Marketing has been a hotbed of digital transformation for more than a decade but with the recent emergence of Generative AI (GenAI), the most profound changes lie ahead. GenAI improves continuously on a logarithmic/exponential curve of competency mastery. Its potential is limited only by the availability and cost of computing and whatever governance can be applied to control it. While we are amazed at its current abilities, it is just getting started and the rate of acceleration means that in three to five years it will be phenomenally more proficient at everything we teach it to do, and the more people use AI to do their jobs, the faster AI will learn to do everyone’s job.

GenAI has many use cases for marketing like creating personalized emails, social posts, product imagery, audience segments, and much more. However, in a few years, we will no longer think of AI in terms of use cases because single prompts will automatically generate, manage, and optimize processes, projects, and campaigns at orders of magnitude less cost and greater scale. That will have a profound impact on the nature of marketing work for people. GenAI will reduce the need to hire additional marketing staff, collapse some roles, and expand others. Ultimately the result will be fewer people working in marketing in the next five years. Ultimately the result will be fewer people working in marketing in the next five years.

The impact of course is not limited to marketing, it will affect most white-collar jobs up the org chart. Despite this, it is important to remember that fewer humans in the loop does not mean zero humans in the loop. AI may always be better at creating something out of everything, people will always be better at creating something out of nothing.

Furthermore, there will be important new go-to-market challenges and opportunities as AI Super Agents enable programmatic shopping. Marketers will need new methods to influence not only how people coach their AI agents to shop for them, but also how AI agents coach their human owners on what products and services to choose. That said, due to the acceleration of AI capabilities, now is the time for marketing leaders to begin planning how they will redefine their organizations, roles, skills, and practices.

How We Estimated The Productivity Impact Of AI

IDC modeled the work of 24 key marketing roles across five main categories of work.

Management and Planning
Branding and Creative Services
Campaign and Engagement
Analytics and Reporting
Other

We then estimated how much of each category of work can be delegated to GenAI over the next five years, which admittedly may be conservative. The model accounts for a step function of work delegation to AI in 2025, but there may be additional step functions in capabilities that have yet to be revealed in the evolution of AI. Of note, Sam Altman is on record estimating that once Artificial General Intelligence (AGI) becomes available in the early 2030s, it will automate 95% of all the creative work of marketing and all the creative work marketers hire outside service firms to do for them.

We then combined staffing levels and fully loaded cost estimates to calculate the productivity impact of adopting GenAI throughout a marketing team of 56 employees. The result is that in the next five years, GenAI will advance to the point where it can handle more than 40% of the collective work of marketing teams and potentially 100% of specific marketing tasks. Now is the time to start scenario planning for such a huge shift in the nature of work happening so fast.

Preparing For Fundamental Organizational Restructuring

Some key takeaways for marketing leaders to prepare their organization to take advantage of GenAI include:

