The digital airwaves and social media feeds have recently gone wild with examples of how the AI-driven chatbot ChatGPT has solved riddles, generated high school essays and explained why the Croatian football team has outperformed similar sized nations at recent World Cup Tournaments. Understandably, it has again raised important questions about the impact of AI on our lives, enterprises, and broader society.

First and foremost, let’s start with definitions. What is Generative AI and where does OpenAI/ChatGPT fit within all of this? Generative AI is a branch of computer science that involves unsupervised and semi-supervised algorithms that enable computers to create new content using previously created content, such as text, audio, video, images and code.

ChatGPT (which stands for Chat Generative Pre-Trained Transformer) is a chatbot developed by OpenAI. ChatGPT is built on top of OpenAI’s GPT-3.5 family of large language models (LLMs) and is fine-tuned with both supervised and reinforcement learning techniques. It is being hailed as the smartest chatbot ever developed. OpenAI was founded in 2015 (initially as a non-profit organization) and early investors included Elon Musk & Peter Thiel. In 2019, it became a for-profit organization and inked a $1bn deal from Microsoft. This deal allowed it to use Microsoft’s Azure Cloud Platform for its research and development; and in return, Microsoft was given the first opportunity to commercially leverage early results from OpenAI’s research. OpenAI has a stated goal of promoting and developing friendly AI in a way that benefits humanity as a whole and is viewed as the leading competitor to DeepMind (acquired by Google in 2014 for $500M).

It is important to understand that while ChatGPT is a good example of generative AI technology, the market segment is much broader.  LLMs began at Google Brain in 2017, where they were initially used for translation of words, while preserving context. Since then, large language and text to image models have proliferated at leading tech firms like Google (BERT and LaMDA), Facebook(OPT-175B and BlenderBot) and OpenAI (GPT-3 for text, DallE-2 for images and Whisper for speech). Online communities (e.g. MidJourney), open-source providers (e.g. HuggingFace) and startups such as Stability AI have also created generative models. In Q4 this year, a spate of text-to-video models from Google, Meta and others have emerged. Generative models have largely been confined to larger tech companies because training them requires massive amounts of data and computing power. But once a generative model is trained, it can be “fine-tuned” for a particular content domain with much less data.  Today, Generative AI applications largely exist as plugins within software ecosystems.

The questions that technology and business leaders should be asking in terms of what Generative AI means for the enterprise are outlined below:

How will it be incorporated in existing enterprise technology environments?

Code Generation – GPT-3 has proven to be an effective generator of computer program code. GPT-3’s Codex program is specifically trained for code generation and works well when given a small function. Microsoft’s Github has a version of GPT-3 for code generation and is called CoPilot. The latest versions of Codex can identify bugs and fix mistakes in its code and can explain what it does occasionally. The goal for these tools is to not eliminate programmers but to make tools like Codex and CoPilot “pair digital assistant” with humans to improve their speed and effectiveness.
Enterprise Content Management – Vendors in the Headless content management space are incorporating these types of generative AI tools for both content generation and recommendations. This is to deal with the increased content velocity as additional forms of content are based on a single source generated by AI with human oversight.  It is not being used to write whole copy, but rather an outline for the content author to use as a draft.  In addition, there it is likely to impact GUI design in the form of “generative design” with the likes of Figma or Stackbit potentially including generative AI capabilities in as part of collaborative interface design engines.  
Marketing and CX Applications – Outside of the use of content generation for advertising and marketing along with the automation of marketing campaigns, the primary application for early versions of generative AI is in AI driven chatbots and agents for contact centers and customer self-service such as employed by Salesforce and Genesys, and these have initially delivered mixed results. However, this next generation of capabilities will mean a broader range of interactions, more accurate answers, and lower levels of required human interactions which will result in higher adoption and eventually more training data for the models. In the near future, generative AI will become more prevalent in the creation of personalized product recommendations through insight analytics, better and deeper customer segmentation as a steppingstone to true personalization and contextualization of experiences, and better understanding customer satisfaction and performance.  
Product Design & Engineering – It will also affect technologies in the product lifecycle management (PLM) and innovation space with the likes of Autodesk, Dassault Systemes, Siemens, PTC and Ansys continuing to build capabilities to enable design engineers & R&D teams to automate and expand the ideation and optioning process during early-stage product design, simulation, & development.  Generative AI design would allow options for engineering and R&D teams to consider in terms of structure, materials, and optimal manufacturing/production tooling.  For example, it would potentially suggest a part design that optimizes against factors like cost, load bearing, and weight. Generative design can also enable reimagining of product look and feel, often resulting in unique aesthetics and form that is not only more compelling to end users, but more practical and environmentally sustainable. Many of these vendors have attached their generative design offerings to additive manufacturing capabilities that are needed to realize these unique products.  Opportunities exist across multiple industries for generative design.  Automotive, aerospace, and machinery organizations can improve product quality, sustainability, and success, while life sciences, healthcare, and consumer products companies can improve patient outcomes and customer experiences.   

