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2023 is nearing an end and organizations must work to map out budgets and spending plans for 2024 and beyond. As a CX practitioner, are you considering all key aspects of these programs and planning for them?

This has been a year of exceptional turbulence due to expectations for a recession, military actions in Europe and now the Middle East, and inflation remaining high. IDC found that 67% of respondents in October 2023 think there will be a recession in the coming year, and 37% of global respondents said their biggest concern was “inflation driving up vendor pricing beyond budget expectations”.

Yet, organizations remain committed to their CX programs.  IDC found that Customer Experience (CX) initiatives and projects are a priority being “most immune to budget reduction regardless of the economic environment” and “continued investment is a must in the next 12 months”. CX comes in second only behind Security, Risk & Compliance initiatives.

While it is interesting to see these projects remain high on the priority list, it is clear that a thorough review and refresh on what is needed for successful programs be undertaken as we roll into next year. 

“Voice of the Customer (VoC) and CX practitioners are clearly focused on metrics and outcomes of their programs.  They are being challenged by management to show Return on Investment for their programs as quickly as possible or risk a pull back in investments.”

Key areas for consideration in these programs are:

Maturity of program – VoC programs often start with structured surveys to solicit for customer feedback and then grow to include unstructured data and even inferred data. (see “Voice of the Customer” Programs: Where Are They Today? More Importantly, Where Are They Going?”) Depending on what stage of this maturity spectrum a program is on will dictate the investment in software and services to support it.  Rather than move from one type of customer feedback data to another, programs often are cumulative in the data they gather.
Staffing and Skillset – VoC programs are required to have skillsets to not only listen and gather customer feedback, but to analyze it, and integrate solutions to act on it through sales, marketing, support and customer success. They also need program managers, data scientists, IT support (including app and web development) and sponsorship in each functional department tied into the program. Practitioners should consider if they are prepared to provide these skillsets internally or find an appropriate partner that can offer these services.  In addition, this partnership can change over time as an organization decides to move to either outsource more of this work, or take it on internally.
Make sure to engage your customers – Customers today expect their brands to not only listen to them where they want to be (social media, product review sites, communities), but show them they are listening and engage them.  A recent WSJ article discussed how TikTok can be used to suggest ideas for new products or services, and if a brand is not quick to see the suggestion and follow up, a competitor could swoop in with a new product and quickly take share from the brand. Investments in CX and VoC programs should not only consider resources to engage the customers, but product development budgets should consider unexpected ideas that need to be addressed quickly.  Customers today are demanding great customer engagements and moving on to new brands if they feel they are not getting them. 
GenAI – The buzz around this new technology is getting significant attention in the CX space.  The opportunities to improve customer interactions via natural language interfaces and internal improvements such as feedback summarizations and insights has the potential for process and experience improvements. This can lead to potential hyper-personalization experiences where products can know their customers individually and dynamically update digital products and services to meet their real-time needs.  Being so new, the GenAI offerings on the market are still working out pricing options.  CX practitioners should consider budget items for GenAI as they move forward to ensure they can consider some form of implementation of them in 2024 and beyond.
Enterprise-wide programs – Customers today expect brands to understand and coordinate the different functional teams involved in customer engagements.  Marketing campaigns, sales efforts, and digital commerce and support all have to know what the other is doing to ensure customers do not receive disconnected emails, phone calls, etc. They also want to know that the calls or emails they do receive have taken account for their history (why receive an email to buy more of a new product if the customer has indicated they are not happy with the old one?)
Metrics and outcomes – Any program, CX or other, should have key metrics and outcome expectations followed to ensure they are having a positive effect on the organization.  Net Promoter Score (NPS – relational and transactional), Customer Satisfaction (CSAT), Overall Customer Satisfaction (OSAT), and Customer Effort Score (CES) are just a few of the most common metrics.  IDC is predicting that “By 2027, one-fourth of global brands will abandon CSAT as a measure of customer experience and adopt a Customer Effort Score correlated to outcomes as a key indicator of journey satisfaction and success” Reducing churn, improving annual recurring revenue, and improving customer lifetime value are business outcomes frequently used.  Tying metrics to investments in CX and VoC programs is key to ensuring they are performing for the organization.

