AI adoption and decision intelligence
Evidence-led AI advisory for decision-heavy organisations.
Dr Mikhail Vasenin helps UK organisations adopt AI, improve forecasting, and design analytical workflows that support better decisions across finance, strategy, and operations.
Decision intelligence
Workflow
Forecast
Evidence
AI adoption
AI priorities, forecasting discipline, reporting workflows, and human review.
Academic base
Finance academic
Research and teaching foundation in empirical and quantitative finance.
Method depth
PhD in Quantitative Finance
Analytical training across finance, econometrics, and evidence-led methods.
Applied work
Applied AI founder
Founder experience translating analysis into practical systems and workflows.
Market focus
UK and North East base
Local ecosystem credibility with UK-wide advisory delivery.
Core lens
Decision intelligence
Forecasting, reporting, analytics, and human-in-the-loop AI use.
Services
Focused advisory for AI adoption, forecasting, and analytical workflow design.
Forecasting and Decision Intelligence
Design better evidence-led forecasting, reporting, and decision-support workflows.
Best for
- Founder-led businesses
- Operations teams
- Reporting-heavy SMEs
Typical output
- Forecasting workflow review
- KPI and signal design
- Decision-support roadmap
Discuss forecasting support
Applied AI for Analytical Workflows
Move from shallow AI experiments to practical analytical workflows with human oversight.
Best for
- Teams exploring AI
- Innovation leads
- Analytical functions
Typical output
- AI workflow review
- Automation opportunity map
- Human-in-the-loop design
Map AI opportunities
Quantitative Finance and Market Intelligence
Apply rigorous quantitative framing to finance, fintech, digital-asset, and market questions.
Best for
- Fintech teams
- Finance leaders
- Research-heavy organisations
Typical output
- Market analysis briefing
- Event-study framing
- Evidence-led interpretation
Review market questions
Strategy Workshops and Executive Advisory
Give leadership teams a structured route through AI adoption, risk, priorities, and delivery.
Best for
- Leadership teams
- Professional services firms
- Innovation programmes
Typical output
- Executive workshop
- Adoption priorities
- Risk and governance framing
Plan a workshop
How this helps
Better AI decisions start with better analytical workflows.
The work is designed for teams that need practical clarity: what to prioritise, what to automate, how to keep humans in the loop, and how to turn analysis into action.
Clearer AI priorities
Identify where AI can improve real decisions instead of adding disconnected tools.
Stronger forecasting discipline
Connect models, signals, judgement, and review cycles into a more accountable workflow.
Better reporting workflows
Move from dashboards that display activity to reporting that supports action.
Better decision support
Design analytical systems around interpretation, uncertainty, and practical next steps.
Responsible human-in-the-loop AI use
Keep oversight, validation, evidence quality, and governance in the workflow.
Business cases
Concrete situations where research-led advisory can create practical effect.
The site avoids invented case-study outcomes. Instead, it explains where Mikhail's finance, AI, and systems background is most commercially relevant.
Business case
AI adoption priority map
A leadership or innovation team has many AI ideas, but no clear way to decide which use cases are worth testing.
Mikhail's angle
Frame AI opportunities around decision value, data readiness, workflow friction, risk, and human oversight.
Intended business effect
A shorter, better-ranked list of AI priorities with clearer next actions and fewer unfocused experiments.
Business case
Forecasting and reporting redesign
A team produces reports or dashboards, but the outputs do not consistently improve planning, forecasting, or action.
Mikhail's angle
Audit the signals, assumptions, review cycle, and decision points that sit around reporting and forecasting work.
Intended business effect
A more disciplined analytical workflow that connects data, interpretation, accountability, and forward-looking decisions.
About Mikhail
A finance academic and applied AI founder working at the boundary of evidence and systems.
Mikhail combines quantitative finance, empirical research, sustainability-aware analysis, and practical systems thinking. The consulting focus is deliberately narrow: helping organisations use AI and analytics where decisions, forecasting, reporting, and governance matter.
Read more about MikhailView selected publicationsResearch-backed advisory
Mikhail's work sits across empirical finance, sustainable finance, digital assets, quantitative methods, and applied AI.
Founder perspective
Through Osiris Systems, he translates analytical methods into practical tools, reporting workflows, and decision-support concepts.
Built for business use
The consultancy focus is on clear AI priorities, responsible workflow design, and analytical systems that help teams make better decisions.
AI adoption
Decision intelligence
Quantitative finance
Sustainability analytics
Intelligent systems
Projects and proof
Curated signals of research, founder, and applied systems work.
Founder and applied AI/analytics lead
Osiris Systems
Applied analytics and intelligent systems work focused on making data, workflows, and decision support more usable.
Shows the connection between quantitative research, workflow automation, and practical analytical products.
Founder and quantitative finance lead
Osiris Finance
A finance-oriented analytical product direction for chart-first intelligence, market interpretation, and evidence-led signals.
Connects empirical finance, market analytics, and usable information design.
Academic, advisor, and knowledge-exchange contributor
Research and innovation work
Selected public-safe research, teaching, and knowledge-exchange work at the intersection of finance, AI, and analytics.
Provides the evidence base behind the advisory work without overclaiming private project outcomes.
Insights
Practical notes on AI adoption, decision intelligence, and analytical systems.
AI adoption
Why most AI discussions in business are still too shallow
Useful AI adoption starts with decisions, evidence, oversight, and workflow design, not tool enthusiasm alone.
4 min read
Decision intelligence
What makes a forecasting system actually useful
A useful forecasting system is a decision workflow for interpretation, action, revision, and accountability.
5 min read
Analytical workflows
Why dashboards often fail decision-makers
Many dashboards display information without reducing uncertainty or improving forward-looking action.
4 min read
Start with a focused conversation