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Stop adding AI to your SDLC. Redesign it.
As AI adoption grows, CTOs face new challenges to manage AI responsibly alongside their teams. Those who move quickly stand to benefit most from the opportunities AI brings. In our recent CTO Craft Online Byte, we walked through an example of what AI orchestration looks like in 2026, which will become the norm by 2028.
To introduce the Byte, I shared findings from our Engineering 2028 survey. Almost all survey respondents were already using AI. Half reported the greatest benefits in AI being increased speed and innovation.
I shared how productivity expectations are rising more sharply among teams that have more AI experience. This lies in contrast to the usual technology “hype cycle” trends, where increased utilisation leads to disillusionment.
Teams tend to start using AI tools following a co-pilot model. They use them to accelerate the way they have always worked, but as experience increases, they find new ways of working to fully utilise the AI tools.
There has always been latent demand for more software in product software companies. AI removes the barrier to product creation by creating code at lightning speed, leading to increased demand.
This all paints a rosy picture… so what’s going to slow us down?
- Capability gap: Engineering is faster than ever, making leadership harder. AI adoption is a change management process.
- Operating model gap: In such a fast-changing environment, standards and process can’t keep up. There are fragmented tools and shadow AI usage.
- Governance gap: Compliance and security concerns, IP leakage and other risks are emerging. Accountability and ownership are unclear.
We can’t simply lock down AI – so how do we, as CTOs and technology leaders, control what’s going on?
“The problem with autonomy without alignment is that it doesn’t scale.”
The path to engineering orchestration
As competition, stakeholder expectations, and advances in AI technology increase, we need to facilitate the safe adoption of AI technologies. We need to create an environment where we won’t get in the way of progress and increased productivity. But we need to take safety, compliance and consistency seriously.
Technology leaders need to change the way we work: we need to move to a model of engineering orchestration. This means using a platform that allows us to manage the use of agentic AI workflows while retaining oversight and control over what is produced.
The human controls agentic workflows across every aspect of the software development lifecycle (SDLC). All standards, such as technical, regulatory and design systems, are included in the orchestration platform. This is the shift from doing work to designing systems that do work.
Changing skills priorities
As engineering teams become orchestrators, team sizes will reduce and individuals will become more versatile. New roles will appear such as Product Engineer – engineers who understand customer/end-user needs, as well as being able to orchestrate the solutions. Other Engineers need to develop system thinking. They need to understand the bigger picture around architecture, non-functional requirements and design patterns.
As development cycles shrink, miscommunication will have an even greater negative impact, so leaders need to consider cultural differences, as well as time zone and proximity even more carefully.
“Being fluent in AI is becoming as important as being able to type on a keyboard.”
Skills needed are evolving. Prompt engineering is a basic need for people involved in software engineering. A key differentiator will be commercial understanding: really understanding the value we bring to our companies and customers. The best engineers will be creative and curious – exploring what’s possible and approaching problems from a different angle.
The “human moat” surrounding orchestration and governance is a set of unique, irreplaceable human skills: strategy, leadership, empathy, ethics and creativity.
Our role as leaders is to hire for, grow and retain these skills, understanding that our own role is being redesigned. We need to:
- Move on from managing tasks to designing systems
- Raise AI fluency across our organisations
- Strengthen governance without slowing innovation
- Build product-focused, commercially aware teams
What AI orchestration looks like in practice
Our CTO, Aleksandar Karavasilev, demonstrated to the CTO Craft community Damilah’s multi-agent platform (DMAP) to bring AI orchestration to life. Here are the principles behind our AI orchestration platform:
- Start with context, not prompts
DMAP begins with an explicit project context: the technology stack, governance constraints, standards, and which models handle which tasks. Every agent working on your project operates from the same rules.
- Run work through a structured workflow
DMAP supports agentic workflows within all stages of the SDLC, such as requirements, design, development, testing and validation.
- Humans in the loop by design
Work can be paused, reviewed, approved or rerouted at any stage. You define where human sign-off is required, what a pass looks like, and who is accountable.
- Every decision is auditable
DMAP keeps a full record of what ran, what was produced, what a human approved, and why anything was rerouted.
- Cost management
With model costs changing often, it is critical to monitor token usage and costs. DMAP includes this on a dashboard for full visibility and control.
Take the next step towards AI orchestration
Those who are continuing to add AI on top of their existing SDLC, relying on experienced people to absorb the extra review and governance load, will soon struggle. Those who redesign how work flows through the system entirely will remain competitive.
At Damilah, we are helping companies understand the barriers to AI orchestration. If you are looking into making the change, get in touch today to set up a conversation with me and the team.
Iain Bishop
CEO, Damilah