Every marketing team is different: It is critical to plan for your specific marketing mission, team structure, operating environment, and technology infrastructure. Some marketing teams are overweight on branding, advertising, direct marketing, lead generation, etc. Therefore, your plan needs to be uniquely designed around how much of what kind of work is assigned to each of your staff roles.
Review work processes and data flows: GenAI solutions will consolidate into multi-modal platforms that can create, automate, and analyze whole projects, processes, and campaigns. But they will need vast amounts of data that is structured and tagged for training and retrieval augmentation as well as strong governance and security for optimal performance in the context of your business.
Assess vendor roadmaps: Buyers should focus on the breadth and depth of use cases vendors support not only within marketing but across all customer-facing functions. Use cases will initially translate into business outcomes and create strong economic justification for future investment.
Rapid roadmap: Buyers should also focus on how effectively the vendor’s architecture, tooling, and service resources accelerate the journey of operationalizing the use case roadmap.
Determine the level of infrastructure required to support each type of work: Successful AI deployments will require significant infrastructure – whether provided by the vendor or the user. Marketing technology buyers should work with vendors to determine the required resources and partner with IT counterparts to determine the organization’s readiness to support each type of work. In some cases, the governance, security, data architecture, etc. may not be mature enough to support full GenAI enablement across the martech stack.
No AI Islands: AI capabilities should be implemented from the data layer up not from the task automation layer down. While many GenAI apps exist, every instance of GenAI in a commercial enterprise should share common services for data, governance, security, etc.
Prepare staff (and organizations) for fundamental job changes: Marketing leaders should assess how much work will be delegated to GenAI, and across which roles, based on the applicable use cases. They should prepare staff for significant changes to their roles which may necessitate upskilling, re-organization, elimination of some job titles, expansion of others, and the creation of entirely new career paths. While innovations, historically, are additive to the job market, the transition is inevitably challenging. Marketing leaders will need to consider organizational impact if they wish to successfully deploy GenAI with minimal disruption.
Prepare your data: GenAI is fueled by data. Organizations that do not have real-time, clean, governed, data sets will not be able to take full advantage of this new generation of marketing technology. Martech buyers should partner immediately with IT counterparts to ensure CDP (customer data platforms), or similar Data Lake structures are in place to capture all customer interactions and deliver customer data as an enterprise service on which to base AI decisions across all departments.
Audit your current vendors but be prepared to initiate RFPs (Request for Proposals): It may not be necessary for buyers to rush out and buy the latest and greatest AI tools – especially not individual, disconnected, point solutions. Marketing platform vendors have or will infuse GenAI capabilities into their solutions and as AI evolves from task to process many discrete AI capabilities will consolidate into marketing platforms.
Ingenuity over Innovation: While GenAI will increase the productivity of various marketing functions by 40%-100%, sheer output is not the final measure of a marketing team’s success. As GenAI creates almost limitless personalization capacity, issues of brand identity, market positioning, differentiation, novel messaging, and anticipating cultural trends will soon become the new attributes of best-in-class marketing organizations.

Artificial intelligence (AI) has a tremendous potential to accelerate organizations’ sustainable transformation journeys and create business value. AI can help automate ESG data collection and increase the accuracy of reporting, improve operational efficiencies and anticipate and respond to supply chain risks.

However, the use of AI also poses risks that can harm ESG performance, compliance, and trustworthiness. For example, an algorithm bias could lead to unfair or unethical decision making.   

As organizations are implementing artificial intelligence into their operations, they need to be strategic about the sustainability-related use cases that generate the greatest financial and non-financial ROI. And, they must be aware of the industry-specific risks that this technology can pose.

IDC recently conducted a comprehensive IT buyer survey uncovering current market trends around sustainability and AI, including pain points, spending intentions, and high in-demand use-cases. 

In order to get a more comprehensive understanding of their AI-related value proposition, IT vendors need to understand the full scope of the intersection between AI and sustainability. The different layers are often still presented as disconnected topic areas. According to IDC’s survey, less than 10% of organizations worldwide are currently addressing them through their sustainability/ESG function. This leads to fragmented messaging vis-à-vis their customers and an incomplete picture of their capabilities and responsibilities.

Likewise, IT buyers need to better understand their needs regarding AI-enabled sustainability solutions for the best possible ROI, and they need to be aware of the risks they expose themselves to when leveraging AI across their operations. 

IDC’s Sustainability Framework and the Role of AI 

Currently, more than three quarters (76%) of IT decision makers worldwide consider AI and its derivatives to be “critical” or “very important” for their organization’s sustainable transformation journey. More than 40% say that at least half of their AI spend has a sustainability-related component.  

Sustainable AI – Managing the Environmental and Social Footprint of AI 

The rapid growth of AI is driving up energy and computational demands on datacenters, requiring substantial infrastructure upgrades, as IDC analysts Rob Brothers, Sean Graham, and Shahin Hashim lay out here.

With an increase in power capacity comes a spike in carbon emissions and the building of physical assets that have embedded carbon and need to be decommissioned at some point. GenAI also requires power-hungry GPUs, which, on average, require 10-15 times more energy than CPUs (ibid.). Energy consumption is particularly high at the beginning of the GenAI lifecycle during the training and tuning phases of AI models.  

Expanding Power Capacity and Energy Consumption To Meet AI’s Infrastructure Needs 

Not surprisingly, environmental concerns are top of mind for organizations when deploying AI/GenAI. One-third of survey respondents said that they only work with or buy from IT vendors that meet certain environmental sustainability criteria. Only 2% said that they do not make buying decisions that factor in environmental considerations.  