While the list could go on, these are just a few of key areas to consider for updating your CX and VoC programs for next year.  The key action items to consider are to know where your customers want to be heard.  Be there, listen, and let them know you are listening.

The success of your content strategy hinges on aligning with the dynamic needs of your audience. As we step into 2024, it’s crucial to reassess and optimize your approach. Here are five overarching themes to make your B2B content more successful in the coming year:

1. Replace the Nonlinear Marketing Funnel

Bid farewell to the traditional marketing funnel. Marketers and sales teams today aren’t just talking with one buyer but with an entire buying cohort. Within an organization, over a dozen individuals may express distinct needs, driven by their respective tasks to be accomplished. Historically, sales and marketing adhered to a linear model—the funnel. However, the paradigm has shifted away from the progression of a lone individual through a linear journey. The funnel, once standard, lacks customer centricity and may explain the challenges faced by marketing and sales in nurturing and building relationships across the entire spectrum of the buying committee.

In 2024, it’s essential to move beyond the linear model. Based on extensive research, IDC’s new Adaptive Customer Experience (ACE) model is a circular and evolving framework and not linear at all, like its predecessor. This is because engagement with tech buyers is no longer linear.

B2B buyers have B2C experience expectations today.  They expect you, as a vendor, to understand and provide solutions for their challenges, jobs to done and business outcomes. ACE is a customer-centric framework to evolve how you go to market. 

Marketing is the conductor of orchestrated journey engagement, with data, automation and analytics to make this all work.

Laurie Buczek, Research VP, CMO Advisory Practices

And perhaps most importantly, to fully engage with a buyer, marketing and sales need to play on the same field. Put another way, no longer should the two functional groups be looked at as if they are running a relay race, where marketing passes a baton onto sales to finish the race. In fact, it’s not a race, it’s a customer experience journey.

2. Fast and Cost-Effective Digital Marketing Content Integration

In the race for B2B leads, content integration emerges as a critical strategy for effective lead generation. The quest for valuable B2B leads demands a streamlined approach to filling the content pipeline promptly and economically. One powerful tactic in achieving this goal is the incorporation of licensed content into your strategy. This approach is twofold in its advantages: it not only expedites content production but also brings in valuable perspectives from industry experts. By leveraging curated content, you can maintain a consistent and relevant flow of information to your target audience. This not only satisfies the thirst for timely insights but also proves to be a savvy financial move by minimizing production costs, allowing for a more efficient allocation of resources in the broader spectrum of B2B demand generation.

Content marketing services play a pivotal role in this endeavor. They not only facilitate the integration of licensed content but also offer expertise in optimizing its impact. The strategic use of these services ensures that curated content aligns seamlessly with the overarching B2B digital marketing strategy. In essence, the synergy between fast and cost-effective content integration, bolstered by licensed content and guided by content marketing services, becomes a key driver in propelling the efficiency and success of B2B demand generation efforts.

3. Craft a Balanced Thought Leadership Strategy

The role of thought leadership in shaping a compelling narrative and fostering trust cannot be overstated. A proactive thought leadership strategy stands as a cornerstone in the pursuit of generating demand. While it may not be the generator of B2B leads, it does serve as a vehicle to establish authority within the industry, elevating your brand awareness, positioning your business as a key player and trusted advisor. However, the landscape in 2024 demands a nuanced approach—it’s not just about the quantity of thought leadership pieces but the delicate balance achieved through strategic integration. Building a thought leadership strategy that strikes this equilibrium is essential for navigating the evolving dynamics of B2B demand generation successfully.

