Key factors to consider when integrating emerging technologies into tech product portfolios
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Emerging Technologies and Trends for Tech Product Leaders
Scope the tech landscape for innovations and opportunities
Review the Gartner Emerging Tech Impact Radar to understand which technologies and trends have the most potential to disrupt a broad cross-section of markets.
Download the 2023 radar to see in simple graphic form:
- 26 of the highest-impact emerging technologies and trends
- Their role in driving change, e.g., enabling productivity and next-generation AI
- How big a market impact each will have
- The rate of adoption for each
Emerging technologies and the tech product landscape
In the hypercompetitive, rapidly changing high-tech industry, technology product leaders must act decisively to spot, evaluate and monetize emerging technologies in their portfolios.
- Tech Discovery
- Tech Market Strategy
- Tech Monetization
- Tech Product Integration
Key to product strategy is gauging the impact of emerging technologies and trends
As a tech product leader, you need to evaluate the impact that emerging technologies and trends will have on the technology market in terms of:
Breadth of impact (number of industry and geography segments impacted)
Depth of impact (level of transformation it’s likely to generate)
Speed of impact (how quickly the technology is approaching early adopter stage)
Issues to watch:
What are the emerging trends and technologies, and how should vendors respond?
Who are the innovators, and how are they driving the technology forward?
When will new technologies have a substantial impact on existing markets?
For example, the market for generative AI is rapidly evolving and is expected to have a significant impact in three to six years. Generative AI is a type of AI that learns patterns or structure from existing data, and uses that information to create entirely new content, which can range from media content like text, images or audio to synthetic data and models of physical objects.
Right now, there are many small tech startups in the space, but the technical offerings are fragmented and require a combination of tools rather than a single streamlined process. Plus, the cost of training large generative models puts this technology out of reach for most vendors.
However, GenAI has the potential to have a high impact across industries ranging from life science to media to aerospace to energy and beyond, with proven use cases from creating new molecules and accelerating drug development to preserving data privacy.
When combined with existing development automation techniques, it has the potential to automate up to 70% of the work done by software developers.
GenAI actions for tech product leaders
Determine the impact of generative AI on your target industry and domains by assessing the potential use cases, and prioritize specialized industry and domain solutions when incorporating generative AI in your products.
Prioritize opportunities where AI-generated data could benefit your existing product offerings by examining key pain points in AI development. For example, to accelerate the AI development cycle, lessen regulatory concerns and lower the cost of data acquisition.
Examine and quantify the advantages and limitations of generative AI by analyzing where generative AI could bring breakthroughs as it requires skills, funds and caution, and then weigh technical capabilities with ethical factors.
Develop a rigorous AI trust, risk and security management (AI TRiSM) program to ensure AI model governance, trustworthiness, fairness, reliability, robustness, efficacy and data protection for AI integrated into your AI solutions.
Study tech market patterns to capitalize on emerging tech opportunities
For tech providers to capture the market potential of emerging technologies, they have to identify high-potential opportunities and then determine the path to sustainable competitive advantage. In the process, they need to:
Craft a successful go-to-market strategy
Achieve a differentiated market positioning
Build valuable channel relationships
You’ll need to determine the competitive landscape and then strategize how to pursue your chosen segment and market and sell the chosen technologies. Among the issues to consider:
Differentiated value propositions
Overcoming buyer objections
Prime market segments
Beneficial partners and ecosystems
New business models
Build market-potential estimates into your tech product strategies
Consider as an example of market estimates, the growing interest in conversational AI avatars. Gartner estimates that by 2025, these human-like virtual personas will support 70% of digital and marketing communications, up from less than 5% in 2022.
Conversational AI avatars combine computer-generated imagery, natural language processing, synthetic voice and emotion AI to create representations of both people and brands. GenAI will make it easier to humanize avatars to create a more personalized and engaging customer experience.
Some organizations are also exploring how AI avatars can exist in the metaverse, representing digital versions of people called “digital humans.”
AI avatars rely on technologies, including advanced virtual assistants, multimodal user interfaces and graphic creation tools, that are in various stages of evolution and adoption, but the market potential for tech providers is significant with diverse use cases, especially in marketing, sales and employee communications. These tools are also relatively easy to develop and scale.
Actions for tech providers sizing AI avatar opportunities
Identify where AI avatars can add business value to software products, including as brand ambassadors, customer care representatives or in the metaverse. AI avatars can also be used for content generation, marketing and training material for audience engagement.
Begin to experiment with the technology now, regardless of the maturity of AI avatar technical components, to stay ahead of competitors.
Partner with creative and media agencies as a way to accelerate the adoption of this technology.
