Amir Shodiev, Owner & CEO, Nazemius LTD – MARK AI, outlines how the company is developing technology to connect organisational memory, evidence and governance in this exclusive opinion piece.
Artificial intelligence has moved rapidly from experimentation into everyday business. The next challenge is enabling AI systems to understand the organisations in which they operate, including the history, assumptions and constraints behind business decisions.
Stanford’s 2026 AI Index reports that 88% of respondents to a 2025 McKinsey survey said their organisations used AI in at least one business function, while 79% reported regular use of generative AI. Scaled AI-agent deployment remained in the single digits across nearly all business functions. Global corporate AI investment reached US$581.7 billion in 2025.
The figures suggest a transition. Businesses are adopting AI rapidly, but widespread use of AI tools has yet to translate into equally widespread deployment of autonomous systems. More capable models will help, but reliable autonomy also requires persistent business context.
From AI tools to business infrastructure
Companies already use AI to analyse information, generate software, support customers, create forecasts and automate individual tasks. Scaling those capabilities across an organisation introduces a different problem.
A model may understand customer acquisition costs or recognise declining margins. Without organisational context, however, it may not know why a previous decision was made, which assumptions shaped it, what happened afterwards or whether those assumptions remain valid.
Business knowledge is distributed across people, documents, databases, financial systems, operational processes and historical decisions. Access to more information alone does not resolve that fragmentation. An AI system also needs a way to connect evidence with the organisation’s experience and objectives.
A more complete architecture would combine AI models with business context, memory, evidence, decision support, scenario analysis, governance and action. Models provide reasoning capability. Context establishes what the organisation does and how it operates. Memory preserves continuity, while data provides evidence.
Decision and scenario systems connect that evidence with objectives and possible consequences. Governance establishes boundaries and accountability. Bringing those components together could make autonomous action more reliable within real business processes.
Why I founded Nazemius
This challenge led me to establish Nazemius LTD, a UK-registered technology company developing business infrastructure centred on persistent organisational intelligence.
My background spans sales, operations, logistics, process optimisation and real estate. Across those environments, I repeatedly encountered the same structural problem: businesses generate enormous amounts of information, but information does not automatically become understanding.
AI’s ability to reason across multiple sources prompted a question: could a system continuously understand the business lifecycle surrounding an enquiry, rather than waiting for someone to ask it a question?
That question became the foundation for MARK AI. MARK stands for Manager for Acceleration and Registration of Knowledge. We are developing it as an autonomous digital operating layer designed to understand and support the evolution of a business over time.
The aim extends beyond a chatbot or an agent connected to a collection of tools. MARK is intended to maintain an understanding of what a business knows, what it has experienced, which decisions it has made, what patterns are emerging and how changing conditions could affect its future.
Building continuity into business AI
MARK is being developed around a continuously evolving understanding of the organisation. The system is designed to maintain context over time, connect new information with previous business experience, recognise emerging patterns and evaluate decisions as circumstances change.
Planned capabilities include scenario intelligence, confidence calibration, contradiction monitoring and simulation learning. Together, those capabilities are intended to help the system evaluate possible outcomes, assess the strength of its conclusions, identify conflicting evidence and learn from simulated situations.
Continuity is central to the approach. An AI system that cannot retain relevant business context risks treating each situation as an isolated request. Sustained organisational understanding could support a more useful form of autonomy, grounded in the business’s experience rather than a succession of disconnected interactions.
Why contradictions matter
Businesses constantly generate conflicting signals. Sales may report strong demand while finance sees worsening cash flow. Marketing may report improving customer acquisition while retention deteriorates. A decision made six months earlier may no longer be appropriate because market conditions have changed.
An autonomous system should do more than select the newest information. It should identify the conflict, assess what changed, examine the reliability of the evidence and establish which assumptions or decisions depend on resolving it.
Contradiction monitoring could therefore help AI move beyond answering isolated questions towards tracking how a business evolves. The objective is to preserve the relationship between evidence, decisions and outcomes as new information emerges.
Expanding from the UK into the UAE
Nazemius began with a UK foundation, but its next stage is increasingly centred on the UAE. We are progressing our transition and expansion into the country, where we see an opportunity to develop autonomous business infrastructure.
Nazemius is seeking strategic sponsorship, capital and support during its founding stage. We are also pursuing relationships with investors, family offices, technology partners and innovation organisations that can contribute market access, expertise and long-term strategic value.
Our priority is to establish the right foundation for MARK AI in the UAE before pursuing expansion across the GCC, MENA and international markets. Funding is part of that process, alongside the partnerships needed to support development and future deployment.
The next enterprise intelligence layer
Future enterprises may draw on multiple AI models for reasoning, specialised expertise, language capabilities and analysis. Alongside those models, businesses may need a persistent intelligence layer that understands the organisation, remembers its history, monitors its current state and evaluates possible futures.
Enterprise AI could consequently evolve beyond the familiar sequence of model, prompt and answer. Context, memory, evidence, decisions, scenarios and governance would become integral to the path from reasoning to action.
AI could become part of the infrastructure through which a business understands itself, detects change, evaluates decisions and adapts over time. MARK AI is being developed to explore that possibility.
The defining question may shift from how intelligent a model is to how well the wider system understands the business it has been asked to operate. Answering that question will require continuity, evidence and accountability alongside reasoning capability.
Image Credit: Nazemius
Source: Tahawul Tech