Practitioners need to also balance the technology’s environmental footprint with its potential social and governance related impact (“Responsible AI”). These issues include biased decision-making and discriminatory outcomes, data security and privacy issues, and unethical business conduct due to the malicious use of AI. 

Of course, the materiality of each of these issue areas will be very industry-specific, and practitioners need to understand the concerns and demands of their various ESG stakeholder groups. For instance, these topics are covered by commonly used ESG reporting standards and frameworks (e.g., the SASB standards), which means that ESG investors will have a close look at corporate reporting and performance on these issues. 

AI for Sustainability – Identifying the Top Industry Specific Use Cases 

GenAI, and AI technologies in general, have the potential to substantially improve and accelerate organizations’ sustainable transformation efforts. The use cases will be very industry-specific, depending on:

The individual ESG issues areas that organizations need to address.
Their stakeholder environment.
The complexity of their supply chains.
Whether they are delivering physical or non-physical offerings.  

Demand for AI-enabled solutions also varies based on maturity and adoption levels. Naturally, organizations that are just getting started on their tech- and AI-enabled sustainability journey will need solutions that can help them get up to speed on meeting regulatory requirements. These organizations are looking for providers that can also help them figure out how to use the products effectively.

Organizations that are further ahead on the maturity curve require more industry-specific nuances in terms of functionality and issue coverage. Vendors will need to be more explicit about the ROI that these solutions can deliver, as they are likely competing against existing or other (new) sophisticated solutions that are procured to improve the delivery of concrete sustainability outcomes. 

Priorities and Pain Points by AI for Sustainability Adoption Level 

In order to determine the most sought-after use-cases, IDC surveyed current end-user demand for AI-enabled solutions across different dimensions that are essential for building the use-cases. The results help IT vendors develop commercial-grade offerings, and provide peer guidance to IT buyers that are trying to prioritize their AI and sustainability tech spend.

These categories include prioritization of ESG issue areas by industry (e.g., greenhouse gas emissions, waste and water management, human rights management, employee wellbeing and DE&I, etc.), biggest impact regarding sustainability management in the value chain (e.g., sourcing, manufacturing, shipping, end-of-life management, etc.), and challenges around the ESG data lifecycle/journey (e.g., collection of insights regarding the regulatory environment, breaking up of ESG data silos, stakeholder management, ESG report creation, etc.).

Below are examples of the top industry-specific use-cases that emerged for Manufacturing, Retail, Energy, and Life Sciences: 

Industry-Specific Use Cases of AI-Enabled Sustainability Solutions

Summary

AI intersects with sustainability/ESG in many ways. Successful organizations look at the intersection from a risks and opportunities perspective and tie their approach to Sustainable AI and AI for Sustainability closely to their overarching sustainable business strategy. This will also require organizational adjustments and alignments, as responsibilities for AI and sustainability span across multiple functions that include IT and LOB personas.

As sustainability strategies become more holistic and ESG materiality more the driver for action, AI and sustainability strategies need to account for the diversity in ESG issues that can be caused by the use of AI, and practitioners need to be able to pick the solutions that truly help address their organization’s most material issues. 

For more information on the topic, thought leadership research on sustainability/ESG, and an overview of IDC’s sustainability/ESG analysts and offerings, please visit our sustainability/ESG website. 

What do software supply chain security and generative AI have to do with each other? Until recently, the answer was “not much.”

But that’s changing due to a new type of software supply chain risk known as package hallucination. Package hallucination creates novel opportunities for threat actors to plant malicious code within software supply chains and prey on developers who use generative AI to write code.

Here’s a look at how this type of attack works, why it adds a new layer of difficulty to software supply chain security, and what enterprises can do to stay ahead of this challenge.

What is Package Hallucination

Package hallucination happens when a large language model (LLM) references a software library, module, or other type of package that does not actually exist.

For instance, imagine you’re using an AI tool like GitHub Copilot to help develop a Python, and it spits out a line of code like the following:
import advancedmathlib

No Python module or package named advancedmathlib exists. If Copilot generated code like this, it would be hallucinating.

