If Artificial Intelligence has been on your mind a lot lately, that’s hardly surprising. ChatGPT has demonstrated that AI is finally not perpetually ten years away. It’s hard to predict how fast it will change things, but we all know it will change things.

Some people are privileged or influential enough to wonder what to do about it. Elon Musk and the hitherto unknown, to us mere mortals, “father of AI” Geoffrey Hinton, feel that its progress should be slowed down. The genie must be put back in its bottle before the wrong people use it to have their [bad] wishes granted.

However, most of us are less influential, and we need a backup plan. We must consider what to do with AI, while those with money and power consider what to do about AI.

In the absence of a blinding stroke of innovative thinking that caused you to wake up in a cold sweat yelling, “Eureka! I know what the next ChatGPT app should do!”, I recommend some reflection, imagination, and formulation of an adoption strategy. And more so if your strategy was a ChatGPT app.

First, let’s reflect.

Looking Back to Predict the Future

The industrial revolution changed the lives and livelihoods of hundreds of thousands of people, accelerated urbanisation, and solidified global value chains. Cotton picked in the southern USA was shipped and transformed into products in Manchester, England, to be sold across Europe. If you walk into a department store today, you may encounter a Manchester department selling household linen. The shadow of the industrial revolution lives on.

Then the electronic revolution was ushered in side by side with social change caused by the second world war. This evolution changed the way businesses operate—electronically. Work started to be done better, cheaper, and faster. People moved from the factory floor to computer rooms and eventually to sitting at terminals at their desks. Education became much more important. People could no longer be trained on the job to serve as a cog in an industrial process. They needed to arrive educated; otherwise, they couldn’t learn what was required from the work.

The next significant change came with the birth of the age of the internet. Ask any parent who has known life before and after it, and they will speak of the social and educational impact, good and bad, it has had on their children. Economically, the internet has created complex local distribution chains that no single organisation owns or understands, yet they are vital to our lives and businesses. We notice when they fail (as they did during COVID).

Reimagining the Future

Knowing that each technology has brought significant change not only in business but economically and socially, we shouldn’t be hesitant to reimagine a radically new future.

Your current business or government department’s mission doesn’t start and stop at the boundaries that you teach your people to maintain. No organisation is an island. Each is part of a larger supply chain that may begin with extracting raw materials from the ground to handing the finished product to the consumer. Your organisation’s role, big or small, doesn’t dilute the fact that the entire supply chain is your business. It’s just that you might not own it all.

But imagine you do own the entire supply chain. Everything before and after you, right up to when you hand over your goods and services to the human(s) you impact—it’s all yours. You’re a vast conglomerate that mines, builds, transports, enhances, innovates, markets, sells, and delivers. Whether it’s iPhones or electricity, you do it all.

Now consider the pending disruption that AI will have. You have a broad responsibility to decide which parts of these currently outsourced supply chain businesses will be disrupted. Do you enhance them? Retire them? Swap them with a previously unknown provider who will now do what they did but with AI? And don’t forget to include your current portion of your large conglomerate business inside this conversation. Do you turn parts of it off?

Netflix is No Longer Known for Mailing DVDs

Consider Netflix, once a postal DVD service. Down the DVD supply chain are actors, scriptwriters, and producers—content creators. Up the supply chain were the postal service and the viewing devices (once VCRs). Netflix now controls all of these, an end-to-end entertainment service. They saw the writing on the wall for their own business and went looking for new opportunities along their existing supply chain. They stopped what they once did and disrupted what others were doing to create a new business.

What Will You Do?

What actions will your business take — what will it stop, start, and continue doing?

Data is the New Oil

Data embodies everything we do and hope to achieve in commerce. It provides intricate details about consumers, enabling them to make remote payments from the comfort of their homes. It tracks goods, ensuring delivery from Shanghai through all the points in between. Data has even transformed into the currency we use. Data is the lifeblood of the modern economy.

AI-driven models, such as ChatGPT large language models, consume data and generate powerful relationships that uncover previously unknown connections and properties. Organisations will no longer rely solely on rigid processes as their key differentiators; data will drive continuous improvement. Companies that previously traded off customer experience for operational excellence may now achieve greater client intimacy. For example, Nike could use AI to shift from basic sizes to personalised unique shoes, built in-store with 3D printers.

