Winning in times of disruption chevron_left Technological disruption
AI has moved faster than most organizations expected. Pilots are running. Tools are licensed. And yet, very few companies are turning AI into real, scalable business impact. The gap is not technology maturity. It is organizational readiness.
The next phase of AI will not be defined by better models. it will be defined by organizations that redesign work for AI and master human–AI collaboration
Ready for the next phase of AI?
How Smart Leaders Use AI to Strengthen BPO Performance
How strategic AI, Human-in-the-Loop models, and BPO expertise drive measurable business performance.
From experimentation
to execution
Most AI initiatives do not fail because the technology is weak.
They stall because:
- Processes are fragmented
- Data is inconsistent
- Ownership is unclear
- Governance is added too late
As organizations move beyond isolated AI use cases toward automation across entire workflows, these weaknesses become impossible to ignore.
This is where the next phase begins.
The numbers leaders cannot ignore
$2.6–$4.4 tn
Potential annual value GenAI could add to the global economy
80%
of organizations are experimenting with AI, but most initiatives never scale to measurable business impact
90%
of leaders report shortages in AI-critical skills
~80%
of enterprise data is unstructured. Meaning most business knowledge is still underused
The Winning Strategy:
from Generative AI to Agentic Automation
Instead of supporting single tasks, AI systems are starting to plan, decide, and execute multi-step workflows across business operations. This changes the role of AI from assistant to execution engine, while humans remain responsible for oversight, governance, and decision authority.
But agentic automation only works when foundations are strong:
Trusted, accessible data
Clearly defined end-to-end processes
Explicit business rules and ownership
Governance designed into workflows, not added afterward
Whitepaper
From GenAI to Agentic Automation
The next wave of value creation
Despite a 200% rise in GenAI spending, most pilots stall. We explore why scaling remains elusive.
How can companies evolve towards scalable industrialized AI to fully realize automation and productivity potentials?
- Design processes for AI – not AI for inefficient processes
- Human in the Loop in industrialized processes
- Master hallucination in quality assurance
- Structured data – the fuel for AI agents
- The secret sauce of intelligent automation: AI Enabled Performers
Agentic Automation is not an add-on feature you simply switch on. It rests on robust data foundations, clearly orchestrated processes, and governance embedded in the way work truly operates.Christian Schierjott Global Head of Business Solutions, SPS
From Promise to Practice: Preparing for Agentic Automation in Business
Episode 11
Scaling AI isn’t as simple as flipping a switch. In Episode 11, we uncover what it really takes to prepare for agentic automation in business.
From pilots to performance
The organizations that succeed with AI do a few things differently:
What sets the winners apart is how intentionally they apply AI:
- They focus on impact, not volume. They prioritize where AI changes business outcomes, not just efficiency metrics.
- They design for human and AI collaboration. AI handles repetition and scale. People provide judgment, accountability, and exception handling..
- They build readiness before scaling. Strong data, governance, and workflow design turn pilots into operational capability.
- They combine AI with deep domain expertise. Competitive advantage does not come from generic automation. It comes from redesigning processes around industry knowledge.
- They work with strategic partners to leverage their experience to achieve more benefits, leverage know-how, gain speed and limit risk
This is how AI moves from hype to sustainable business value.
SPS does not help clients “use AI.”
We build and run 100% solutions
10% Algorithms,
20% Technology
70% Process and People.