How Package Hallucination Affects Supply Chain Security

In some cases, AI-generated code that references packages that don’t exist would simply result in the code not compiling or running properly, because the application would fail when it tries to retrieve the nonexistent package.

But it’s possible that something more insidious could happen. If threat actors were to create a package with the same name as the one hallucinated by an AI model, and if they injected malicious code into that package, the application would likely download and run the malicious code.

Note, too, that the package does not need to be malicious at the outset. It could initially be legitimate but beacon to a command and control server that updates the package with malicious code at a later date – so simply scanning the package for malicious contents won’t always reveal the risk.

In this way, AI package hallucination creates novel opportunities for attackers to poison software supply chains.

To date, no real-world software supply chain security attack has been known to occur. But researchers at Lasso Security showed how easily this type of attack could happen. They found that AI models hallucinated software package names at surprisingly high rates of frequency and repetitiveness – with Gemini, the AI service from Google, referencing at least one hallucinated package in response to nearly two-thirds of all prompts issued by the researchers.

Even more striking, the researchers also uploaded a “dummy” package with one of the hallucinated names to a public repository and found that it was downloaded more than 30,000 times in a matter of weeks. This is proof positive that large numbers of developers are blindly trusting AI-generated code that references hallucinated packages, and that it would be quite easy for threat actors to exploit this risk.

A New Twist on an Old Story: Package Hallucination vs. Typosquatting

If software supply chain exploits involving AI hallucination seem familiar, it’s probably because they resemble other types of supply chain attacks – especially package typosquatting, a technique threat actors have long used to trick software developers into incorporating malicious code into applications.

Package typosquatting involves uploading malicious packages with names that are similar, but not identical, to popular software packages. For instance, an attacker typosquatting on PyTorch (a legitimate, widely used Python library) might name a package PyTorchh or Py_Torch. Through carelessness when coding or browsing software repositories, developers might accidentally import the malicious package into their applications.

However, compared to package typosquatting, AI package hallucination has the potential to be more insidious and harmful, for several reasons:
● When used as a software supply chain attack method, package hallucination is likely to have a much higher success rate than typosquatting because it doesn’t rely on errant keystrokes to trigger a successful attack. Instead, it exploits the tendency of programmers to run AI generated code without assessing or validating it first.
● Developers are more likely to fall for the package hallucination attacks because they may assume that code generated by popular AI-assisted development tools can be trusted.
● For attackers, identifying commonly hallucinated package names doesn’t require highly specialized skills or tremendous amounts of time and effort. They can simply generate code using AI services, then scan it for repeated instances of hallucinated package names, using the same method as the Lasso Security researchers.

In short, package hallucination will likely prove easier for threat actors to exploit, and lead to a higher rate of malicious package downloads, than traditional approaches to injecting malicious code into software supply chains.

Protecting Your Software Supply Chain From Package Hallucination

The good news is that protecting software supply chains from this new type of risk boils down to leveraging the defenses that enterprises should already have in place, such as:
● Generating a Software Bill of Materials (SBOM) for applications they develop. SBOMs identify the software components within applications, making it easier to determine whether they include any hallucinated packages that may contain malicious code. (Unfortunately, IDC research shows that only 28 percent of enterprises automatically generate SBOMs.)
● Using Software Composition Analysis (SCA) tools to scan codebases for vulnerable components, including unrecognized packages that may have been hallucinated.
● Establishing guidelines and policies for AI-assisted software development, such as rules requiring developers to validate third-party software components before integrating them into a codebase.

Software supply chain security was already a serious challenge, with attacks surging in recent years. The package hallucination risk suggests that the problem is likely to grow even worse, making it all the more important for enterprises to invest in effective software supply chain defense and visibility solutions.

The next wave of industrial revolution is here, and it’s being propelled by a brand of businesses that are native to the digital world. Digital-native businesses, or DNBs, are enterprises built from the ground up on digital technologies like cloud computing, data analytics, and artificial intelligence. Unlike traditional companies trying to retrofit new technologies, DNBs have disruption codified into their organizational DNA.

As entities born into the era of ubiquitous connectivity and rapid technological progress, DNBs are unshackled from legacy constraints. They can be bolder in adopting cutting-edge innovations to gain a competitive edge. This first-mover advantage allows DNBs to continuously reinvent industries and push the boundaries of what’s possible.