Strategic integration of thought leadership pieces is intertwined with the broader goal of aligning with the values and preferences of the target audience. This imperative underscores the necessity for content personalization, a pivotal aspect in crafting a distinctive B2B digital marketing strategy. Certain content marketing services specialize in the creation of bespoke pieces that delve into industry pain points while aligning intimately with your brand’s core values. These finely tuned and tailored creations resonate more profoundly with the audience, amplifying the potential for meaningful engagement and, consequently, driving successful B2B demand generation efforts.

Engaging research firms and leveraging their content marketing services becomes a strategic move to gain a topical edge. By delivering sought-after information crucial for decision-making, along with providing unique insights and high-quality research, you not only establish trust with your buyer but also elevate awareness, increase media exposure, and generate leads.

4. Strategic Decision Mapping and Audience Alignment

What can help you define your customer’s decision stage in their journey? Strategic investment in a lead generation program that yields measurable results is essential in a landscape where every dollar counts, emphasizing the efficient allocation crucial for successful B2B demand generation. Identify programs meticulously aligned with your target audience, recognizing the nuanced landscape of your market.

Understanding the customer’s decision stage within their journey is paramount for B2B demand generation. This involves the implementation of a targeted lead generation program rooted in a comprehensive database of buyer personas that align seamlessly with your overarching marketing strategy. By pinpointing the specific stage at which your audience is making decisions, you can tailor your efforts to provide timely and relevant information, optimizing the chances of conversion.

Audience alignment, as a complementary aspect, takes strategic decision-making a step further. It requires a nuanced comprehension of the target market, encompassing factors like demographics, preferences, and pain points. A successful alignment strategy involves crafting content and engagement tactics that resonate with the unique needs of the audience at different stages of their journey. This tailored approach not only boosts relevance but also fosters a deeper connection with the audience. In essence, strategic decision-making and audience alignment become symbiotic forces, driving the success of B2B demand generation efforts by ensuring that every interaction is purposeful and resonant with the evolving needs of the target audience.

5. Seamless Sales-Ready Handoff: Closing the Loop

The transition from marketing to sales marks a crucial juncture in the customer journey, and it’s at this point that many leads encounter challenges. A seamless handoff is essential to ensure the continuity of the conversation initiated online. The use of interactive tools in your marketing toolkit becomes a pivotal strategy in empowering sales teams to seamlessly pick up where online interactions left off. These tools go beyond mere engagement; they serve as facilitators of value-selling conversations by providing valuable insights and equipping sales teams with the necessary resources for meaningful and informed customer interactions.

To determine the right tools for your team, it’s essential to evaluate their compatibility with your objectives. We provide some insights in a recent blog, Evaluating Sales Tools: The Pros and Cons for Sales Enablement Leaders.

As we step into 2024, the landscape of B2B content marketing continues to evolve. By incorporating these five key strategies into your approach, you can adapt to the changing dynamics of the market, enhance lead generation efforts, and foster enduring customer relationships. Stay agile, stay informed, and let your content lead the way to B2B success in the years to come.

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The demand for GenAI is higher than we have seen with any emerging tech because it is widely recognized to impact market opportunities, competitive dynamics, and long-term strategic plans in ways we haven’t seen before.

GenAI triggers a new leg of the digital journey, one IDC refers to as AI Everywhere. In this next leg of the journey, business leaders will need to adjust their strategy, invest in new technologies, and cultivate new skills. As leaders prepare their organizations for AI Everywhere, they face the following critical decisions.

Strategy and Roadmaps

About half of organizations have an AI roadmap in place and are expected to update it to incorporate the new GenAI use cases; the rest will need to develop a roadmap with Gen AI use cases at the heart of it.  

GenAI has compelling promise for both productivity gains and revenue growth. IDC predicts $1T in productivity gains by 2026 for enterprises who leverage GenAI and automation technologies and a doubling of revenue growth rates for those who use GenAI to co-develop digital products and services. 

According to our global C-Suite survey, we believe organizations will prioritize productivity use cases for the next 18-24 months then revenue growth use cases in 3-5 years.

