In the rush to automate back-office workflows, many UK businesses overlook a crucial fact: business process automation (BPA) is personal data processing. Under the UK GDPR, introducing BPA tools without privacy-by-design can expose your company to compliance, reputational, and operational risks.Automation increases the volume, velocity, and visibility of data flows, making it essential to understand where personal data travels, who controls it, and how it’s secured. For SMEs and large enterprises alike, GDPR compliance must be built into your automation program — not bolted on after deployment.What “High-Risk” Processing Means for Automation ProjectsAutomating decisions, workflows, or data enrichment stepscan trigger “high-risk” processing when individuals’ rights and freedoms couldbe affected — for example, automated HR screening, invoice processing withpersonal identifiers, or cross-border data enrichment.When processing is high risk, a Data ProtectionImpact Assessment (DPIA) becomes mandatory before go-live. This ensuresrisks are understood and mitigated upfront rather than discovered afterdeployment.Accountability and Automation: Why SMEs Must RethinkTheir GDPR ControlsUnder UK GDPR, SMEs are held to the same accountabilityprinciple as larger organizations: you must demonstrate compliance,not just claim it.Automation expands data flows across multiple systems, meaning: More processing activities under one controller’s responsibility. Increased reliance on processors (vendors, cloud services). Continuous changes to data purpose, storage, and access.Before rolling out your BPA tools, ensure that every automated process is mapped, risk-assessed, and governed.Quick GDPR Glossary for Automation Projects DPIA – Data Protection Impact Assessment; mandatory for high-risk processing. DPA – Data Processing Agreement; defines controller–processor obligations. IDTA/Addendum – UK transfer tools replacing EU SCCs. TRA – Transfer Risk Assessment; required for restricted data transfers.BPA Tools Implementation Discovery: Map Data, Systems, and Risks (Pre-DPIA)Before drafting a DPIA, perform a data-mapping exercise across the automated workflow: Identify data sources, categories, and flows (especially special category data). Record controllers and processors for each step. Confirm the lawful basis for every processing operation (e.g., contract, legitimate interest). Use a DPIA screening checklist to decide if a full DPIA is required.Early discovery reduces rework later in the rollout and aligns privacy engineering with system design.‍BPA Tools Implementation DPIA: A Step-by-Step Checklist1. Scope & Necessity: Define the purpose, benefits, and less intrusive alternatives.2. Describe Processing: Document data subjects, categories, recipients, and transfers.3. Assess Risks: Evaluate likelihood and severity to individuals’ rights and freedoms.4. Mitigations: Plan for minimisation, pseudonymisation, encryption, access control, and retention.5. Consultation: Involve your DPO, stakeholders, and consult the ICO if residual high risk remains.6. Decision Log & Review Cadence: Record DPIA outcomes, assign owners, and link to release management cycles.BPA Tools Implementation and Lawful Basis: Get It Right, Then AutomateEvery automated task must have a documented lawful basis linked to its purpose.Typical mappings include: Contract: Processing required to fulfil a client or employee contract. Legitimate Interests: Efficiency or analytics automation that doesn’t override data subject rights.When in doubt, perform a Legitimate Interests Assessment (LIA) — particularly for automation involving monitoring, HR, or analytics data.Pro Tip: Maintain a “purpose–basis–data” linkage table in your automation catalogue for quick audits.BPA Tools Implementation Retention: Policy, Schedules, and ConfigurationsAutomation should not mean endless retention. Apply storage limitation principles to each dataset: Define retention events (task completed, invoice paid, case archived). Configure secure deletion or “put-beyond-use” patterns in your BPA tools. Maintain an evidence pack: retention schedule + deletion logs for audits.Avoid “keep just in case” – regulators view that as a breach of minimisation and accountability.BPA Tools Implementation with Vendors: DPAs, Sub-Processors, and AuditsWhen outsourcing parts of automation to SaaS or cloud providers, ensure your Data Processing Agreement (DPA) includes all Article28 UK GDPR requirements: Documented instructions, confidentiality, TOMs, sub-processor approval, assistance, deletion, and audit rights. Operationalise the DPA: run restore tests, verify security evidence, and maintain incident logs.BPA Tools Implementation & International Transfers: IDTA/Addendum + TRAIf your automation vendor stores or accesses data outside the UK: Confirm if the transfer is restricted. Choose between the UK International Data Transfer Agreement (IDTA) or the Addendum to EU SCCs. Conduct a Transfer Risk Assessment (TRA) to evaluate legal and technical safeguards.Document the chosen transfer tool in your DPA and your automation catalogue.BPA Tools Implementation Security: Technical & Organisational Measures (TOMs)Effective BPA security reduces both bot fragility and privacy risk.Essential controls include: Least privilege access & segregation of environments. Encryption in transit and at rest. Key management, logging, and alerting. Regular resilience and restore testing.For SMEs, demonstrating “appropriate” security can align with Cyber Essentials or ISO 27001 frameworks.BPA Tools Implementation for Data Subject Rights: DSAR-Ready by DesignAutomation must support data subject rights from day one.Embed mechanisms to: Locate, export, or delete records quickly. Prevent orphaned data in automation queues. Include processor assistance SLAs inside your DPA to guarantee compliance.Building DSAR-readiness now avoids retrofitting pain later.BPA Tools Implementation Governance: Records, Audits, and MonitoringMaintain a live automation catalogue containing: Purpose, lawful basis, DPIA link, DPA link, retention, TOMs, transfer tools, owner, and next review date. Integrate with release management — run pre-production DPIA checks and monitor vendor/sub-processor changes.Ongoing governance ensures automation remains compliant as it evolves.BPA Tools Implementation Rollout Plan: Timeline, RACI, and KPIsA successful BPA rollout under UK GDPR follows a six-week phased plan, integrating compliance deliverables at each milestone rather than treating them as afterthoughts.Phase 1 – Discovery & Mapping (Week 1)Start by cataloguing all automated processes, data sources, and system integrations. Identify controllers and processors, define purposes, and complete a DPIA screening.Accountable: Project lead (privacy-by-design owner)Consulted: DPO, system architectsKPIs: 100% of automated processes mapped; DPIA screening decisions logged. Phase 2 – DPIA, DPA & TRA (Weeks 2–3)Run the full DPIA for high-risk processing, execute Data Processing Agreements with vendors, and complete Transfer Risk Assessments for any international data movement.Responsible: Privacy teamConsulted: Vendors, legal counsel, IT securityKPIs: All high-risk processes documented; signed DPA and TRA on file before build. Phase 3 – Build & Configuration (Weeks 4–5)Configure automation workflows with privacy controls built in — least privilege, encryption, retention triggers, and logging. Validate lawful basis per task and integrate deletion schedules.Responsible: Automation engineersAccountable: Product ownerKPIs: No open security gaps; retention and deletion events configured in all workflows. Phase 4 – UAT & Go-Live (Week 6)Conduct user acceptance testing with privacy test cases —DSAR readiness, audit logging, and rollback validation. Approve productiondeployment only after residual risk review by the DPO.Accountable: DPO and release managerConsulted: End users, QA, IT operationsKPIs: 100% UAT sign-off; zero unresolved DPIA actions; no data quality regressions. Phase 5 – Post-Launch Review (Ongoing)Monitor automation stability, incident response, and DSAR fulfilment performance. Feed lessons into your change management and periodicDPIA review cycle.Accountable: Operations & governance leadKPIs: DSAR response time under 30 days Deletion requests completed within SLA Audit findings closed within 14 daysDiscover how our AI-Powered Business Assistant helps you monitor privacy KPIs and automate compliance tasks end-to-end.