As we progress into the second quarter of 2024, DNBs are poised to accelerate their trajectory as digital pioneers. Here are the top 5 trends that will shape their transformative impact.

Generative AI Becomes Their Killer App

By 2025, research indicates that DNBs will invest in generative AI (GenAI) technologies like ChatGPT at a blistering pace – 5 times higher than traditional businesses. This isn’t surprising given how generative AI perfectly complements the data-driven, innovative core of DNBs.

From automating back-office workflows to enriching customer touchpoints with conversational interfaces, DNBs will embrace generative AI across every facet of their operations. Their ambitions will be turbocharged by the fact that many prominent generative AI players like Anthropic and OpenAI are themselves DNBs pioneering novel use cases.

As of early 2024, nearly $50 billion has already been poured into generative AI startups and scale-ups (source: PitchBook, January 2024). With their laser focus on disruptive technologies, DNBs are at the forefront of creating world-changing generative AI solutions and applications that will redefine how businesses operate.

Data As The Fuel For Growth

In this AI-powered era, data is not just the new oil – it’s the renewable fuel propelling innovation for DNBs. By 2026, these digital tenacities will spend over a third of their cash burn on technologies like robust data architecture and platforms to integrate generative AI models with their proprietary data sources.

Why the heavy investment? Because how well a DNB can generate value from generative AI directly ties back to how strategically it leverages its own and third-party data reserves. From reducing costs to fortifying data privacy and knowledge security through AI, superior data capabilities will dictate which DNBs gain an unassailable competitive advantage.

While DNBs currently lead traditional companies in data investments and access to collaborative data platforms, the deluge of diverse data streams is an emerging challenge. Cracking the code on unifying and extracting insights from these disparate data sources will be key to unlocking the full potential of their generative AI exploits.

Rise Of The Automated, Augmented Workforce

The frenetic pace of innovation also brings with it an acute shortage of skilled technical talent for emerging roles like AI prompt engineers and data scientists. By 2025, over 70% of DNBs will lean on a powerful two-pronged approach to plug these workforce gaps:

Intelligent Automation: By harmonizing artificial intelligence with robotic process automation (RPA), intelligent automation will allow DNBs to automate repetitive manual tasks while deploying generative AI to handle sophisticated, judgment-based work. This duet of technologies will be instrumental in alleviating the talent crunch across roles.
Enhancing the Human Element: While leveraging advanced automation, DNBs won’t lose sight of the human factor. They will double down on investments in employee experiences – from fostering engaged cultures to pioneering flexible work models. Proven to invest twice as much as traditional firms in this area, DNBs understand an outstanding employee value proposition is vital for attracting and retaining top talent.

Generative AI’s omnipresent role in the workplace may have rocked certain industries, but the tech-first DNA of DNBs positions them to be relatively insulated from workforce displacement fears. Instead, they are poised to be catalysts in reskilling and upskilling their existing employees to adapt to the demands of an AI-augmented future of work.

Delivering Superlative Customer Journeys

In the hyper-competitive digital economy, customer experience (CX) is the universal currency. Recognizing this, over 50% of DNB scale-ups will heavily prioritize CX and personalization as key battlegrounds in 2024. 27% of DNBs plan to directly invest in dedicated CX initiatives and projects.

GenAI will be their loyal lieutenant in this campaign. From deploying AI-powered chatbots and virtual assistants to reduce support costs to leveraging predictive analytics to hyper-personalize products and services – DNBs will pull out all the stops to curate unparalleled customer journeys. The ultimate goal? To foster a deep, emotive connection with users that inspires rabid brand loyalty and long-term value creation.

Underpinning this CX supremacy is the recognition from venture capitalists that DNBs harnessing generative AI for superlative customer engagement are hugely attractive investment propositions. As the catalysts of the modern consumer economy, DNBs have an acute understanding that individualization at scale is no longer a luxury, but an existential imperative.

Pioneers Of The New World Of Work

By 2026, a remarkable 85% of new job roles across emerging tech disciplines like quantum computing, autonomous transportation, augmented reality and blockchain will be birthed by DNBs. This prophecy is rooted in the fundamental truth that these companies are natively wired to constantly reimagine the art of the possible.

