AI Consulting
AI Consulting that ends in production, not a slide deck.
Strategy, data, governance and operating model — designed by senior practitioners and delivered by the same accountable team.
Where AI value comes from
Three value plays.
Use AI in everyday work
Productivity, copilots and assistants in the tools people already use.
- →Less time on drafting, searching and summarising routine work
- →Better use of AI licences already paid for
- →Consistent answers from service desks and assistants
Reshape critical workflows
Credit, fraud, maintenance, supply chain and documents, redesigned around AI.
- →Faster, explainable credit and fraud decisions
- →Fewer manual touches and errors in document-heavy processes
- →Earlier warning on asset, quality and supply risks
Create new revenue
AI-enabled products and services for your customers.
- →Personalised offers and next-best actions
- →New data-driven services for existing customers
- →Faster launch of AI-enabled products
Consulting services
Five ways we help leaders get AI right.
AI strategy, roadmap & value cases
Many AI ideas, no agreed priorities or business case.
- Opportunity sizing
- Use-case portfolio and prioritisation
- Business cases with value formulas
- Value tracking
Deliverables Prioritised roadmap, value cases with one-line value formulas, value-tracking plan.
Data readiness & architecture
Data and platforms not ready for AI in production.
- Data and platform assessment
- Target architecture
- Build-vs-buy and vendor/platform selection
- Integration plan
Deliverables Readiness assessment, target architecture, platform shortlist, integration plan.
Governance, risk & regulation
AI risk and new regulation without a clear owner or framework.
- Responsible-AI framework
- EU AI Act readiness and ISO/IEC 42001 alignment (designed to support, not certification)
- Model-risk management
- AI security
Deliverables Governance framework, risk register, readiness gap analysis, control plan.
The GoGenesis AI Trust Framework →Operating model, CoE & change
AI pilots scattered across teams with no owner or adoption plan.
- AI Centre of Excellence design and launch
- Roles and decision rights
- Adoption and change
- Training and capability building
Deliverables CoE charter, operating model, decision-rights map, adoption and training plan.
CoE best practice →Training →AI due diligence for M&A and investors
Deal teams need to know whether a target's AI claims are real.
- Whether the AI claims are real
- Data assets and risks
- Team and IP
- Post-deal value-creation opportunities and red flags
Deliverables Diligence report with a red/amber/green summary and a 100-day value-creation plan.
Engagements
Fixed-scope ways to start.
Typical durations — final scope agreed with you.
4–6 weeks · fixed fee
Discovery & Value Case
Use case, data, security and baseline confirmed; success criteria and value gate agreed. Matches How we deliver step 1.
Discuss scope3–4 weeks
Data & Platform Readiness Assessment
Readiness scorecard, target architecture and integration plan.
Discuss scope4–6 weeks
AI Governance & Regulatory Readiness
Framework, gap analysis and control plan, designed to support your regulatory obligations.
Discuss scope2–4 weeks · fast-track available for deal timetables
AI Due Diligence
Red/amber/green diligence report and a 100-day value-creation plan.
Discuss scopeOur approach
Diagnose → Design → Prove → Scale → Run.
- 01
Diagnose
◆ Problem and baseline agreed
- 02
Design
◆ Value formula and success criteria agreed
- 03
Prove
◆ Go / no-go: pilot beats baseline
- 04
Scale
◆ Integration, governance and adoption in place
- 05
Run
◆ Quarterly value review
Senior practitioners on every engagement
The people who scope the work stay on it.
Recommendations we can build, run and support
Strategy, delivery and managed operations from one accountable team.
Managed Services →Proven partner platforms, not a blank page
We start from platforms already running in production.
Industries
Consulting grounded in ten industries.
Banking · Fraud
Real-time card fraud scoring
HDFC Bank
Rule-based fraud detection was missing attacks while flooding analysts with false alerts.
- +39%
- ATM fraud caught
- −66%
- false alerts on card-not-present
- ~15 ms
- per transaction, ~6M txns/day
Banking · Credit
Credit decisioning at production speed
Leading consumer & SME bank, 100+ credit models
New credit models took 9–12 months to move from data to production.