ETL Pipeline Development in the UK: Building Data Foundations for an AI-Ready Future In 2025, ETL pipeline development in the UK has evolved from a back-office engineering task into a strategic business enabler. As organisations race to modernise their data estates and unlock AI-driven insights, the ability to move, transform, and govern data reliably has become a competitive advantage. Why ETL Still Matters — and Why It’s Changing ETL (Extract, Transform, Load) remains at the heart of every data ecosystem. But the tools and expectations around it have shifted dramatically. Across the UK, companies are: Migrating from legacy SSIS or Informatica setups to cloud-native ETL or ELT platforms such as Azure Data Factory, Databricks, Matillion, or Snowflake. Moving from nightly batch jobs to real-time data integration, using CDC (Change Data Capture) and streaming. Embedding data quality, lineage, and compliance directly into their pipelines to meet UK GDPR and FCA operational resilience requirements. In other words, the question is no longer “Do we have an ETL tool?” — it’s “Do we have a trusted, scalable ETL pipeline that supports analytics and AI safely?” UK Market Context The UK data-integration market is one of the most mature in Europe. Driven by cloud adoption, financial-sector regulation, and the rise of AI workloads, spending on data and analytics infrastructure continues to grow by more than 12% per year. Industries leading ETL modernisation include: Financial services and insurance, where auditability and data lineage are mandatory. Healthcare and life sciences, focused on secure patient-data integration. Retail and eCommerce, connecting customer and inventory data for real-time decision-making. Public sector, using G-Cloud frameworks to procure modern data-pipeline services. Modern ETL Pipeline Development: What It Looks Like A modern ETL pipeline development project in the UK typically involves: Discovery and audit – mapping data sources, data quality, and compliance gaps. Architecture design – selecting the right stack (Azure Synapse, Databricks, Matillion, or Fivetran + dbt). Implementation – building robust extract and load mechanisms, then applying transformations using SQL or Spark. Automation & orchestration – monitoring, alerting, and error-handling built in from day one. Governance layer – lineage, metadata, and access control to satisfy regulatory requirements. Testing & deployment – CI/CD pipelines, test datasets, and version control for transparency. The end result: a governed, AI-ready data platform that scales with the business. Compliance and Data Sovereignty When designing ETL pipelines in the UK, data compliance is never optional. Solutions must align with: UK GDPR and the Data Protection Act 2018, including lawful data transfer mechanisms. FCA and PRA operational-resilience frameworks, requiring defined RTO/RPO for critical services. NHS Digital DSP Toolkit (for healthcare providers), mandating data-handling standards. This means every pipeline should come with a clear processing role (controller vs processor), audit trail, and documented recovery procedure. From ETL to ELT and Beyond The shift from ETL (transform before loading) to ELT (transform after loading) is now mainstream. Cloud-native tools allow UK companies to load raw data quickly into scalable warehouses and apply transformations later — improving agility and reducing infrastructure cost. Modern pipelines increasingly combine: Batch and streaming data. iPaaS connectors for SaaS applications. DataOps monitoring to ensure continuous reliability. AI-readiness hooks, preparing datasets for analytics or machine-learning use cases. Choosing the Right Partner in the UK For most organisations, success depends less on the specific tool and more on the expertise behind its implementation. An experienced ETL pipeline development partner can help with: Migration from legacy ETL systems. Cloud-architecture design and best practices. Continuous support and monitoring. Compliance documentation and audits. Integration with BI, analytics, or AI layers. When evaluating providers, look for experience in your sector, cloud certifications (Azure, AWS, or Databricks), and proven delivery under UK compliance standards. The Road Ahead As the UK accelerates toward an AI-enabled economy, ETL pipeline development will remain a cornerstone of digital transformation. Reliable, transparent, and compliant data movement isn’t just an IT goal — it’s what empowers decision-makers to trust their insights and act faster. Whether you’re migrating legacy systems or building a new cloud data platform, the next generation of ETL pipelines is about more than data movement — it’s about enabling intelligence, innovation, and impact. About Sigli Sigli helps UK and European organisations modernise their data pipelines and prepare for the AI era. Our data engineers design, automate, and manage ETL and ELT pipelines with built-in governance, resilience, and transparency — so your teams can focus on insights, not infrastructure. Learn more about our data engineering services →