What are the pitfalls?

Generative AI, while providing lower-cost, higher-value solutions, has significant ethical and perhaps legal implications. There are significant questions over issues like copyright, trust and safety. Organizations must consider issues such as privacy and consent around data, reproduction of biases and toxicity, generation of harmful content, sufficient security against third-party manipulation, and accountability and transparency of processes. Neglect of AI ethics isn’t just a moral quandary – it is a significant business risk that means less trust, less control, and less ability to advance the models in an optimal way. Businesses must take a multi-pronged approach to AI from developer to end-user, first and foremost guided by a framework including principles that appropriately consider all ramifications of AI. Businesses should also choose models where techniques such as adversarial input (training against bad or manipulated data), benchmark dataset training (checking for biases via label tests), and XAI (explainable AI) are used. Finally, concerns with AI ethics are intrinsically linked to how accountability measures are enacted. Businesses should ensure they take a Human-in-the-Loop (HITL) approach to ensure minimal model drift, rigorous monitoring of output, and continuous improvement. AI must not be viewed as an independent, black box entity, but should rather be seen as human-computer interaction where optimal usage comes from deep understanding, meticulous monitoring, and striving for accuracy of the model.

How will it affect jobs?

At the end of 2020, the World Economic Forum (WEF) predicted that AI would displace 85 million jobs by 2025. The main jobs it identified under threat would be the likes of data entry clerks, administrative assistants, accounting and auditing professional amongst others. By the same timeframe, it predicted that 97 million new jobs would be created as AI becomes more mainstream in the enterprise. Growing job demand would focus on data scientists, process automation specialists, digital marketing and strategy experts as well as many other more roles. Generative AI means that we can add a new role to that list – prompt engineers. Basically, this role focuses on working out what to type into AI chatbots to get the best out of them. Some would expect these individuals to also deal with so-called ‘hallucinations’ – where Generative AI gets it completely wrong. These types of entirely new job descriptions highlight how an emerging technology not only displaces activities, but also creates new ones. The classic creative destruction principle initially outlined by Schumpeter. However, for business and technology leaders – it does require a dynamic and ongoing assessment of required digital skills including continuous gap analysis and roadmaps to ensure that the necessary capabilities are available to support the digital business of the future.

Moving forward, the best place to watch new and interesting generative AI use cases is in the start-up and scale-up space. The likes of Jasper (Copywriting), Stability AI (Visual art), DoNotPay (Legal Services), Omnekey (Creative Content), Paige.ai (Cancer diagnostics) and Mostly.ai (Synthetic data) showcase how quickly this space is fueling a range of game changing innovations across the industry – and potentially what’s around the corner for so many industries. It is incumbent on all of us to ensure that we approach this fascinating space with the right balance of curiosity and skepticism.

The number of digital native businesses (DNBs), start-ups, and scale-ups has grown exponentially over the past decade and now represents a significant market cap. Despite the economic downturn, Digital Native Businesses should be on your radar as they drive economic growth, create new jobs, and foster innovation. However, their DNA differs from traditional organizations as they are completely cloud driven.

What is a Digital Native Business?

IDC defines Digital Native Businesses (DNBs) as companies built from the start around modern, cloud-native technologies, leveraging data and AI across all aspects, from product development to logistic operations and customer engagement. By leveraging new and emerging technologies, platform services, and marketplaces, DNBs grow and scale fast, disrupt industries, and create new markets.

