Every year, we gaze into crystal balls to divine the future of our industry – or at least where it’s headed for the year ahead. The result is often a triumph of incrementalism: we predict that we will get more of what we already have. The truth is, technology isn’t as revolutionary as we often think – and commenting on incremental changes alone may not help us understand what lies ahead.
Along with a few near-term predictions in technology, I’d also like to make some predictions about related changes to organisations, processes, and the cultures around them. Here’s my main prediction: By 2030 what we’ve come to know as “IT” today will be virtually unrecognisable.
No-code software will drive truly distributed technology
By 2030, the “de-codification of coding” – meaning the use of no-code or low-code platforms – will become real. Assembling code blocks into new applications will be possible without having to manipulate the underlying code itself. And software that “learns to learn” will deliver on the dream of self-writing software – one that evolves through learning.
Distributed technology will accelerate democratisation of innovation
The centre of gravity and decision-making is shifting away from top-down IT bureaucracy to bottoms-up adoption. Monolithic, centralised applications will give way to developing distributed and agile solutions. We’ll see technology-driven innovation from anyone and anywhere - not just dedicated technology organisations.
The ecosystems of tomorrow will be a hybrid of people and machines
Technology tomorrow will be an ecosystem of people and machines. These horizontal ecosystems of technology and people will serve both existing sectors and emerging business models – spinning up and down in real time to drive competitive advantage.
These three factors will be part of a future where technology becomes the primary enabler for individuals to achieve business goals, and will put pressure on the traditional enterprise structure. The “gig economy” is only the first expression of this nascent way to help organise businesses, and even entire economies and nation states.
If all that is the shape of the longer-term future, then what are the trends that we will see amplified in the next year? I outlined some of the major areas in a recent keynote.
Data and analytics will start to revolutionise Agile as we know it. As continuous delivery models expand and accelerate, Agile will have an increasing appetite for data-driven insights. Data insights become integrated into an increasingly granular and fast-paced process for creating new value. This will all be driven by sophisticated insight-generating engines tied to real-time business and financial metrics.
Bottom Line: The health and vitality of your software experiences and investments will be measured and even predicted in ways not possible before.
Automation happens today through things like continuous test, release, and business process automation. But to truly capitalise on automation, you need to standardise and integrate workflows smoothly across the DevOps process and tool chain. Analytics will help find bottlenecks or weak spots in your automated software flows.
Bottom Line: The future of automation is intelligent – it learns, adapts, and self-optimises the entire system. Increasingly, we’ll see software that is running development alongside and, in some instances, instead of humans.
This is the year that we will start getting better at what we mean by AI. It’s not about sentient robots – yet, anyway – but in essence is a set of algorithms expressed as code operating on data. Advanced analytics engines are the “tip of the spear” of AI across the software development lifecycle.
AI and machine learning are driving a fundamentally different approach to software development. Machine intelligence will finally deliver on the promise of big data, and the power of learning-based systems will help build and deliver better software faster.
Bottom Line: The core activities of managing, governing, and securing your technology won’t go away, but they will become more efficient, automated, and intelligent. In doing so, this will help you focus more of your energy one what matters: building new value to drive your business forward.
As software becomes the primary way that customers interact with brands, security is becoming synonymous with “trust”. This means you are now securing the entire value chain of your company – including your brand – not just data itself.
Our ability to reduce threat vectors by both enhancing the intelligence of identity and driving more sophisticated analytics will improve, but so will hackers’ ability as machine learning and AI become part of the security threat landscape.
Bottom Line: The same things that are at risk in the enterprise today will still be at risk tomorrow – data, and business continuity. AI will pose new threats that the enterprise will have to cope with, and fighting AI with AI will become a necessity.
While short-term technology predictions can help drive resource planning, longer term forecasts can shape the evolution of your capabilities in using technology as a strategic asset. If you don’t have people with the right Agile expertise or automation know-how, it’s time to invest in those resources. If you aren’t exploring how to use predictive analytics and machine learning, then now is the time to begin. And it’s not too soon to start taking a hard look at your IT organisation with an eye to retooling it for an age when technology is both highly distributed and decentralised.
Otto Berkes is global chief technology officer at CA Technologies.