Industry Disruption

There’s no doubt GenAI has made an impact — 37% of all IT leaders worldwide believe GenAI has already disrupted their business (19%) or will significantly do so in the next 18 months (18%), according to our Global GenAI survey. Life sciences, healthcare, professional services, and media/entertainment are the industries that expect to be disrupted in the next 18 months. In life sciences and healthcare, GenAI is being used to digitize biology to reduce the time and costs to bring new drugs to market. In the media and entertainment industry, GenAI will allow broadcasters to deliver transformative viewing experiences that keep audiences engaged and entertained. And GenAI will allow IT service firms to reduce delivery costs and time, with 40% of services engagements including GenAI-enabled delivery by 2025, according to our predictions.

Tech Roadmaps

Unlike early-stage technology markets, where most organizations wait for the technology to mature, we are seeing a high percentage of organizations planning to invest early on because they recognize the competitive advantage GenAI can bring to their business. However, as tech suppliers evolve their roadmaps for GenAI, tech buyers are struggling to understand when these capabilities will be commercially available.

Enterprises are asking, “When will GenAI Security and Governance be built into the technology?”. We expect 70% of cloud and software platform providers will bundle GenAI safety and governance packages with their primary services by 2026.

They are also asking, “To what extent will cloud providers enable GenAI use cases?” We believe the answer is, “A large extent,” as cloud providers make significant investments in foundation models, AI platforms, and high-performance infrastructure. By 2025, we expect 70% of enterprises will have formed strategic ties with them for GenAI.

And in a world of distributed data, enterprise are already thinking about latency issues and when they can run LLMs on clients. We know PC OEMs, operating system vendors, and silicon providers are eager to bring inferencing capabilities down from the cloud to the client and predict that 80% of new PCs shipped for commercial use will include AI-specific silicon designed to run LLMs locally by 2027.

Maintaining Trusted Enterprise Status

Over the past several years, enterprises have worked hard to be a trusted digital business. As AI is heavily used, enterprises will need to navigate new dimensions of trust as they contend with GenAI risks around data training sets, hallucinations, security exposures and frauds. One of the biggest question enterprises are asking is, “How do we govern AI?”. To address their questions, we have developed predictions about how organizations will govern.

Educate: We believe understanding AI will be the responsibility of employees and 60% of large enterprises will mandate formal data literacy and responsible AI training to mitigate new risks created by the pervasive use of GenAI by the end of 2025.

Extend: Because GenAI has the potential to magnify existing risks around data privacy laws that govern how sensitive data is collected, used, shared, and stored, 80% of large organizations will extend their Data Loss Prevention (DLP) deployments to GenAI environments to prevent privacy violations and data breaches.

Guide: Review boards will be a critical element of the governance and by 2025, 75% G2000 companies will have implemented review boards for management oversight of ethical and responsible AI use.

Monitor: Once these oversight boards are in place, 40% of organizations will utilize AI-enabled risk and compliance solutions to continuously monitor data in real-time to predict non-compliance internally or from 3rd-party associations.

Transparency:  We expect to see LLM transparency statements become the norm as over 30% of organizations that leverage sophisticated AI to produce their products or services will disclose the sources of data used to train the AI models utilized by 2026.

Funding GenAI

The demand for GenAI is high and we are forecasting that organizations will spend $16B on Gen AI in 2023, growing to $143B by 2027. But this spending will be largely dependent on the answers the IT industry provides to the questions outlined above.

If the price for GenAI capabilities is too high, enterprises will wait until the price comes down.
If the roadmaps are slow to address current tech challenges, enterprises will wait until they are in place.
If trusted enterprise status cannot be maintained, enterprises will delay investments until it can be.

AI Everywhere

Rapid adoption is moving AI from an emerging software segment to a critical technology at the center of a platform transition and a new phase of computing – AI Everywhere. GenAI’s potential for innovation is comparable to the introduction of the internet and the potential is limitless. GenAI is here. Are you ready?

