Enterprise AI Services: Strategy, Agents and Production

ENTERPRISE AI SERVICES · UK & EUROPE e-KHOOL AI Services: From Strategy to Production Practical AI services for enterprise planning, deployment, development and optimisation.

Turning AI Potential Into Business Outcomes Turning AI Potential Into Business Outcomes Four questions many organisations face when moving beyond AI experiments. 01 02 03 04 Identifying the right opportunities Choosing a deployment environment Integrating AI into real workflows Managing costs and reaching production Not every idea is a good Data sensitivity, Value appears when AI Usage, model choice use case. Priorities governance and connects to the tools, and engineering effort should reflect business infrastructure shape data and approvals all matter once a pilot value and feasibility. where and how AI people already use. needs to scale. should run.

From AI Ambition to Business Impact From AI Ambition to Business Impact Successful enterprise AI requires the right priorities, the right environment, useful workflows and a clear path to implementation. Identify Enable Apply Improve Find meaningful Establish the right Connect AI with Evaluate performance, business technical and practical business manage costs and opportunities. deployment foundations. workflows. support implementation. Insights from each cycle sharpen the next set of priorities

Turn AI Opportunities Into a Practical Roadmap Turn AI Opportunities Into a Practical Roadmap AI Strategy & Consulting: identify suitable use cases, assess readiness, prioritise and define success measures. Business goals Use-case discovery Readiness assessment Plan carefully • A readiness view across data, skills, systems and governance Priorities ranked by value, feasibility and ROI considerations Defined success measures and a practical roadmap Business value What this stage delivers • A shortlist of use cases tied to business goals Prioritisation Demand forecasting Deprioritise Niche reporting Roadmap Start here Support triage Quick wins Meeting summaries

Deploy AI Around Your Enterprise Requirements Deploy AI Around Your Enterprise Requirements Private AI Deployment: choose an environment that fits your data, governance and infrastructure needs. Enterprise users 1 2 3 4 Access and governance controls Private AI environment Models and business applications Deployment environment assessment Data, infrastructure and operating constraints On-premises Private cloud Control Direct control of hardware and location Isolated capacity; responsibility is shared Upfront effort Higher capital and set-up effort Lower upfront; ongoing usage costs Scalability Grows by adding hardware Can flex more readily Model and infrastructure requirements Compute, model choice and sizing needs Access controls and governance Roles, permissions and oversight Alignment with your requirements Deployment shaped by organisational policy

Build AI Agents for Real Business Workflows Build AI Agents for Real Business Workflows AI Agent Development: agents for defined tasks, connected to your tools, with oversight built in. Business request AI agent Documented on the AIRA page Action or approval Knowledge and tools Verified outcome Custom development Guided agent builder: goal, instructions, knowledge, tools, guardrails, deploy • • Multiple AI models with model routing • Integration with your enterprise tools • Multi-agent workflows with human • Workflow design and agent • Custom agents for defined business tasks approval steps orchestration Support, HR, sales and Connectors across CRM, HR, finance, operations use cases IT and more

Make AI Usage More Efficient Make AI Usage More Efficient AI Cost Optimisation: see how models and tokens are used, then balance cost with quality and latency. HYPOTHETICAL DA S H B O A R D C O N C E P T Understand usage Model and token consumption by agent, app and team Token usage by workload type (%) Evaluate model routing Routine queries 40% Document summaries 30% Complex reasoning Code assistance 20% 10% Match each workload to a suitable model Identify caching Find repeatable requests where caching fits Monitor trade-offs Track cost alongside quality, latency and workload needs

Bridge the Gap Between AI Prototypes and Production Bridge the Gap Between AI Prototypes and Production Hire FDE (Forward Deployed Engineer): engineering support that works close to your team. Collaboration with business 5 and technical teams 4 Requirements translated Testing into implementation tasks 3 Integration Support for system 2 integration and testing Helping AI solutions move towards production 1 Prototype Embedded engineering Production readiness

What Makes Enterprise AI Work in Practice? What Makes Enterprise AI Work in Practice? Business value comes from connecting strategy, technology, workflows and ongoing optimisation. Business alignment Solve a clearly defined problem. Deployment foundation Use an environment appropriate to the organisation. A Reliable AIPowered Business Workflow Workflow integration Connect AI to the relevant systems and tasks. Continuous improvement Evaluate quality, cost, reliability and outcomes. Implementation support: engineering collaboration that enables every part of the ecosystem

Practical Applications. Measurable Outcomes. Practical Applications. Measurable Outcomes. A case-study framework for verified results. Figures withheld until source, measurement period, wording and customer permission are verified. Private AI Deployment AI Agent Development AI Cost Optimisation Customer: to be confirmed Customer: to be confirmed Customer: to be confirmed BUSINESS CHALLENGE BUSINESS CHALLENGE BUSINESS CHALLENGE Pending verification Pending verification Pending verification SOLUTION SOLUTION SOLUTION Pending verification Pending verification Pending verification MEASURED OUTCOME MEASURED OUTCOME MEASURED OUTCOME Pending verification Pending verification Pending verification MEASUREMENT PERIOD MEASUREMENT PERIOD MEASUREMENT PERIOD Pending verification Pending verification Pending verification

From Business Need to Practical Implementation From Business Need to Practical Implementation A proposed five-stage approach to AI delivery. 1 2 Understand the Assess requirements business objective and constraints 3 Define scope and success measures 4 Implement and integrate 5 Evaluate and improve Possible project measures Task quality and completion Workflow time Integration reliability AI usage and cost User adoption and business value

Where Could AI Create Value for Your Business? Explore the e-KHOOL AI capability that aligns with your business priorities. Priorities Foundations Workflows Discuss Your AI Requirements Improvement