Everyone has yearly lists of technologies to watch. There are many technologies you should track and test, but the focus here is on the set of technology clusters you need to be successful – the technology itself, the skills and competencies needed to understand and evaluate them. , strategies for testing the impact the technology might have on performance and sourcing decisions.
How will you understand, evaluate and test technologies that can improve, automate, eliminate or reinvent your processes? Do you plan to in-house, co-source or outsource your clusters? My vote? In-source it with a bit of co-sourcing. Once a tech has passed all the tests and you want to scale it, outsource the muscle while continuing to focus on bundling at the ranch. Recruitment is linked to sourcing. Do you plan to recruit tech experts? Will you invest enough to retain them? Are you going to build a real or de facto Center of Excellence around technologies? I don’t know how to bring anything together without an internal team.
Consider consolidating these five technologies in 2023:
1. Low-code platforms
2. Durability requests
3. Data lakes/fabrics/architectures
4. Augmented and virtual reality
5. AI and machine learning
Which ones can you ignore (for now)?
1. The Metaverse
2. Quantum Computing
4. 3D manufacturing
5. Total Experience
1. Low-code application development
“Programming”, as we understand it, will be gone in less than a decade (except the programming needed to develop low-code and no-code platforms). Does this mean that programming will completely disappear? No, but it does mean that a lot of app development will be done on low-code/no-code platforms by “developers” without formal programming training.
It is impossible to overstate the impact of low-code/no-code platforms that allow non-programmers to develop applications. Your low-code team can develop applications faster and more cost-effectively than traditional programming-based, requirements-based application development.
You need low-code/no-code rig jockeys. Expand low-code expertise far beyond the technology department and spread it among your business analysts. 2023 is the year you should accelerate the transition. Find a platform partner and go.
The recently announced partnership between Google and mCloud Technologies takes stock: the marriage between digital technology and sustainability is officially consummated. This partnership will lead to applications that will, among other things, have an impact on our survival. Even the United Nations is focused on “achieving environmental sustainability through digital technology”.
But the focus on sustainability goes far beyond that. The first step? Determine which clusters you need. Are your products and services necessary for sustainability? Are there any opportunities you haven’t considered yet? Google and mCloud will focus on “sustainability apps aimed at reducing carbon emissions around the world”:
“In oil and gas: Digital emissions management streamlines methane reduction programs, combining unique AI-based emissions sensing with Google Earth Engine and 3D digital twin capabilities to measure, locate and fix methane leaks with high precision
“In buildings: AI-powered energy savings automatically eliminate energy waste and minimize energy consumption intensity for commercial and industrial facilities, leveraging solar radiation data from Google Earth Engine to optimize sites demand that are now deploying electric vehicle charging infrastructure
” In the wind : Connected visibility into wind turbine performance, combined with real-time wind and weather data provided by Google Earth Engine, continuously optimizes wind power production, while Google-enabled image processing AI Cloud streamlines wind turbine blade inspection to maximize renewable energy potential.
Where do you live? Are you making something? Do you ship products in containers? Do you deliver anything? Which applications connected to your products and services need to be redesigned? The development and application of sustainability software is exploding. Consumers are ready to adopt apps that enable sustainability.
3. Data Lakes to Data Fabrics from Data Architecture
Data is always king. Or, as the AI community has described it, data is now the new oil. You need a real plan here. In the 20e century, we had databases, which have become data warehouses which are now data lakes optimized thanks to data fabrics. Since most data is unstructured, you have no choice but to create data lakes under the “steerage” of data structure, all of which includes enterprise data architecture.
From an architectural perspective, you want a data infrastructure that enables flexible data analytics and data-driven application development. Now is the time to rethink enterprise architecture away from the hamburger images of the 20e century and see it as a proactive project. How? Start with your own layers of burgers and rethink the “architecture” strategically then tactically – in that order.
Data lakes are non-discriminatory data repositories that enable data analysis of all kinds of data. Data structures speak to an overall parsing philosophy – which you should embrace. Data architectures are the guide, the governance and the glue.
There are many tools that will help you achieve this. Start with your current data providers. If you’re not impressed (through actual due diligence), move on, but remember that in the long run, you need data lake/data fabric/architecture brains. You can leave the strength to someone else. The process should start with the development of adaptive architecture and then reverse engineer into tissues and lakes. This is a moment when the top-down is essential.
4. Augmented and virtual reality
Augmented and virtual reality technology continues to grow (though not as fast as the hype surrounding it). Apple has entered the headset race. Microsoft strikes massive deals with the US military. The gaming industry is fully engaged, and there’s no metaverse – whenever it finally arrives – without AR/VR.
Like many clusters discussed here, AR/VR technology has huge potential – even in industries you don’t immediately associate with AR/VR, perhaps including your own. Your team’s due diligence should focus on the business processes, modeling, and exploration that will allow you to decide whether augmented reality/virtual reality deserves pilots.
If you work in the education, travel, entertainment, gaming, retail, and healthcare industries, you need to designate special teams to explore the potential of AR/VR technology. Pilot applications and create financial models around products and services enabled by technology. Track how your competitors are using augmented reality/virtual reality.
5. AI and machine learning
Artificial intelligence (AI) and machine learning (ML) are the important business technologies of the past decades. The applications of AI/ML to business models and processes are endless. AI/ML focuses on automating business processes and tasks, intelligent decision-making, predictive analytics, personalization, and conversational interfaces, among many other areas. There is “narrow” AI/ML and “generalized” AI/ML which refers to the “limitability” of problems. Most problems are well-bounded problems, that is, problems that can be modeled. For example, automating the processes by which someone should receive a loan, or whether someone should be admitted to a university, can be modeled quite easily, since the variables that predict acceptance/rejection are known.
AI and machine learning can literally change your business processes and perhaps your entire business model. The venture capital community believes this, countless venture capitalists know this, and entrepreneurs are gathering at the doorsteps of every industry on the planet. You need an internal team to review your processes to assess where AI/ML should play. Piloting begins today.
What’s not on the 2023 list
Good news: you can save time, effort and money by putting certain technologies on hold – at least until 2024, when you can revisit them. For example, The Metaverse, which is way behind schedule and won’t mature for years, can easily fall through the cracks of 2023. Quantum Computing, which will have very specialized applications for the next 3-5 years , can also be put on the table. 6G, which follows the slow rollout of 5G, will still be in 2024. 3D manufacturing has reached some application walls, and Total Experience (TX), which will be enabled by conversational AI and whose definition means too many different things for too many companies (not to mention huge technology integration challenges) – can also wait a while.
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