London’s businesses run on data — but without the right engineering backbone, volume turns into chaos. If you’re looking to hire data engineers in London, the goal isn’t just headcount; it’s building resilient pipelines, clean models, and a governed platform that leaders trust.This article explains why a robust data engineering strategy matters for London organisations and how partnering with a specialist (or augmenting your team) turns raw data into timely, actionable insight. We’ll cover pipelines, architecture, ETL, storage — and where Sigli’s Data Engineering Services London fit in.What is Data Engineering and Why London Businesses Need ItDefinition. Data engineering designs and builds the systems that collect, store, process, and prepare data for analytics — covering pipeline development, data architecture, ETL/ELT, and databases.Without dedicated data engineering:Poor data quality and low trust in metricsInefficient, manual data flowsPlatforms that don’t scale with growing dataMissed opportunities for revenue, efficiency, and CXWhy hire data engineers in London nowEfficient pipelines and a modern platform accelerate decision-making, reduce costs, and help you compete in London’s fast-moving markets (finance, retail, media, healthcare, and tech).Key Components of a Robust Data Engineering StrategyData Pipelines (Batch & Streaming)Build for scale and reliability. Ingest from SaaS, apps, legacy, and partners; validate and deliver consistent datasets to a warehouse or lakehouse with SLAs and observability.Sigli example. Sigli designs scalable, event-driven and batch pipelines with monitoring and alerting so stakeholders know when data is fresh and dependable — fuel for faster, better decisions.Data ArchitectureStructure that supports growth. Layered architectures (bronze/silver/gold) centralise data, separate ingestion from transformation, and simplify access for BI, product, and AI teams.Sigli example. Sigli’s reference architectures centralise and standardise datasets, improving discoverability and operational efficiency across London teams.ETL/ELT ProcessesClean, transform, enrich. Automate deduping, validation, and modelling; version your transformations; test business logic.Efficiency gains. Less time cleaning, more time shipping trusted metrics to stakeholders.Data Storage & CloudChoose the right foundation. Data warehouse, lake, or lakehouse — balance performance, cost, governance, and future AI workloads.Sigli example. Sigli advises on and implements cloud-based storage that scales seamlessly, with governance and cost controls built in.Benefits for London Businesses That Hire Data EngineersActionable InsightsUnified, well-modelled datasets expose real-time and historical views for accurate forecasting, personalisation, and faster experimentation.Efficiency & Cost SavingsStreamlined pipelines and standardised models reduce manual work, duplication, and infrastructure waste.Example. With an optimised pipeline, Sigli helps teams cut operational delays and shorten analytics lead times.Data-Driven InnovationA reliable platform frees product and analytics teams to prototype new features and launch data-enabled services with confidence.Security & ComplianceEmbed governance (access controls, lineage, audits, retention) to meet UK data protection obligations and strengthen trust.How Sigli’s Data Engineering Services London Help You ScaleOur approach. Sigli delivers tailored services for London organisations:Scalable, observable data pipelines (batch/streaming)Efficient ETL/ELT with automated testing and documentationModern warehouse/lakehouse architecturesCloud platform selection, cost optimisation, and governanceOngoing reliability engineering and platform supportImpact. Sigli has helped UK businesses design data systems that scale with demand, improving decision speed while reducing total cost of ownership.Real-life example (case study). A mid-market services company consolidated siloed reporting into a central lakehouse with automated ELT. Results: 70% faster report delivery, unified KPIs across departments, and a clear audit trail for compliance.Read more about how Sigli helped a client optimise their data architecture here. How to Hire Data Engineers in London (And Start Strong)Assess your data needsMap sources, critical metrics, latency, data volumes, and compliance constraints (e.g., PII handling).Design a scalable pipelineStart with high-value sources; prioritise reliability, observability, and schema/version management.Choose storage & tools wiselySelect warehouse/lakehouse platforms and a transformation framework that fit performance, governance, and cost goals.Implement ongoing supportAugment your team or partner with Sigli for monitoring, optimisation, and continuous delivery as your data grows.Tip: Whether you hire London-based data engineers or partner with a specialist, insist on clear SLAs, cost controls, and a roadmap that includes DataOps.The Future of Data Engineering in LondonDataOps becomes standard. CI/CD, testing, and observability for faster, safer data releases.Cloud & lakehouse adoption. Unifying analytics and AI workloads on elastic platforms.AI-powered engineering. Intelligent data quality, metadata enrichment, and adaptive workloads reduce toil and speed delivery.Sigli’s role. Sigli helps London businesses adopt DataOps and AI-driven engineering patterns so they can ship trusted data products faster and stay competitive.