To learn more about AI Everywhere and what customers need now, view our GenAI resources website here.

In the last few years, we’ve seen a flood of new industry cloud announcements and launches from major cloud service providers (CSPs), enterprise software vendors, global system integrators, and professional services firms. Many vertical SaaS and PaaS solutions are also positioned as industry clouds in the financial services, insurance, manufacturing, retail, energy, and other industries. The IDC Industry Clouds Directory, which we’ve published for several years, today has over 320 listings in 12 industries, and those are just the industry clouds we know about!

It is easy to see why the popularity of industry clouds exploded since the major cloud and software vendors entered the market. These vertically integrated solutions offer capabilities that give organizations a more direct path to cloud, providing built-in compliance with industry-specific regulations for security, privacy, and reporting, among other advantages. Just having these industry standards pre-configured and regularly updated can save organizations a tremendous amount of custom work and costs. And industry cloud vendors go well beyond these basics, usually providing industry-specific tools, workflows, data models, connectors, and other services that give users the opportunity to leverage the latest innovation accelerators in their industry.

If your organization is considering the adoption of one or more industry clouds, consider the tips below to optimize your investment. Our advice is based on over a decade of IDC research covering industry clouds, solutions, and technology verticalization strategies and many conversations with both buyers and vendors of industry clouds.

Go at your own pace. With their promises of a complete and integrated solution for your industry-specific needs, industry clouds seem to represent a massive new system that requires a drastic overhaul of how you currently do your work. But in fact, industry clouds are built with flexibility and modularity in mind that enables organizations to adopt as many or as few modules as they need at a time. If your business already uses cloud services, enterprise software systems, or professional services solutions, adding industry clouds from the same vendors or their partner ecosystem could be a gradual and streamlined process, using a plug-and-play approach.
Don’t worry about vendor lock-in. Thanks to the recent influx of new offerings, there’s no shortage of industry clouds to choose from, especially in highly regulated industries.  It makes sense to start by evaluating industry cloud solutions from your current vendors – after all, they should work out of the box, and time to value is one of the six pillars of industry clouds. Other advantages of working with your current provider(s) include the consumption convenience of having it count towards your negotiated cloud spend or qualify you for other discounts. Best of all, having to be “locked in” with a vendor as a trade-off for some of these benefits no longer holds true. Most vendors today design their industry clouds with interoperability in mind, building them with open architectures to ensure free data flows and collaboration. If the best-fit solution for your needs is offered by a provider other than your own, it is likely that you are only an API away from being able to integrate it into your own industry cloud platform.
Collaborate closely with your industry ecosystem. Industry clouds enable organizations to supercharge their industry partnerships. This capability is more important than ever today, when sustainability reporting, supply chain interruptions, and increasing regulations require greater data transparency and operational agility across a company’s ecosystem. Using industry clouds to collaborate with your industry ecosystem promotes an open yet secure sharing of data, applications, operations, and expertise across organizations, which in turn can enhance decision-making, increase innovation, and ease skill shortages and supply chain challenges, among other benefits.
Use industry cloud platforms to stay competitive. While industry clouds give you a head start by providing specialized security, compliance, data models, and other tools to meet common industry challenges, they also provide opportunities to add your own differentiators to the mix. That can mean adding new generative AI capabilities that are trained and grounded in your own data or using low-code/no-code functionality to optimize and automate workflows, for example. With the platform approach used by many industry clouds today, adding such innovations becomes relatively easy (if your data environment is ready). This extensibility of industry cloud platforms enables companies to stay agile and competitive, keeping pace with the latest technology developments. Moreover, companies themselves (or with their professional services partners) can build new industry capabilities or integrate AI models grounded in the company’s own data, further differentiating and evolving their operations.
Prepare your organization to leverage the full potential of industry clouds. The platform-based and collaboration-centered approach used by most industry clouds may be new to many users. By providing early and continuous communications about the project and training and support for the new tools, you can help lower the barriers to adoption within the organization. In addition to preparing the company data assets, it is equally important to ensure that the company’s data governance and data sharing policies and processes are updated and communicated clearly to internal users and (external) partners. These preparations will help ensure that the industry cloud becomes your intelligent orchestration platform – the set of applications and technologies that support getting the right data to the right place at the right time. Technology can enable data security, compliance, and IP protection, as well as improve collaboration and automation, but only if users know how to use it to its full potential and company data governance policies are consistently implemented.

