, the copycat cooperates. These various strategies shake out like this: in a game where there is a player who “always cheats” and a player who “always cooperates”, cheaters do not win big. Everyone might leave the table with maybe a little bit of something, but it’s only a little bit. In a game of only “always cooperate” players, the end result is a big win for everyone. All players, including the players who always cheat, are better off because of the player who always cooperates.

But if we eliminate the cooperators and introduce copycats (players who will do as their neighbor does), they will eventually eliminate the cooperators and produce a society entirely of copycats or a society entirely of cheaters, depending on the number of interactions. A lower number of interactions produces a society of cheaters, a higher number of interactions produces a society of copycats.  

Now, if fewer interactions will produce a society comprised entirely of cheaters, then where are we headed when we can have groceries delivered to our doorstep without speaking to a single person, have pizza delivered via an app, and connect with potential mates outside of the communities we live and work using dating apps? These questions bring us back to the hypothesis and to the increasing levels of trust in business for American respondents: for some individuals, interactions with large organizations ARE their most frequent interactions and thus have the potential to be the most trusted interactions.

I might never speak to my next-door neighbor, but I will interact with Google, Amazon, or Microsoft one or two (or forty) times a day. I might talk to my brother on the phone every couple of week or so but will likely shop at Target, Walmart, Costco – whatever your big box store of choice – every week.

Trust forms through repeat interactions. If there is no possibility of a repeated interaction, there is no real need for trust. It’s for this reason that tourist traps are, in fact, traps. The majority of visitors to that area are unlikely to come back, at least in the short term, and as a result, the quality of goods can be shoddier, the food can be of poorer quality, and the overall experience can be somewhat lacking. Contrast that with your neighborhood bakery or cafe, whose success depends on the repeat patronage of neighborhood residents – the coffee and pastries are excellent, or the variety of goods perfectly meets your needs – something desirable compels you to return. That bakery or cafe has built their long-term viability on the trust their customers have in the business.

The IDC Future of Trust program is focused on what engenders and maintains trust in business, and what security, privacy, compliance, and ESG offerings lend themselves most to trust and trustworthiness. We also focus research on what breaks trust, as in the recently published survey spotlight on what was perceived to be the greatest “trust-breaking” event for businesses.

Research has long shown that trust, when compared to direct oversight, facilitates more efficient operations, mitigates the adverse outcomes of negative events such as data breaches, and is a pre-requisite to getting individuals to share high-quality personal information. IDC research has shown that trust confers benefits onto key business outcomes as well, namely in the areas of business resilience, operational efficiency, and sustainability.

In the coming months the IDC Future of Trust Program will offer greater insight into the nature of trust and into key features of trust, with empirical research applied to trusted AI, trusted Security and Privacy, and trust by industry. Stay tuned.