In modern software development, certifications are more than badges — they’re signals of quality, expertise, and alignment with industry standards. When releases are frequent and user expectations are unforgiving, Quality Assurance (QA) becomes the safety net that protects functionality, security, and performance. This article explains why choosing a Certified QA Testing Company UK can materially improve delivery outcomes. We’ll unpack the most relevant certifications, show how they translate into better testing practices, and outline practical steps to verify a partner’s credentials. Whether you’re an SME seeking predictable releases or an enterprise with strict compliance needs, certifications help ensure your QA partner follows robust processes, documents evidence, and delivers reliable software — release after release.The Most Common Certifications for QA Testing Companies in the UKISO 9001 (Quality Management)What it means: ISO 9001 certifies that a company has a documented, continuously improving quality management system.Why it matters for testing: In QA, it promotes repeatable processes —test planning, execution, defect management, and retrospectives — so quality isn’t left to chance.ISTQB (International Software Testing Qualifications Board)What it means: ISTQB certifies individual QA professionals (Foundation, Advanced, Specialist levels).Why it matters: Teams staffed with ISTQB-certified testers share a common vocabulary and method, improving test design, coverage, and risk-based prioritisation. For a Certified QA Testing Company UK, high ISTQB density signals a mature testing culture.CMMI (Capability Maturity Model Integration)What it means: CMMI appraises organisational maturity (from Level 2 to 5) across engineering and management processes.Why it matters for QA: It drives disciplined planning, measurement, and continuous improvement—vital for regression, performance, and automation programmes at scale.Other Relevant Certifications & ComplianceITIL: Strengthens incident, change, and problem management around QA in CI/CD environments.PCI DSS: Essential for payment-touching apps; assures secure handling of cardholder data during testing.GDPR compliance: Protects personal data in test environments (masking, minimisation, retention).HIPAA: For health data, ensures privacy and security obligations in test design and data handling.Using the phrase Certified QA Testing Company UK isn’t just SEO—it reflects how these credentials align your partner with recognised industry standards.Why Choosing a Certified QA Testing Company UK Is Crucial for Software DevelopmentGuaranteed quality and reliability. Certified providers prove they follow audited processes, reducing post-launch defects, performance surprises, and security regressions.Access to expert professionals. Certifications like ISTQB indicate disciplined test design, risk-based coverage, and better automation strategy — accelerating feedback loops.Compliance with industry standards. A Certified QA Testing Company UK understands regulatory contexts (GDPR, PCI DSS, and where relevant HIPAA), ensuring your releases meet legal and contractual obligations.When selecting a QA testing partner, opting for a certified company ensures adherence to industry standards and best practices. A certified QA testing company UK not only meets regulatory requirements but also demonstrates a commitment to quality and continuous improvement.For instance, Sigli offers a QA on Demand service that provides flexible and immediate access to expert bug-fixing and testing services. This model allows businesses to scale their testing resources as needed, ensuring high-quality software without the overhead of maintaining a dedicated in-house QA team.How to Verify a Certified QA Testing Company UK1) Check the certifications.Confirm ISO 9001, CMMI appraisal level, and team ISTQB mix on the vendor’s site; verify via official directories when available.2) Review testimonials and case studies.Look for measurable outcomes tied to process maturity (defect leakage trends, cycle time, automation stability).Floral Supply Chain Tech — Sigli built an internal management platform for a floral supply company, improving logistics oversight and communication. QA involvement: focused manual testing to stabilise the application and improve usability.→ Floral Supply Chain TechERP Platform Enhancement — Sigli enhanced the ArkSuite ERP for a global automation leader, adding custom dashboards to improve UX and efficiency. QA involvement: comprehensive functional and performance testing for reliability.→ ERP Platform EnhancementInteractive E-Learning Solutions — Sigli delivered a feature-rich learning platform for expert-led courses. QA involvement: scalability and stability testing for peak usage.→ Interactive E-Learning Solutions3) Ensure they follow established frameworks.Ask how they run functional, regression, non-functional (performance, accessibility, security) suites; how they manage environments and test data; and how they evidence coverage and traceability.The Cost of Working with a Certified QA Testing Company UKIs it worth the premium?Certified partners may cost more upfront, but they pay back through structured delivery, fewer escaped defects, and smoother audits — especially critical for regulated or customer-facing apps.Long-term benefits.Lower rework and hot-fix overheadFaster, safer releases (predictable regression cycles, stable automation)Stronger compliance posture (less risk in privacy and security reviews)ROI of Quality Assurance.The upfront investment in a Certified QA Testing Company UK reduces bug-related delays, safeguards brand trust, and accelerates time-to-market. Over multiple releases, that compounds into a lower total cost of ownership and higher customer satisfaction.Want a reliable, certification-backed QA partner? Book a 30-minute call with Sigli to explore QA on Demand and see how certified practices improve release quality.Book a call

Launching an MVP is supposed to be the fastest way to validate demand. In financial services, the word “minimal” can be misleading: you are shipping into an environment shaped by SCA/PSD2, Open Banking, UK GDPR, and the FCA’s expectations for governance and resilience. This guide turns the usual checklist into a readable playbook — so you can hire the right team in London, make the right architectural calls, and keep momentum without stumbling over compliance.Why FinTech MVPs are different (and risky)Even a slim payments or onboarding flow touches multiple regulated surfaces at once. Strong Customer Authentication (SCA) dictates how you structure two‑factor experiences and when you can legitimately avoid them via exemptions such as merchant‑initiated transactions, low‑value payments, or transaction risk analysis. Know‑Your‑Customer and anti‑money‑laundering controls influence everything from what data you collect to how you handle false positives, sanctions matches, and suspicious activity reports. Data protection runs in parallel: your lawful basis, retention policies, DPIAs and DSAR handling determine whether your product is both usable and defensible.What happens if you under‑engineer these layers? Banks and PSPs may refuse to onboard you or shut you down after testing. The FCA can query your governance and operational resilience. Privacy missteps lead to audits and reputational damage. Worst of all, re‑architecting after a failed pilot can cost more than building it correctly the first time. The safe conclusion is not “move slowly,” but “design compliance into the product fabric from day one.”What a regulatory‑ready MVP looks likeA credible FinTech MVP treats authentication, onboarding, and privacy as product features, not as paperwork.SCA/PSD2. Map your payment scenarios — one‑off, recurring, merchant‑initiated — and implement two‑factor authentication with a measured step‑up. Exemptions should be evaluated by a server‑side policy engine and every decision should be recorded so you can explain why SCA