DNBs set the pace when it comes to product innovation and development; they set new standards in customer intimacy and customer experience. As technology gives them their competitive advantage, even more important in today’s economic environment, they should be on every tech vendor’s radar.

Simone De Bruin, Research Director, Worldwide Digital Native Business, Start-ups & Scale-ups, IDC

DNBs encompass a wide range of both B2B and B2C enterprises, from food delivery to carbon sequestration to cryptocurrency. There are five defining characteristics of DNBs, distilled below as follows:

Use tech as differentiator – they depend on UX-driven innovation cycles, and use tech to compete or to monetize their services and products. All core value and revenue-generating processes are dependent on digital technologies.
Are born digital – they are cloud-native and data-driven and being digital is part of their DNA. They have a tech-driven operating model.
Scale and innovate at speed – with tech-savvy developer and data scientist teams, DNBs aim for rapid growth.
Have an ecosystem-centric approach – they are highly marketplace-driven and a DNB leverages its ecosystem of stakeholders to drive community-led innovation, dynamically evolve, and co-create offerings.
Significantly funded – whether it’s venture capital, bootstrapped, or crowdfunded, DNBs enjoy a high degree of funding to support their ambitious growth ambitions.

Why it’s Important to Pay Attention to DNBs

Despite the economic downturn, DNBs should be on your radar. There is a number of reasons for this, but mostly it boils down to a combination of their innovative mindsets, focus on customers, and ability to rapidly scale, leveraging new technologies. Other factors that help set the trend:

The number of DNBs continues to increase, and the time it takes startups and scale-ups to grow into unicorn status decreases (average 6 years)
At the same time, traditional enterprises are fading as a concept as their lifespan decreases.
Compared to 2021, VC investments in 2022 have dropped. Over the longer term though, VC investments and startup initiatives have grown significantly. 2022 again broke records on fundraising levels as well as on dry powder (funds to invest) available.

The rise of DNBs has been set in motion two decades ago and they will continue to be a major source of innovation for some decades to come. The value that startups create is nearly on par with the GDP of a G7 economy. As Eynat Guez, co-founder and CEO of Papaya Global says, “Technology startups are more than catalysts for growth. They are the engine of growth itself. They solve problems no other sector is addressing with innovative thinking, thus pushing society forward – all while creating jobs, stimulating the economy, and attracting foreign investment.”

DNBs scale rapidly and can generate returns unparalleled to those in traditional enterprises. Vendors who aim to engage with DNBs in the traditional way will find it difficult to keep up with their fast-changing requirements. In addition, they represent different opportunities as well. IDC sees vendors engaging in three ways:

Invest / Acquire – CVC invests in young & emerging startups, and scale-ups. For example, Salesforce Ventures, M12, Workday Ventures etc.
Partner / Accelerate – Vendors partner with DNBs, start-ups, and scale-ups, or they engage with them through accelerator or incubator programs. Examples include SAP.io, and IBM Sustainability Accelerator.
Sell-to – Focused on a commercial relationship where DNBs, startups, and scale-up are a new customer segment.

Although there is a fair amount of overlap between these categories, many initiatives are still disconnected and siloed. Vendors should take a more holistic approach to engaging digital native businesses to build longer relationships, partnerships, and commercial relationships. Once a vendor is clear on the type of engagement, vendors need to assess what type of DNBs fit that purpose. The following segmentation looks at the DNB business model:

IDC distinguishes:

Technology Providers. Providing third-party organizations with next-generation technology products or services (e.g., a chatbot for enabling better customer experience in retail banking, a SaaS tool for analyzing space imagery).
Technology Enabled – B2B. Offering products or services to businesses where those services are enabled by next-generation technologies at the core (e.g., a B2B e-commerce company connecting businesses with suppliers and manufacturers or a B2B trading platform for SMEs to source products from distributors and wholesalers).
Technology Enabled – B2C. Offering products or services to consumers where those services are enabled by next-generation technologies at the core (e.g., a car-sharing service enabling consumers to book and locate cars on the fly using an app, a social network for dating, a video-streaming application).