The adoption of industry clouds continues as enterprises across all industries see faster time-to-value in the cloud, improved industry compliance, more effective data models and AI/ML tools, convenient cloud consumption options, and better collaboration with partners. Companies in the highly regulated industries were some of the first to adopt industry clouds but today, organizations in most major industries are actively deploying or planning their adoption. More importantly, there are plenty of options on the market for them to choose from.

To find out how your industry peers are leveraging this new cloud model or learn more about the industry clouds available for your vertical, keep current with the research in IDC’s Industry Clouds, Solutions, and Technology Verticalization Strategies service.

If you have been involved in data, analytics, BI, or AI domains for at least a few years, you have undoubtedly encountered the following situation. Someone in a business unit has a need for information to inform a decision and upon starting a conversation with one of their technology colleagues is faced with a question from the latter “what data do you need?” The response often takes one of two forms, “I don’t really know; give me all the data you have” or “I need this one particular data set to address my immediate need or project.”

The response from the data professional usually addresses the question literally — delivering a data set or access to requested data via a plethora of data management and data visualization technology, from data warehouses and data lakes to dashboards and reports.

Over the years, greater processing power, more automation of data pipelines, and better data visualization have combined to continuously speed up the delivery of data to business users. One might consider that a success, but only if the speed of data delivery is the metric that matters. But does it?

What happens when data is delivered to a business user? The person may be able to look at a trendline; they will likely be able to compare the current period’s performance of any given data point to previous periods. Some organizations have software to highlight to the user anomalies or deviations from past norms; some organizations have even begun to incorporate ML-based automation to help users with autogenerated forecasts of how a given trend might evolve.

However, what this information delivery functionality does not provide is support for the process of decision making, which remains largely the responsibility of human “wetware.” We see the data and we use our experience, mental models, discrete data points from dashboards and reports to assess options that lead to a decision. This approach to the decision-making process lacks scale, governance, orchestration, and technical support for all the steps within a decision-making process — a process that does not stop with the delivery of data to an individual.

This approach to decision making is not sustainable. To achieve better decision making, it is not enough to invest in the best data lakehouse, best data science team, best AI technology, or best data visualization tools.

Our contention is that one of the biggest shortcomings plaguing most organizations is the use of the question “what data do you need?” We propose that the solution to overcoming substandard returns on investment in data, BI, and AI starts with changing the question to “what decisions do you need to make?”

For a more detailed discussion of decision intelligence challenges, opportunities and benefits see A Case for Decision Intelligence: From “What Data Is Needed?” to “What Decisions Need to Be Made?”

Decision Making

At its core, decision making is the process of orienting the decision maker, using all relevant information, toward selecting one of two or more alternatives based on probabilities of success and decision execution constraints.

Accessing and organizing data into a form that makes data available for analysis.
Analyzing data using descriptive and predictive methods, including AI.
Recommending a decision: defining and presenting likely alternatives or options.
Deciding by selecting an option based on constraints and goals. 
Executing a decision by acting based on the decision made.
Monitoring and evaluating results of the decision and action.

Given the opportunity to accelerate decision velocity through AI-powered decision intelligence while facing technical, operational, and organizational roadblocks, leading organizations are making a clear choice in how they allocate their investments – they are tightly connecting the six steps of a decision-making process and deploying decision intelligence software.

Decision Intelligence Software

To be considered a decision intelligence software by IDC, the software product should be packaged for the purpose of fully or partially automating all steps in the decision-making process. Decision intelligence software includes capabilities for decision design, engineering, and orchestration.