Consider the ethical and legal implications over the long term. Ensure permissions and contracts for using individuals or organizations in personalized AI videos and how to communicate to the customer that these videos have been generated using AI.
Develop economics-based strategies to monetize emerging technologies
Turning emerging technologies and trends into profitable revenue growth requires tech product leaders to address fundamental economic and business model options. Market sizing projections, customer value estimations, unit economics, profitability analyses and models of cannibalization risk and pricing are needed to evaluate monetization opportunities.
Tech monetization models are complex
Even when market opportunity seems to exist, tech providers must be able to turn that opportunity into revenues. Consider the scenario for application programming interfaces.
In a March 2022 Gartner survey, 94% of the respondents reported using third-party APIs, up from 52% in 2019. This reflects a trend that tech providers know well: Today’s customers increasingly expect them to offer APIs for integration and composable applications.
For tech providers, APIs can increase product value, attract new customers and grow revenue. But current pricing models for SaaS products don’t always support customer demand.
Gartner expects that by 2025, 75% of application providers will have revised their current product packaging and pricing models to support customers’ consumption of APIs and building of composable applications. But what exactly does that revenue stream look like?
Sample tech monetization challenge: Making money from APIs
The first step in transitioning to an API-first approach is to take into account whether the APIs are part of an existing product or if they will be part of a new product — and the pricing model aligns in one of three ways.
Indirect Pricing: For APIs that are part of an existing product, the current pricing model will affect how the organization can monetize APIs. For instance, user-based organizations that can shift to a value-based pricing model can align with consumption — shifting the unit of metrics away from number of users to the number of transactions, pages downloaded or other metrics suited to the specific organization.
Direct Pricing: For APIs being offered as a new product, tech providers can consider more direct pricing options. If the APIs vary in worth, charge individually for each API or individually for select APIs. If not, consider tiered pricing — which Gartner research shows is preferable to pay-as-you-go for many customers and providers.
Hybrid Pricing: A third option is to combine the two methods to offer variety but maintain your product’s user-based pricing. This hybrid model can offer daily API entitlement based on a number of licenses, but also offer the flexibility of additional calls for an added fee.
Monetization opportunities also don’t last forever, so it’s important for tech product leaders to understand the context in which they are pricing emerging-technology products and services. In the case of APIs, for example, it will become harder to make money from APIs as:
Customers come to expect API access as a standard (not premium) feature
Application architects get more comfortable with their choices of enabling products
Enterprise development and operations teams gain the skills and experience to do much more on their own
How to integrate emerging technologies into tech product and service roadmaps
For tech product leaders to incorporate emerging tech into product and service offerings in a way that drives competitive advantage, they need to consider the following about those technologies:
Common, unique and must-have features and functions
Primary use cases
Ways in which the tech delivers value to customers
Adoption and demand-growth rates
Methods for delivering differentiated products and services.
Sizing growth of emerging innovations like metaverse
Metaverse technologies have the potential to fundamentally transform how organizations do business, but the hype and deflated expectations have both sidetracked many real conversations about the potential for tech product portfolios — especially when money can’t be made from technologies when users are wary.
Despite the limitations, Gartner still expects that by 2026, 25% of people will spend at least an hour a day in the metaverse for work, shopping, education, social and/or entertainment purposes, and the mature metaverse is far into the horizon.
Still, product leaders need a product vision across the three stages (see figure) through which the metaverse is likely to evolve and during which the trends and technologies that combine to enable metaverse interactions and experiences will themselves evolve at various speeds and acceptance rates.
This can feel overwhelming, but product leaders need to understand three key areas:
Which emerging technologies and trends within the metaverse are most relevant to them in each phase.
When one or more relevant emerging technologies and trends should be adopted.
How current offerings and strategic roadmaps fit into the broader metaverse concept.
Also, while specific technologies are still evolving, the metaverse will feature five key building blocks that product leaders should consider as they develop their product roadmaps.
Content and context: Digital representations of all items and locations within the physical world that will overlap with our physical reality.
Decentralization: This will enable new possibilities in terms of information distribution, encryption, tokenization and immutability, providing new capabilities in managing digital assets and exposing new commercial possibilities.
Experiences and interfaces: While there will be other devices and applications to experience the metaverse, the most popular will be via immersive technologies like augmented reality, mixed reality and virtual reality.
Computer and infrastructure: Many aspects and experiences of the metaverse will develop out of a shift in computing toward more distributed and decentralized processing and storage.
Sensing: Data captured by a multitude of both stationary and nonstationary sensors will provide deeper insight into environmental context, enabling highly personalized interactions within the metaverse.
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