- 2–3 wks
- data-to-production (from 9–12 months)
- +12%
- loan approvals
- −10%
- credit losses
Energy & Manufacturing · Operations · Data platform
Refinery digital twin
MRPL, 15 MMTPA refinery
Operational data was spread across dozens of IT and OT systems.
- 15
- MMTPA refinery modelled
- 5
- people on the build team
- 6 mo
- to an enterprise digital twin
NoteResults from deployments by GoGenesis and its platform partners. Outcomes vary by portfolio and operating environment; your results are set in the pilot.
Our consultants
Senior practitioners on every engagement.
George Molakal
President
Strategy, value creation and M&A
Serial entrepreneur and CEO with 30 years of building, scaling, acquiring and exiting companies across five continents, including two that reached multibillion-dollar scale. He helps boards, CEOs and investors turn AI into measurable enterprise value — including AI that saved one client more than US$140M in a single year. Harvard Business School and Oxford alumnus, author of four books and international keynote speaker.
George Samuel
Chief Strategy and Operating Officer
AI transformation and delivery
Co-founder of GoGenesis AI with 20+ years leading data and AI transformation at Deloitte, EY and IBM. He turns stalled AI pilots into production systems that move the metrics that matter — working capital, asset uptime, fraud loss, cycle time and margin.
Bruce Jones
Venture Partner, Industry
Industry transformation
Technology and business-development leader who has driven large-scale transformations, innovation programmes and turnarounds across EMEA, the Americas and APAC. Deep experience in financial services, industrial and energy, spanning data, analytics, automation, robotics, AI and security.
Sujith Varghese
Venture Partner, Fintech
Credit risk analytics
Credit strategy and risk analytics leader with 20+ years in financial services, formerly at Citi and Argus. Specialist in retail credit policy, risk analytics and regulatory reporting across the full credit life cycle, leading global teams that deliver executive insight to CROs.
Vikas Tyagi
Venture Partner, Banking
Credit and risk
Chief Credit Officer and SVP at Sallie Mae, leading end-to-end credit strategy and analytics across the lending lifecycle. Over 16 years at Citi he rose to Managing Director and Chief Risk Officer for Branded Cards and Lending, founded Citi's US Personal Banking analytics centre in India and led its loss-mitigation task force through the 2008 financial crisis. A former Chief Engineer on oil tankers, he brings an engineer's discipline to risk; MBA, University of Maryland.
Gregg Macaluso
Venture Partner, Supply Chain
Supply chain, operations and M&A due diligence
Corporate and supply chain strategist who has led vertical-segment practices at large global management consultancies, as well as working as an industry practitioner and educator. Expertise spans strategic procurement and supplier-network design, M&A strategy and due diligence, global operations and logistics-network optimisation, and supply chain systems integration with SAP IBP, Llamasoft and JDA.
Jayakumar Mohanachandran
Strategic Advisor · Head of Cybersecurity
Cybersecurity and IT governance
Dubai-based CEO and Co-Founder of F9 Infotech, a global systems integrator for cybersecurity and multi-cloud, with 25+ years of IT leadership across EMEA and APAC. A three-time CIO of the Year in the GCC, he is President of the CIO Association UAE, founder of the Kerala Cyber Suraksha Summit, and a fractional CIO and board advisor.FAQ
Questions leaders ask about AI consulting
Where do we start?+
Usually with an AI Strategy Sprint or a Discovery & Value Case: we agree the business problem, the baseline and a one-line value formula before anything is built.
How fast can we see value?+
Discovery takes 4–6 weeks and a controlled pilot 8–12 weeks. Each stage has a value gate, so you decide on evidence before committing more.
Do you replace our systems?+
No. We work alongside your existing systems through APIs or secure file transfer, on your cloud or on-premises.
How do you handle governance and regulation?+
Governance is designed in from day one using the GoGenesis AI Trust Framework: human approval for regulated decisions, explainability, monitoring and security. Our work is designed to support frameworks such as the EU AI Act and ISO/IEC 42001; it is not legal advice or certification.
Can you also build and run what you recommend?+
Yes. The same accountable team can deliver the pilot, take it to production and run it through Managed Services.
Do you do due diligence for deals?+
Yes. AI due diligence for acquirers, private equity and investors takes 2–4 weeks, with a fast-track for deal timetables, and ends in a red/amber/green report and a 100-day value-creation plan.
Contact
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