was, or wasn’t, applied. Recovery and retry paths must avoid duplicate charges and preserve the authorisation context.KYC/AML. Choose providers for PEP and sanctions screening, decide when documentary evidence or non‑documentary checks are appropriate, and define thresholds that trigger manual review. Ongoing monitoring is not a later phase: set the cadence now, capture adverse media, and keep tamper‑evident evidence of what you checked and when.FCA expectations. Decide early whether you need your own permissions (EMI, AISP, PISP) or will operate as an agent. Build your policy stack — risk, complaints, financial promotions, incident management and outsourcing — alongside the product. Operational resilience is practical: who declares an incident, what your impact tolerances are, and how you communicate with customers and partners.Open Banking. Scope consent to the minimum necessary, explain purpose and duration in plain language, and implement token lifetimes, refresh, and revocation from the outset. Resist copying bank data you don’t need; minimise and expire.UK GDPR & privacy. Complete a DPIA where risk is high (for example, biometrics or credit‑related processing). Record lawful basis per activity, separate consent from your terms, automate retention and deletion, and honour user rights without a support backlog.PCI DSS (if you touch cards). Aim for zero PAN handling by pushing tokenisation and vaulting to your PSP. If card data ever crosses your boundary, scope tightly, segment networks, and keep evidence of scans and controls.Security and accessibility. Align builds with OWASP ASVS, manage secrets properly, enforce least privilege in cloud/IAM, and maintain an audit trail that links user actions to business decisions. Accessibility is not a nice‑to‑have: authentication and payments journeys must work for keyboard and screen‑reader users, with clear focus order, contrast, and time‑outs that can be extended.How to hire MVP developers in LondonLook for teams that have shipped into this reality before. References for SCA and KYC implementations are worth more than generic portfolios; ask to see sample architectures and test evidence. Probe for FCA awareness — have they collaborated with SMF holders or an MLRO, and can they show you the artefacts?On the engineering side, expect a secure SDLC with design reviews and threat modelling, CI gates for linting, tests and dependency checks, and an automated suite that regression‑tests authentication, onboarding, payments, and consent. Mature teams arrive with playbooks: incident response, rollback, fraud handling, and a plan for collecting evidence during the incident so audits aren’t guesswork later. Cadence matters too — short, focused iterations with a demo every one to two weeks, and explicit compliance checkpoints during discovery, build, and pre‑launch.When you run vendor due diligence, ask for real outputs rather than promises: exemption decision logs from a previous build, a DPIA template they actually used, a working audit trail, and a redacted incident post‑mortem. The right partner will be comfortable showing you how they work, not just what they say.Pitfalls to sidestepMost failures rhyme. Over‑collecting personal data creates GDPR exposure without improving conversion. Skipping exemption logic bloats your SCA prompts and crushes success rates. Storing or logging PANs — even unintentionally — explodes your PCI scope. Thin or immutable audit trails make it impossible to explain KYC and payment decisions. Ignoring accessibility excludes customers and draws scrutiny. And unclear permissions with your FCA status or PSP role can stall onboarding when you can least afford it.Timelines and cost, realisticallyDisclaimer: This guide is informational and not legal advice. Engage qualified compliance counsel and coordinate with your principal firm and PSP as needed.A typical path looks like two to four weeks of discovery and design to map data flows, choose providers, draft your DPIA and SCA policy; six to ten weeks of integration work across auth, KYC, payments, consent and logging; and a further two to four weeks for hardening—pen testing, accessibility review, game days and an evidence pack. Budget for PSP fees, KYC checks, sanctions data, fraud tooling, observability, penetration testing, accessibility audit, legal review and a contingency for iteration after PSP or FCA feedback. The secret to hitting dates is simple: tie each user story to a control or evidence item so you never scramble before launch.Ship faster without compliance re‑work. Get an evidence‑ready MVP team versed in SCA/PSD2, KYC/AML, FCA & GDPR.Book a 30‑minute call →Prefer email? Write to info@sigli.com.

If you need to move quickly without giving up product direction or intellectual property, IT team augmentation is usually the right call. You can add experienced engineers within days, keep your ceremonies and technical standards, and ensure that code, documentation, and know‑how remain inside your organisation. When your scope is clearly defined, outcomes are fixed, and the budget must be capped, outsourcing delivers best: treat it as a black‑box engagement with strong governance and acceptance criteria. Prefer a deeper dive? Compare models on the Team Augmentation page, and see how staff augmentation services for developers in the UK work in practice.The Decision Factors for IT Team Augmentation in the UK (Control, IP, Speed, Risk, Cost)Control is the clearest dividing line. With augmentation, you keep the steering wheel: your product and engineering managers decide the roadmap, architecture, tooling, and Definition of Done, while augmented engineers pair with your team and follow your sprint rituals. Outsourcing flips the operating model. You still set business goals and review outcomes, but the vendor runs delivery day to day, optimising for contractual milestones rather than your internal cadence.Intellectual property and knowledge transfer follow from this. Augmentation concentrates all assets in your systems — from source code and infrastructure‑as‑code to runbooks and architectural decision records—so practices like pairing, code reviews, and internal wikis steadily build your long‑term capability. Outsourcing can also transfer IP, but the tacit knowledge that makes systems maintainable often remains with the vendor unless you plan structured shadowing, joint incident drills, and a formal handover.Speed to impact tends to favour augmentation in the early weeks. Once access is granted, a small pod can start within days and aim for a first production‑ready pull request within ten business days. Outsourcing typically needs a period of discovery and a statement of work; once underway it can deliver large packages predictably, but that initial ramp is slower.Risk profiles differ. If your scope is evolving, augmentation lets you absorb volatility sprint by sprint and adjust priorities without change requests. Outsourcing excels where the scope is stable and risks are best handled through specification, acceptance tests, and change control. Neither model is inherently cheaper; cost outcomes depend on throughput, quality, and time‑to‑value. A senior augmented pod that ships the right thing faster will often outperform lower day rates that lead to rework. Fixed‑price outsourcing can be highly efficient for a well‑bounded scope, but chasing the lowest rate can increase total cost of ownership when quality and integration suffer.When to Choose IT Team AugmentationChoose augmentation when scope is fluid and learning can’t pause, when you want to upskill your existing team through pairing and rigorous code reviews, and when you need specialist capability now without adding permanent headcount. The goal is early and visible impact — first PR in ten business days, then steady ownership of a defined slice of the backlog—while all IP and operational knowledge remain in your repositories. See it in action in Implementing AI on one of the UK’s most popular property data tools: an embedded data/ML pod worked within the client’s rituals and infrastructure, delivering production value quickly while keeping code and know‑how in the client’s repos. Curious how this looks end‑to‑end? Explore a pilot plan on the Team Augmentation page and see how staff augmentation services for developers in the UK are run in practice.When to Choose OutsourcingOpt for outsourcing when requirements are stable, outcomes are easy to measure, and the budget must be capped. It works well for non‑core modules and integrations that can be delivered as a black box with a clear interface and strong acceptance criteria. Your governance should emphasise discovery, traceability to acceptance tests, performance gates, and a structured handover so that maintenance is predictable once the engagement ends.Hybrid ModelsMany UK organisations blend the two. Keep core, domain‑heavy work close — either fully in‑house or with augmented engineers embedded in your team — while outsourcing peripheral modules such as adapters, migrations, or dashboards. Make the hybrid model safe with shared repositories, agreed integration patterns, common CI gates, a unified Definition of Done, and scheduled handovers. The result is scale without fragmentation: the centre of the product remains coherent, while specialist work streams progress in parallel.Decision MatrixImagine a simple 2×2. The horizontal axis represents scope clarity from low to high; the vertical axis represents the need for control and IP retention from low to high. High control and low clarity points to augmentation: run a short discovery sprint, fix access on day one, and target an early PR. Low control and high clarity favours outsourcing: lock the scope, codify acceptance tests, and budget a buffer for edge cases. If both clarity and the need for control are high, use a hybrid: keep the kernel of the system in‑house and contract peripheral work with tight integration checks. When both are low, resist committing to a delivery model; re‑scope first and use two to four weeks of discovery to de‑risk assumptions. Typical pitfalls include onboarding delays, hidden edge cases, integration drift, and premature commitment to fixed price.Implementation PlaybooksFor augmentation, think in a fourteen‑day arc. Day zero to one cover access, security briefings, and IR35/GDPR paperwork. Days two to three focus on pairing to set up environments and smoke tests. By the end of the first week the team should have selected and delivered a scoped starter ticket and opened a production‑ready pull request with tests and documentation. The second week is about incorporating review feedback, releasing safely—feature flags help—and then independently owning a small backlog slice. Define KPIs such as lead time, PR cycle time, and escaped defects so value is visible.For outsourcing, sequence the engagement from discovery to delivery to handover. Discovery clarifies goals, constraints, non‑functional requirements, data flows, and security and accessibility needs. The statement of work then fixes scope, milestones, acceptance tests, change control, IP transfer, and residency and audit rights. Delivery proceeds in iterations with regular demos and traceability to acceptance tests. Handover closes the loop with code, documentation, runbooks, test suites, and a joint production‑readiness review so operations do not inherit surprises.Use augmentation when you must move fast without losing control or IP; use outsourcing when your scope is stable and outcomes and budget are fixed; use a hybrid when you want core knowledge to remain in‑house while peripheral work streams scale externally. To see how this plays out in delivery, compare models on the Team Augmentation page or review how staff augmentation services for developers in the UK operate in practice.

In today’s digital-first business environment, functional and regression testing is essential for UK SMEs seeking to protect revenue, reduce churn, and improve operational efficiency. Functional testing ensures that every feature works as intended, covering both happy paths and key negative scenarios. Regression testing acts as a repeatable safety net, confirming that recent changes haven’t broken previously working functionality. Together, these approaches safeguard your product, protect revenue, and provide measurable ROI.Functional vs Regression Testing — Executive DefinitionsFunctional testing evaluates whether individual features work correctly, covering both positive scenarios (happy paths) and negative edge cases. It ensures users can complete their core tasks without friction.Regression testing focuses on stability after changes. Every new release or code update risks breaking previously functional workflows, and regression testing acts as an automated safety net. Together, functional and regression testing give UK SMEs confidence that releases are reliable, errors are caught early, and revenue-critical paths remain intact.By combining both, UK SMEs gain confidence in product stability while reducing manual QA effort and incidents.The 7 High-ROI User Journeys to Automate FirstTo maximize ROI, focus on flows that directly impact revenue, retention, or support costs.1. Authentication & Account Access (including SSO/MFA)Login friction is a key driver of churn, and account lockouts generate costly support tickets. Automation should cover signup, login, password reset, MFA, SSO, and role checks. This ensures that new account flows are stable and that any changes to authentication logic don’t introduce errors. Case Study: Permissions and Onboarding Stability2. Checkout & Payments (SCA/3DS)Revenue depends on smooth payment flows. Automation should validate the entire checkout process, including cart, address entry, shipping, Strong Customer Authentication (SCA) challenges, and receipt/refund handling. Testing edge cases such as failed 3DS challenges or interrupted sessions ensures fewer chargebacks and lost sales.Case Study: Ecommerce portal/booking flow QA3. Billing & SubscriptionsBilling errors directly impact retention and create disputes that escalate to legal or customer support costs. Automated tests should cover plan changes, proration, VAT, credit notes, and subscription cancellations. Validating billing calculations and invoicing logic protects revenue integrity and customer trust. Case Study: Finance logic regression coverage in platform upgrade4. Core Transaction / Order or Booking LifecycleYour money path must be flawless. Automation should verify all stages: create → update → cancel → state transitions → notifications → audit trails. End-to-end testing ensures that orders, bookings, or transactions remain consistent even as business logic evolves.Case Study: Manufacturing & Industrial Monitoring Case Study 5. Search & Filters (with Sorting/Pagination)Discoverability drives conversion. Automated tests should validate search relevance, empty states, boundary conditions, and sort stability across datasets. Silent failures here can quietly reduce sales or frustrate users, especially in complex marketplaces or content-heavy platforms.Case Study: Data-heavy UX correctness6. Data Import/Export & IntegrationsData corruption or failed integrations can be catastrophic. Automation should handle CSV templates, validation rules, large file handling, webhook events, retries, and contract tests for APIs. Automated coverage ensures onboarding and data migration processes remain smooth, saving both time and support costs.Case Study: Modern integration surface7. Settings & Permissions (RBAC, Tenant Isolation)Security and compliance are non-negotiable. Automated tests should validate the allow/deny matrix, audit logs, and multi-tenant isolation. This prevents costly access leaks or accidental data exposure in SaaS or enterprise platforms.Case Study: Role-heavy ERP or B2B platformFor teams looking to get started, Sigli’s QA on Demand can help set up a lean regression suite for your top journeys, making testing efficient and scalable.