Decision design. Decision design provides functionality for users to define business goals or desired outcomes; map decision-making workflows, including feedback loops and approval points; and identify choices and constraints that limit the options available to decision makers.
Decision engineering. Decision engineering provides functionality to organize the available data using semantic and/or ontological frameworks, then analyze the data using a range of use case–specific methods that may include simulation, optimization, descriptive, and predictive analysis.
Decision orchestration. Decision orchestration provides functionality to monitor all elements of the decision-making workflow (e.g., rules, algorithms, data sets, approval hierarchies, goals, constraints, decisions, and so forth) and enables their ongoing or periodic adjustment based on automated and human-generated feedback loops.

Note that to see this decisioning plane in the context of the other three planes of an enterprise intelligence architecture see IDC blog post Navigating the Planes of Enterprise Intelligence Architecture.

Together, these capabilities enable not only the collection and analysis of data, but also all the capabilities shown below.

Note that IDC analysts are currently in the process of working on an assessment of a select set of decision intelligence software vendors, which will be published in Q1 2024.

Benefits and Promises: Why Should You Care About Decision Intelligence?

Every organization wants to traverse the decision-making process as fast as possible. In some cases, going through one cycle can take hours, days, weeks, or even months. In other cases, it can happen almost instantaneously. However, speed is only one factor that leads to greater decision velocity; the other is control.

We define control as decision governance, but also as quality, accuracy, and contextualization. Speed and control in decision making must be balanced. Having control of the decision-making process matters, but it cannot come at the expense of speed or the organization risks having lower adaptability when the inevitable time for course correction arrives. Decision intelligence can play a critical role in:

Identifying decision-making processes as they exist in ongoing operations rather than as envisioned in outdated process documentation.
Improving retention of knowledge about how decisions are made and by whom.
Ensuring decision-making consistency across the organization and its decision makers.

Together, these benefits, in turn, are foundational to the achievement of desired business outcomes, such as increased revenue, lower costs, improved margin, and so forth. To achieve success and differentiation in the digital era, organizations should consider the following:

Guide technologists involved in data engineering and management, and even more importantly, those involved in data analysis, data science, and AI, to pivot from asking what data their constituents need to what decisions they need to make.
Evaluate decision attributes to identify best opportunities for decision automation and decision augmentation, respectively. Differentiate between data-driven and data-informed decision making. Highlight the role of people versus machines and shortcomings and competencies of both.
Begin developing a cadre of staff with specialization in decision design, engineering, and orchestration.
Invest in data literacy initiatives to ensure all staff become more comfortable with embracing uncertainty and develop a healthy skepticism of recommendations generated by opaque systems.
Ensure the decision intelligence software provides the necessary transparency via decision monitoring and cataloging to engender trust among all users.
Reconsider the goal of your data, analytics, and AI initiatives and investments. If they are not helping improve decision velocity, consider reallocating resources to projects that are.
Focus on change management. One of the biggest challenges facing organizations is to address the growing gap between the exponential growth of technology for AI-powered automation of decision making and slowly evolving the ability of people to accept and adopt the new technology into their routines.

The technology landscape is rapidly evolving. External pressures placed upon the business, coupled with innovations across IT – from developments in hybrid and multicloud, to the introduction of commercial Generative AI – are placing an increasing amount of attention on cloud computing. Evidence shows that those organizations with a focus on building hybrid and multicloud foundations benefit most from having the ability to rapidly adopt new technologies at scale as they initiate new products and services and drive cost and resource efficiencies across the business.

This realization is causing seismic shifts, not only around how cloud is consumed but how cloud buyers make investment decisions and measure return on investment. To capture these changes in the market, International Data Corporation (IDC) has launched a new Continuous Intelligence Service (CIS) called Cloud Adoption Trends and Strategies that has been designed to uncover the changing requirements of the cloud buyer.