Their propensity for deep tech R&D, coupled with their appeal to Gen Z and Gen Alpha technophiles, positions DNBs as the ideal launchpads for redefining the future workforce. Fields like robotics, ethical AI governance, nanotechnology, and virtual production are just a few examples of novel vocations these companies will spearhead.

In tandem with universities that cultivate breakthrough innovations, DNBs are the conduits translating theoretical possibilities into commercialized products and services that transform industries. In their quest to remain hyper-relevant and future-proof their operations, DNBs will constantly shed old job archetypes in favor of emergent roles adept at harnessing new technology frontiers.

Embracing Change As A Constant 

From leveraging generative AI as an upheaval catalyst to architecting novel workforce paradigms, digital-native businesses are hard-coded for metamorphosis. Their superpower lies in their ability to be nimble and quickly adapt to disruptive technological shifts in a VUCA (volatile, uncertain, complex, ambiguous) world.

While companies anchored in 20th-century operating models scramble to evolve, DNBs are unencumbered by legacy frameworks or archaic mindsets. They can freely re-imagine industries from first principles and rapidly pivot to capture opportunities in dynamic, technology-fueled environments. 

So as we hurtle into a future where human-machine partnerships become the norm and AI co-pilots uplift every experience, DNBs are poised to be our extraordinary guides. By relentlessly staying ahead of the innovation curve, these digital leaders will open new realms that enhance how we work, engage, and fundamentally live as a society.

Disruption is no longer an existential risk, but an existential imperative for businesses. Digital-native enterprises are living, thriving case studies in how boldly embracing change is the only sustainable path to creating long-term value. The trends of 2024 are merely airbrushing the beginning of their transformational legacy.

AI: The Game Changer

Hold on to your hats because the business world is in for a transformative ride! Artificial intelligence (AI), especially generative AI (GenAI), is rapidly changing how companies operate. This isn’t just a passing trend; it’s a strategic must-have for businesses that want to stay ahead of the curve.

According to IDC’s December 2023 Future Enterprise Resiliency and Spending Survey (Wave 11), 66% of organizations worldwide are exploring the potential of GenAI. The survey found that an estimated 50% of legacy application code is running in production environments today with 40% being replaced with GenAI applications.   Many are in the early stages of model testing or developing use cases. This heightened interest underscores the transformative power of AI in reshaping business landscapes.

IDC’s research shows a surge in companies exploring GenAI, recognizing its potential to revolutionize how they work. And when it comes to the ability to generate content, AI can turn isolated asset into connected experiences that benefit everyone – not only employees and customers, but also everyone and everything in the ecosystem.

Experience Orchestration: The Heart of the Revolution

The future belongs to businesses that prioritize experiences. This is where IDC’s latest released framework for an experience-orchestrated (X-O) business comes in. Imagine a world where AI-powered technology connects everything, creating seamless, data-driven experiences at every touchpoint.

As described in the IDC Perspective: The Value of an Experience-Orchestrated Business, the definition of an X-O business delivers shared experience value powered by intelligence. To compete in an AI everywhere world, digital businesses must orchestrate a meaningful value exchange between the organization and their key stakeholders. Data is vital to intelligent applications embedded in daily operations and decision-making.

Insights help align actions with desired outcomes and ensure that investments deliver the desired results for the experience-orchestrated business. Using AI-enabled technology to optimize journeys and automate workstream tasks, organizations can break down organizational silos and foster connectedness across the experience ecosystem.

In today’s competitive environment, where economic uncertainty reigns supreme, exceptional experiences are the key differentiator. Transforming mundane tasks into meaningful interactions strengthens relationships and fuels growth, even in challenging times. IDC’s research highlights that becoming a digital business requires a strategic focus on experience orchestration. By investing in technologies and processes that enhance daily operations and interactions, businesses can elevate their digital maturity and stand out from the crowd.

People Power: The Human Touch in GenAI World

The shift to an X-O business requires not just the right technology, but also the right talent. Companies need passionate individuals who are driven to create exceptional experiences. This means fostering a culture that embraces AI and focuses on outcomes derived from stellar experiences, not just the outputs of completed tasks.