Vendors will look to identify, or recruit, technology-oriented DNBs to invest in, or partner with. The B2C and B2B types of DNBs are more likely a new customer segment. Vendors who want to engage with the next Snowflake, Uber of Instacart need to first be able to identity what type of opportunity they provide, and then have the system in place to cherish and nurture them to grow. 

Taking this segmentation one step further, the following subsegments can be distilled to look at high-growth opportunities. Each category within the DNB landscape consists of various functional markets. These functional markets are not mutually exclusive. Metaverse (tech-driven) solutions will impact the (tech-enabled B2B) retail industry that eventually effect the (tech-enabled B2C) consumer tech in fashion for example.

The lifeblood of the Future Digital Economy

DNBs are the lifeblood of the future digital economy. With growing investments in digital natives and an exponential growth of companies born in the digital age, digital natives start to command a sizeable portion of tech spend. However, that tech spend will differ for each of the different categories as defined by IDC. ​As DNBs exert a strong influence on the market, they should be on the radar of any tech provider. Tech is their key competitive advantage. Even during an economic downturn, they are not likely to downsize their IT investments. However, to get traction and increase engagement there is great value in understanding their business operations, what enables their success, and what their IT requirements are.

If you’d like to learn more check out our latest research here, or contact Simone de Bruin, Research Director, WW Digital Native Business, Start-ups & Scale-ups
At the moment we are collecting data from digital native businesses. So, if you are a startup, scale-up, or mature digital native business and would like to participate in our research – and receive our CEO tech book as a thank-you gift, please click this link to participate!

At IDC Cloud Pulse, a quarterly survey that takes in views from up to 1,700 cloud consumers, we have been tracking how companies are being impacted by, and how they are responding to, macroeconomic trends. We then consider how this relates to the consumption of cloud. Questions around Inflation and Energy Costs were added to our survey first in Q1, 2022. The Possibility of a Recession was new in Q3. And we will now be adding Recession into future surveys.

These new additions tell a story about the challenges businesses have had to endure over this last year. Not as much, however, as the responses we received when we asked about the likely impacts to businesses.

Each quarter companies are asked what events are most likely to cause disruption to their business over the course of the year. They can respond using a sliding scale of 0-10 with 10 being the highest amount of disruption. The above results show only those responses between 8-10 (what we would consider as ‘high impact’).

During Q1 and Q2, not too many more than a third of companies said they felt they were experiencing major impacts from the macroeconomic trends we asked about. In Q3, this rose to around a half.  Inflationary costs were seen across more of the business, energy price rises became a reality, and national and global recessions became less likely to avoid. At the same time, markets still battled ongoing supply chain challenges, in part brought on by continued to reactions to the COVID pandemic. Many of these impacts are intertwined, making the current macroeconomic landscape even more difficult for companies to navigate.

IT Budgetary Impacts

During Q3, for the first time, we saw companies shift towards a more pessimistic view of the business environment. Early indications from our Q4 data suggest we will see even higher rates of pessimism moving forward.

This pessimism impacts budgets. Cloud makes up around 31% of IT budgets – this is up only slightly from around 30% seen in Q4, 2021 despite previous years showing higher annual rates of growth in terms of cloud as part of IT budgets. The real challenge is, however, that IT budgets are decreasing when viewed as a proportion of overall company revenue. As we know, company revenue is also being challenged (and where it isn’t, margins are suffering from increased costs).

If we focus on the area of inflation – where we see some of the earliest impacts on industry in terms of economic stress – we gain valuable insight into why we are seeing budgetary constraints across IT. Companies operating or consuming a cloud environment first felt inflationary pressure in the form of increased professional services costs, and then application software subscriptions. These are two areas where companies are more likley to operate with rolling monthly contracts.

During Q3, we started to see more companies (25%) saying they could now see increases across private cloud infrastructure (most likely brought about as a result of increased equipment and energy costs). Internal skills costs were also a major challenge.

Public Cloud as a Response

Responding to these challenges, a quarter of respondents said they will be looking to migrate more of their environments to public cloud/ Software-as-a-Service. Around the same amount again (23%) said they will be looking to find reductions for spend across their cloud estates.