Cloud Adoption Trends and Strategies provides insight into what drives cloud buyers to make certain decisions as they purchase cloud computing and the services and applications that will live in the cloud. Now, more than ever, the cloud market requires deep understanding in the way organizations are looking to create a more customized cloud experience. Cloud service provider, platform, hardware, and application choices will influence how cloud buyers’ businesses progress through future digital transformation projects, especially those surrounding data and AI. These are all covered in depth in Cloud Adoption Trends and Strategies.

Cloud – A Foundational Choice With Tangible Benefits

Cloud adoption drives innovation. The following image shares results which show a competitive advantage in terms of new technology adoption for buyers with broad cloud estates.

Are cloud buyers innovating or ‘staying the course’ in 2023?

Cloud teams have had to pivot fast over the last few years to be pandemic-prepared, inflation-proof, and to meet a raft of new demands, from rationalization to competitive business operations. Many have had to think on their feet, reduce development times for new projects, and find ways to be savvy despite a cloud skills shortage. 

The number of cloud buyers that say their business is ‘staying the course’ in terms of attitude towards innovation is slightly higher this year than last. More than half of cloud buyers, however, recognize they need to be among the first to adopt new technologies to stay ahead of the game, and a large proportion that may not be fast movers say their business still wants to be ‘fast to act’. These figures already show that cloud buyers are placing emphasis on innovation. When looking at cloud budgets, IDC finds that around half of that budget is, in fact, attributed to new projects and innovation, as opposed to cloud operations and maintenance.

For cloud buyers that are already mature in cloud (many of which will already be adopting hybrid and multicloud strategies) the focus on adopting new technologies is much higher than the average cloud buyer – 71%. These organizations have laid foundations that foster innovation. Building out hybrid and multicloud environments enables them to free up financial and human resources and encourages them to innovate by providing access to new technologies and deployment models.

The Different Cloud Conversation

In 2023, the cloud buyer’s conversation with those supplying products and services is different to what it was even a year back when a lot of conversations were focussing on cost. There is still a strong focus on innovating and delivering new products, but cloud buyers also demand outcomes around data-driven strategies, with many taking a data and application-first approach to deploying on cloud. Conversations around interoperability, contractual choice and ecosystems and marketplaces all factor highly. These are happening at the same time as customers are demanding options that enable them to have choice around underlying hardware, and guarantees around performance and latency, connectivity, scalability, and options for cloud management, sustainability, and operational efficiency.   

How vendors approach these conversations can make a huge difference. In Q3 2023, IDC Cloud Pulse found that improved customer services led many cloud buyers to increase their spend with a cloud provider. A misalignment of performance requirements and resource requirements led to decreases in spend. Over a fifth of cloud buyers with broad cloud footprints that chose to discontinue services with a provider said the main reason for doing so was because they found a vendor with a more appealing switch plan, and those with newer cloud deployments were impacted by a perceived lack of expertise and poor customer service. The ability to tailor products, services and guidance for these organizations can make or break a cloud rollout and a cloud contract.

It is not just the cloud service providers that need to pay careful attention – 60% of companies deploying cloud today also employ professional services for delivery and management of their cloud services (Cloud Pulse Q3 2022).   

Understanding the Cloud Buyer

Drawing upon insights uncovered from IDC’s quarterly Cloud Pulse survey, Cloud Adoption Trends and Strategies can provide insights into how cloud buyer’s requirements differ across industry verticals, regional markets, and types of organizations. As the cloud market becomes increasingly hybrid, cloud demands themselves are becoming more individual, and complex. Just as cloud buyers have a head-start on innovation when laying hybrid and multicloud foundations, service providers and vendors can have a head-start on understanding cloud buyer requirements by learning about their challenges and business needs.

For more information on Cloud Adoption Trends and Strategies, contact IDC research director Penny Madsen at pmadsen@idc.com.

Additional Resources:

IDC solutions for teams requiring custom data-driven strategies.
Business Value support and solutions