Dull, Dirty, and Dangerous

AI has already proven its usefulness in performing mundane tasks, such as answering repetitive client queries. Did you know that majority of calls to Tax Departments concern tax numbers, which are currently answered by humans.

When combined with machines (or robots, if you prefer), AI can tackle tasks that are not being done due to their dull, dirty, or dangerous nature. This includes agricultural tasks like fruit picking, waste management, chemical handling, industrial cleaning, mining, and manufacturing. Expect low-wage job opportunities to be replaced by more reliable machines that only require training once and do not complain (however justified) about working conditions—nor suffer fatal consequences when things go wrong.

What do software developers do all day?

The rapid advancement of AI will alter the way we approach technology, making its production faster and more efficient.

In an enterprise, where most software is built or consumed, research finds that software developers may get as little as two hours a day to actual coding. During this time, a programmer tasked with building business applications might spend between 50% and 85% of their time creating code that doesn’t add direct business value. Instead, it focuses on mitigating risks, addressing quality issues, and adhering to standards and policies—security, reliability, scalability, usability, interoperability, maintainability, and more. These non-business requirements follow repeating patterns but have thus far eluded automation. So far!

What We Do

At SECTION6, we already understand that our clients’ businesses are our business. We align our downstream supply chain with the software partners and vendors most critical to our clients’ missions. Upstream, we concentrate on the recurring challenges and opportunities our clients share when build software that is essential in peoples lives and so critical to organisation success. To forge a bridge between ourselves and our clients for quicker and easier engagement, we invest in a generative culture that we know help us together achieve our shared goals better and faster. Building on this foundation, we bring our in-depth knowledge of mission-critical software—including people, processes, tools, and partners—to support mutual success with our clients.

By understanding our supply and value chain, we anticipate the future trajectory of our clients and ourselves. We recognise that machine learning (ML) and AI will be essential in our clients’ operation. This has lead our own research and development efforts and includes six core capabilities that our clients need now an into their AI / ML drive futures:

Cognitive Automation

Cognitive Automation utilises AI and ML to imitate human learning processes for improved decision-making. By analysing and identifying patterns from diverse data sources, AI/ML algorithms automate complex tasks requiring cognitive abilities, enabling more efficient, accurate process automation.

On-Demand Compute

On-demand computing supplies computing resources across various infrastructures, addressing user and application needs. AI/ML workloads benefit from on-demand computing’s flexibility, optimising costs and performance. Container platforms ensure consistent environments for AI/ML applications, allowing efficient scaling and management.


Contemporary cybersecurity demands a multi-layered approach, with AI/ML bolstering threat detection, response, and prevention. Machine learning algorithms analyse vast amounts of data, identifying patterns and anomalies, while AI-powered security systems adapt to evolving threats, automating responses and reducing remediation time.

Agile Integration

Agile Integration connects data, services, and devices via micro-services, fostering faster decision-making and efficient workflows. AI/ML optimises data processing and decision-making in Agile Integration by automating integration processes, reducing manual effort, and continuously pinpointing bottlenecks and inefficiencies.

Industrial IoT

Industrial IoT extends IoT into mission-critical industrial applications, necessitating greater resilience and security. Incorporating AI/ML enables advanced analytics, predictive maintenance, and real-time decision-making. Machine learning models, trained on IIoT data, identify patterns and anomalies, facilitating proactive maintenance and AI-driven automation for increased efficiency and productivity.


DevOps emphasises collaboration, communication, and automation between software development and IT operations teams. Incorporating AI/ML into DevOps processes enhances software development, testing, and deployment by automating code review, anomaly detection, and performance optimisation. MLOps, a specialised extension of DevOps, concentrates on developing, deploying, and maintaining machine learning models at scale.

Talk to Us Today

Don’t let the opportunity to harness the power of AI and ML for your organisation slip away. Connect with us today and discover how our expertise and strategic approach can help you navigate the AI-driven future, enhance your supply chain, and achieve more for your mission.