With almost a fifth (18%) of respondents saying they are still waiting to assess the impact of inflationary pressures, these figures could grow.

Cloud Pulse findings also show that the number of companies that say they are ‘Public Cloud First’ when it comes to their cloud adoption strategy is more (32%) when companies say they are directly impacted by inflation compared to just 21% for those that are not.

Note, the rate of Public Cloud First companies has been decreasing annually. Q3 marks an increase overall in the number of companies relying on Public Cloud before taking a hybrid approach – though hybrid remains the number-one approach to adoption).

Qualities that Count

Another shift is in the qualities companies are now requiring of their cloud providers. In Q3, 2020, the most important Company Attribute sought by cloud consumers was a global/international footprint. What companies now seek are providers they can label ‘trustworthy’.

In this case, Trustworthy relates to a provider’s ability to be reliable and responsive, to meet contractual requirements, to be transparent about pricing and to keep promises made to the business (we did ask). During this time of macroeconomic uncertainty, customers also want to work with technical experts that can deliver services at speed with guarantees along the supply chain.

We also see shifts in what companies require in terms of cost and pricing. In 2020, the focus was on flexible payment terms and enterprise-wide agreements. Now it is more about flexbile licencing and credits that allow companies to move their applications and workloads across cloud and even non-cloud environments as they require.

Many companies know their business could have to alter the way they consume and gain access to IT over the coming year. Where rising costs – from inflation or energy increases – are already felt the requirement for flexibility gives way to predictable pricing,  with many IT departments focussing more on cost optimization and forecasting.

Many cloud vendors have already started to notice these shifts across their own businesses, from customer conversations they are now having to the budgtary bottom line. Those who will succeed will be those that can quickly pivot and reinvent solutions – in particular with the right financial models.

Companies want to continue to benefit from cloud as they navigate uncertain times but companies will be more selective about what they are deploying and how over the coming year. Many will be taking stock on current digital transformation agendas and application portfolios to create leaner, more efficient, IT responses to current business needs.

For more on these key cloud trends, including how individual providers aligned to current market sentiments and needs, ask further about IDC’s Cloud Pulse data. Cloud Pulse’s rich insights cover a range of cloud topics from deployments to application landscapes, vendor selection, ROI and more.

Developing compelling content, messaging, and campaigns that engage your target audience and move your prospects and customers along the buying cycle is not a simple task. In another IDC blog post, we discuss the challenges of a hyperaware buyer, with a decreased attention span. It’s a time and cost struggle to create the varying content that your buyer is looking for at each stage of their journey.

An effective way of obtaining content for your marketing messaging is by licensing it from a research company. With this method, you obtain digital distribution rights to the research document so you can share it with your target audience in various marketing formats. Licensed content reach can be easily extended if you distribute it across multiple channels, in a variety of media, such as, data graphics, quotes, blogs, animated infographics, short videos and social media posts.

Benefits of Licensed Content

Lower cost
Short time to market (generally 1-2 days to delivery)
Analyst insights

Customized content is a more approachable way to building your content calendar. It involves leveraging expert opinion and adapting research into content that includes customized insight and commentary from an industry analyst that validates your marketing messages. Customized content will often come in the form of a white paper or industry spotlight.

Benefits of Customized Content

Developed around your message
Created for multiple formats
Flexible pricing
Leverage the brand equity and trust associated with the research

Marketing programming and operations have undergone a paradigm shift to focus on whole journey marketing, extend into post-sales loyalty, advocacy, and renewal, connect storytelling and content across connected channels, and blend digital into all marketing programming. To serve the digital-first customer, content needs to evolve to meet the needs of a buying cohort and support their full journey, rather than islands of episodic content creation we experience today.

Start with a deep assessment of your end-to-end content marketing practice including evaluating: strategy, talent, organization, governance, business processes, data and enabling technology.  The assessment will inspire a prioritized roadmap to systematically address gaps, challenges and opportunities. Licensed or customized content can fill in the gaps where gaps, such as talent and data, are identified.

To learn more about licensing or customizing IDC’s research content and how to leverage our global analyst insights, contact us today.

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