In today’s fast-paced startup ecosystem, getting a Minimum Viable Product (MVP) to market quickly is crucial. MVP development services in the UK allow businesses to test ideas, validate assumptions, and engage customers before committing to full-scale development. Whether you’re a UK-based startup or a scale-up, leveraging MVPs helps reduce risk, ensures market fit, and accelerates time-to-market.In this article, we will break down the best MVP development companies in the UK, provide realistic cost estimates for 2025, and give you a no-nonsense checklist for selecting the right partner for your MVP project. If you want to dive deeper, feel free to schedule a PoC & MVP Discovery call with Sigli to discuss how we can help your business grow.‍The Rankings (UK-Focused)Sigli — PoC & MVP Development (UK/EU Delivery)‍Sigli is best suited for B2B SaaS, data/AI, integrations, and measurable validation. They specialize in a discovery → prototype → release methodology, ensuring that analytics are implemented from day one. Post-go-live support and success are central to Sigli’s approach, offering a comprehensive MVP development process with a strong focus on data-driven decision-making and analytics that drive future iterations.Coreblue (Plymouth, England)‍Coreblue is ideal for regulated/enterprise MVPs, offering strong discovery and quality discipline. Their meticulous approach to discovery and MVP foundations makes them a great choice for regulated industries like finance and healthcare, ensuring compliance with industry standards.thoughtbot‍Thoughtbot focuses on design-led product practices, combining high-quality UX/UI design with functionality. They are perfect for businesses focused on user experience, delivering MVPs that blend intuitive design with solid performance.MVP Development Costs in the UK (2025)The cost of MVP development varies depending on the complexity of the project. For entry or lightweight MVPs, the typical cost ranges from £15,000 to £30,000. These MVPs usually feature basic functionalities, are built for a single platform (web or mobile), and have limited integrations.For standard MVPs, costs generally fall between £30,000 and £70,000. These MVPs tend to have more complex features, support multiple platforms, and integrate third-party services, offering a more robust solution for testing product-market fit.When it comes to complex or enterprise-level MVPs, costs can range from £70,000 to £200,000+. These MVPs typically include advanced features such as real-time data processing, machine learning, support for multiple platforms, and high security, making them ideal for regulated industries like finance or healthcare.What Drives MVP Costs?Several factors influence the cost of MVP development:Scope/Feature Count: More features like user authentication, payment systems, and data storage increase costs.Platforms: Developing for multiple platforms (iOS, Android, web) adds complexity and cost.Integrations: APIs, third-party services, and data integrations raise the price.Data/AI: Projects involving advanced data processing or machine learning require additional expertise and resources.Compliance & Security: For industries with strict regulations, such as healthcare or finance, additional security measures and certifications are necessary.Design Depth: Custom UI/UX design adds to costs compared to pre-built templates.Seniority Mix: Senior developers or specialists (like data engineers or AI experts) typically come at a higher price.Pricing ModelsFixed-Scope Discovery → Fixed/Target-Cost Build: This is a common approach where the MVP’s features are defined during the discovery phase and built within a set budget.Sprint-Based (Agile): Ideal for projects that need flexibility and ongoing iterations.Hybrid: A combination of fixed-scope discovery with agile sprints for future feature development.How to Control Your BudgetYou can control your MVP budget by focusing on ruthless scoping, prioritizing must-have features, and launching on a single platform first before expanding to others. Reusing design systems, using no-code or low-code solutions for simpler MVPs, and focusing on analytics before "nice-to-haves" can also help keep costs down.Timelines & Delivery PatternsThe typical timeline for developing an MVP ranges from 4 to 12 weeks, depending on the complexity and features. Timelines can be shortened or extended depending on factors such as team size, feature complexity, platform support, and integration depth.The milestones generally include:Discovery (1-2 weeks): Defining the MVP scope and features.Prototype (2-3 weeks): Creating a clickable prototype to validate concepts.MVP Build (4-6 weeks): Developing the first usable version of the MVP.Beta (2-4 weeks): Releasing to a select group of users for feedback.Iterate & Refine (ongoing after launch): Enhancing the product based on user feedback.According to Adriana Gruschow, building an MVP is not about speed alone; it’s about structured experimentation. While typical timelines range from 4–12 weeks to the first usable release, allowing time for early testing and iteration ensures the MVP truly meets market needs.How to Choose the Right UK MVP Partner (Checklist)When choosing an MVP development partner in the UK, consider the following:UK presence/time-zone fit: Ensure the partner’s location aligns with your team’s working hours.Sector experience & case studies: Look for partners with relevant industry experience.Architecture & code quality standards: Make sure they follow best practices for scalable and maintainable code.Security/compliance: Ensure they meet your security and compliance requirements.Analytics/experiment setup: Make sure they can implement tracking and testing for future iterations.Handover/scale plan: Ensure there is a clear plan for handover and scaling the MVP post-launch.Support SLAs: Ensure they offer adequate support and maintenance post-launch.Red Flags to Watch Out ForVague scope with no clear definition of features.Lack of a release plan or commitment to post-launch iterations.No commitment to QA automation or proper testing.No guarantee of post-launch iteration or support.Sigli’s MVP Approach (Why Work With Us)Sigli follows a three-stage MVP development model: Discovery Sprint → Clickable Prototype → MVP Build & Telemetry. We leverage CI/CD, feature flags, observability, and experiment frameworks to ensure that our MVPs are built for long-term success. Our engagements typically follow a fixed-scope model for pilots and sprint-based development for iterative builds.Ready to take your MVP to the next level? Book a scoping call to see how Sigli can accelerate your MVP development.

