Ciklum Client Conference 2025 marked its 10th anniversary in Prague’s historic Municipal House.
Rajaram Radhakrishnan, CEO of Ciklum, opened the conference by polling the leaders of enterprises in the room about their experimentation with AI and the expected returns in 2026.
75% of leaders in the room agreed that less than 50% of pilots have made it into production. But 100% expected to see positive ROI from AI investments in 2026 and beyond.
An optimistic tone was struck, which kicked off the 2 days of keynotes and practical workshops, all with the common goal of enabling enterprises to unlock the potential of AI across the entire business value chain.
Customer Obsession: The Hidden Differentiator in AI Success
Danny Jennings explored why most AI initiatives stall and what separates the 10% who succeed. The ones who succeed obsess over customers, not technology. They move in short test-and-learn cycles, pivot quickly when assumptions fail, and treat AI as an enabler, not a replacement for core product principles.
Key Takeaways:
- Customer-first discovery: 85% of client ideas are disproven or require a pivot when tested, making continuous customer validation non-negotiable.
- Rapid experimentation: Prototypes can be built in days, and six weeks is enough to move from wrong assumptions to working code.
- External incubators: Turn ideas into validated code in six weeks through ideation, triage, design sprints, and demos. Clear value gates cut waste and focus on measurable impact.
Rewiring the SDLC with AI
Overview
The evolution from Software 1.0 (hand-coded programs) to 2.0 (neural networks) to 3.0 (LLMs and agents) sets the stage for the rise of the autonomous coder. These are AI agents that turn natural language into applications, generate synthetic data, and accelerate the path from idea to prototype.
Key Takeaways:
- A new SDLC: AI agents unify backlog, code, and docs on one layer, replacing translation with orchestration.
- The developer edge: AI won’t replace jobs, but those who use it will outpace those who don’t.
- Autonomous coder: Agents evolve from copilots to independent contributors, generating code and prototypes at scale.
- Software 3.0 platforms: Balance code, neural nets, and LLMs within one ecosystem.
Turning the Back Office into a Growth Engine
Finance, HR, and operations are the quiet engines of enterprise performance. Yet they are often treated as side projects. With AI, the back office becomes a foundation for measurable impact. Done right, automation stops being incremental efficiency and eventually becomes a compounding source of organizational strength.
In a session led by Sarah Topping, Enver Cetin, and Mubine Din, the team showed that the biggest gains don’t come from flashy front-end tools. They come from reducing cognitive load, removing handoffs, and freeing employees for higher-value work.
Key Takeaways:
- Untapped potential: Automating rule-based processes in finance, HR, and ops unlocks hidden efficiency.
- Experience lift: Fewer handoffs and lower cognitive load improve confidence and decision quality.
- Scaling barrier: Poor data, undocumented processes, and resistance to new workflows stall progress.
- Change discipline: Map stakeholders, set clear success metrics, and govern data well.
- ROI balance: Hard savings matter, but so do gains in satisfaction and decision-making.
- Future horizon: AI agents will orchestrate entire multi-step workflows end-to-end.
- Transformation mindset: Treat back-office AI as core enterprise change, not a side project.
Strategic Compounding
The future belongs to organizations that build systems where AI continuously learns and coordinates across the enterprise reliably, argued Katie Mayer and Yannique Hecht. This involves a shift from “tools for tasks” to “agents for outcomes” and from “automation in silos” to “intelligence across systems,” which is the core principle of the “agentic edge” and distinguishes AI leaders from followers.
As Boost.ai highlighted in their work with Conversational AI, the shift from isolated tools to intent models, generative AI, and agentic systems is what allows enterprises to move from one-off pilots to systems that grow stronger with every cycle.
Key Takeaways:
- From tools to agents: The edge comes from moving from task automation to agents that drive enterprise results.
- Proprietary intelligence flywheeling: Data loops continuously improve models and services, creating moats rivals cannot copy.
- Ecosystem positioning: Acting as a central node in partner networks amplifies reach and resilience through network effects.
- Strategic pacing: Winners know when to accelerate and when to pause, avoiding hype-driven gold rushes.
- Compounding advantage: Durable advantage comes from reinforcing mechanisms that strengthen with each cycle, not from one-off wins.
Looking Ahead: The AI Horizon
Large language models sit at the center of today’s AI adoption, but they are not the endpoint. As James Lennon noted, they are powerful pattern learners rather than complete systems, and the edge comes when you pair them with domain context, retrieval, and well-governed data. They’re fast and often astonishing, yet they still simulate reasoning without true memory or understanding, like a student who aces practice tests but forgets the basics under pressure. Treat them as powerful partners, not a silver bullet.
The economics of AI will also shape the next phase. As demand rises, costs will increase, GPU capacity will tighten, and providers will impose usage restrictions. Enterprises that build cost-aware AI strategies, optimize model selection, and invest in strong data management will be better positioned to sustain value.
The next horizon is the evolution of generative UI, from interfaces that feel less like static dashboards and more like a Spotify playlist that adapts as you listen. Apps will shift from rigid screens to personalized environments shaped by each employee or customer in the moment. Organizations ready to rethink workflows and design for fluid, adaptive engagement will gain a definitive edge.
Winning in the Intelligence Age
As the 10th anniversary of CCC came to a close, one theme resonated across every session. AI is no longer about pilots, promises, or predictions. The focus is turning AI from experimentation into enterprise advantage.
The market winners will treat AI as an operating model that learns continuously, coordinates decisions across the business, and embeds intelligence into every interaction.
With 42% of enterprises already deploying AI and more than 90% planning to increase investment by 2028, the race has already begun. Enterprises that place customer-centric innovation, rapid experimentation, and responsible adoption at the core of their strategy will carry the advantage into the next decade.
CCC’s 10-year journey shows how far the conversation has come. The next decade will be defined by how boldly enterprises turn that conversation into compounding impact.
If you’re ready to explore how Ciklum can help you build that advantage, now is the time to start the conversation.