Organization leaders must channel a change management and growth mindset by finding opportunities to embed GenAI into existing applications and providing resources for self-service learning. The C-suite should champion experience orchestration and invest in training and commit to new management models for AI-centric roles. Prioritize how to address human biases and data privacy issues while optimizing collaboration methods.

The road to becoming an X-O business involves several key steps: establishing the right metrics, engaging stakeholders, and adopting the necessary AI-infused technologies that assists in creating and managing engaging content across product, engineering, sales, marketing or customer support. IDC outlines a path forward in The Experience-Orchestrated Business: Journey to X-O Business — Assessing the Organization’s Ability to Become an X-O Business. When choosing which GenAI technology to invest in, businesses should find a balance between the talent and skill needed to build their own solutions, leverage existing tools, and partner experts to accelerate their transformation.

The adoption of AI got a big boost from GenAI, making organizations re-think how they can leverage it for better content creation, operations and experiences.

Content is King: Building Trust in the Age of AI

Let’s take a deeper dive into how AI is changing the content game and how organizations should setup their AI system and associated processes to create and deliver authentic content. Here are 15 considerations when using GenAI in the content supply chain.

Word Usage and Cultural Language Bias: Content should avoid language bias and cultural insensitivity. Organizations must be mindful of how their messaging resonates across diverse audiences.
Accessibility: Customers expect a smooth, frictionless experience online. This means user-friendly mobile apps and clear, concise information that makes completing transactions a breeze.
Transparency: Building trust is crucial to customers who want to know how their data is used to personalize their experiences. Transparency builds empathy and strengthens trust.
Privacy: With data privacy laws evolving, marketers are adapting content creation to ensure customer confidence. Strong security measures are essential to safeguard information.
Brand Authenticity: Customers can sniff out inauthentic content a mile away. Building trust requires actively learning about your audience and reflecting their values in your content.
Shared Knowledge: Providing high-quality, free information about your product or service (or industry if you are so bold) will establish your expertise and foster trust.
Customer Relationships: Real-time, personalized content strengthens the bond between your brand and your customers.
Customer Intelligence: AI can be a powerful tool to understand customer needs and create content that resonates.
Plain Language: Authentic content is created using plain language. Clear communication enhances understanding and fosters trust.
Customer Effort: Make it easy for customers to find the information they need. User-friendly interfaces and clear communication are key.
Social Commerce Content: Tailoring content for regional and demographic differences is essential in the age of social commerce.
Social Media Content: Curate content from trusted sources and ensure your outbound content is high-quality and transparent.
Short-form Content: Respect your audience’s time. Invest in short videos, infographics, and bite-sized content for busy users.
User-Generated Content: Listen to your customers who value reviews, influencer insights, and social media trends which can all inform product and service innovation.
Authentic Brand Voice: Develop a consistent brand voice that the GenAI engine can access to reflect your brand’s values across all platforms.

Ethical considerations are also paramount in the AI era. Customers expect data privacy, responsible AI systems, and transparency in how AI is used. Organizations that prioritize these aspects as part of their content generation will build trust and establish a strong reputation.

The Road Ahead

For technology buyers looking to navigate the transition to an experience-orchestrated business, IDC offers several recommendations:

Provide Transparency of Customer Data: Establish governance and controls around the use of customer data in generating content and recommendations.
Apply Intelligence to Content Creation: Leverage generative AI to create authentic, engaging content that resonates with customers and enhances digital trust.
Continually Measure Digital Trust: Evolve content strategies to maintain customer trust and address changing expectations.
Prioritize Authenticity: Authenticity is key to engaging modern consumers. Embedding authenticity into the brand’s DNA will reflect in every interaction and content piece.

Investments in technology will lay the foundation for insights-driven transformation. IDC’s PlanScape: Experience-Orchestrated Business to Deliver Differentiated Value Outcomes has more information about developing an X-O business strategy. The journey to becoming an X-O business is exciting, but it also requires effort. By prioritizing experiences, leveraging AI, and focusing on outcomes, organizations can differentiate themselves and thrive in the digital age.

The time to act is now! The future belongs to those who can adapt, innovate, and deliver value in a world powered by AI.