The initial wave of hype around AI triggered widespread concerns about the potential risks of the technology. However, now, it has become clear that AI can act as a powerful tool capable of transforming business processes. But how can companies find the right approaches to enterprise transformation? And how can they distinguish true innovation from mediocre solutions? Max Golikov, Sigli’s CBDO, discussed these questions with Thibaud De Keyzer, CEO of Mobilexpense, Board Advisor at TechWolf, and Strategic Advisor at Fortino Capital, in the latest Innovantage podcast episode.Thibaud began his career at IBM, and later spent nearly a decade at SAP, Europe’s largest software company, where he held roles across Belgium, France, and the Netherlands. He founded and successfully sold an SAP consulting business before becoming the CEO of Mobilexpense.Thibaud joined this company three years ago. At that time, the team had a clear goal to make expense management fully touchless. Founded in 2000, Mobilexpense is headquartered in Belgium but operates as a truly European company. It has a presence in eight countries. Its team consists of 200 employees, who represent 28 nationalities. These are people from different generations and with different backgrounds.Bridging cultural gaps in such a team is a crucial task. Thibaud admitted that the foundation was already in place when he became the CEO of the company. His role has mainly been to nurture and strengthen it. For Thibaud, this diversity is not just a cultural asset but a business advantage as it offers perspectives from many angles.The value of AI in business leadership and the role of CEOsInnovation has always been part of Mobilexpense’s principles. For example, the company launched a web-based solution long before mobile technology became mainstream. Many of the processes were embedded in the product years ahead of competitors.When it comes to AI, for Thibaud, it is not just a new feature but the next fundamental utility. It is comparable to the internet in the 1990s or mobile phones in the 2000s. Artificial intelligence represents the next great wave of transformation. According to Thibaud, innovation is one of the strongest cultural drivers. And at the same time, it is a leadership responsibility. At Mobilexpense, he encourages curiosity across the team and provides employees with access to the tools, time, and resources needed to experiment. His role as CEO is to connect the dots between these initiatives and ensure they align with the company’s broader vision.AI is a non-negotiable priority that requires CEO-level ownership. He leads this effort personally and guides teams to focus on solving real business problems instead of pursuing technology for its own sake. How to prioritize innovation projects As Thibaud explained, at Mobilexpense, every new idea enters through a discovery tool where employees can contribute suggestions. From there, the most promising concepts are evaluated with input from business stakeholders and the CTO.Projects that pass this initial filter quickly move into a 90-day pilot phase. If early results are positive, the next step is to test scalability. Once a project proves it can scale, it is moved into production. Here, governance becomes critical. It is very important to track ROI and ensure that the project is secure and ethical.AI projects are not prioritized just because this is AI. Their value is measured by the tangible improvements they can bring to the business. For example, in customer success, that might mean reducing ticket resolution time; in sales, accelerating deal conversions; and in HR, shortening the time required to qualify candidates.For Thibaud, it is necessary to make sure that AI adoption delivers compounding value across the entire organization.How to learn from experience: AI in business leadership insightsBoth failed and successful projects can teach us a lot. Thibaud mentioned a couple of examples from his practice. One early AI experiment involved deploying a sophisticated chatbot in customer success. The team attempted to load the model with a large volume of information to provide comprehensive answers. However, the results fell short. Responses were unclear, unhelpful, and frustrating for customers.Looking back, Thibaud noted that the project’s biggest mistake was removing the human-in-the-loop too quickly. The team later corrected this. They scaled back the chatbot’s scope and reintroduced experts at critical points in the process. The failure demonstrated that human oversight remains essential in AI-driven customer support.On the success side, he pointed to an internal project designed to help auditors quickly access compliance information related to expense reports and company policies. Initially, it was introduced as a small internal efficiency tool. But the project attracted unexpected customer interest and was rolled out externally. It enabled clients to check compliance before submitting expense reports and turned out to be five times more valuable than anticipated.AI in business leadership and the modern CEO insightsAI is reshaping not only how companies operate but also how CEOs should lead their teams. Today, CEOs often feel the pressure to innovate, driven by the fear of being outpaced by competitors. In this context, AI can help a lot. A decade ago, product development cycles required months of effort. Modern AI tools allow companies to test, iterate, and scale at unprecedented speed. A “10x engineer”, who is 10 times better than a mediocre engineer, can become a “1000x engineer”. A skilled professional who leverages AI effectively can outperform peers not just tenfold but a thousandfold. Now, for Thibaud, one of the greatest concerns is neither the technology itself nor the competition. It is vital for him to ensure that his team remains curious and engaged with AI. For him, a lack of interest in AI can be a sign of the organization’s weakness in today’s fast-moving landscape.Building a culture of innovationThibaud believes Mobilexpense has successfully fostered a culture of innovation.To make innovation part of everyone’s responsibility, Mobilexpense holds monthly all-hands meetings where teams share outcomes, discuss opportunities, and raise concerns. He is confident that it is important not to make innovation a purely top-down initiative. It needs to come from the people.He explained that in his role, he also has strong leadership support from the CPO and the CTO. At the same time, he values continuous employee feedback.Though he remains quite optimistic about the future of this technology, AI’s rapid evolution at a large scale looks like an “uncontrolled experiment.” Only time will show the real impact on people and humanity.An AI learning curveThibaud shared that the past 12–15 months have been a period of intense learning for him as he wanted to truly understand what AI is, what it is good at, and what it is not.He recalled that during his time at IBM (it was around 2007–2010), artificial intelligence was already a buzzword, but there was little clarity on practical application. Today, thanks to breakthroughs in large language models (LLMs), the possibilities have become tangible.Even though, as a CEO, Thibaud doesn’t need to code or build infrastructure himself, he sees the potential of AI in such tasks. According to him, it is essential to grasp AI at the application level. It is important to find the use cases where AI can give an advantage in business, for example, in identifying patterns, creating content, or running predictive models.With AI, innovation.has become more accessible than ever before. Young people demonstrate outstanding skills in working with tools like LLMs to explore business problems and potential startup ideas. Now, you don’t need to have a full-stack developer or a machine learning engineer for every task. Nearly 80% of questions can be answered by an LLM. And these answers will be good enough at least for moving forward with your ideas.AI in business leadership and the future of workAccording to Thibaud, AI is unlikely to eliminate roles entirely. However, it will transform how people work. While some jobs may become obsolete, new roles will emerge. And quite often, these new roles will require different skills and approaches.Curiosity and engagement with AI will be key for employees to thrive. Entry-level roles may be affected first, particularly tasks that are repetitive, as they can be easily automated. However, younger professionals who embrace AI early are well-positioned to adapt. At the same time, more experienced employees can leverage AI tools to work more efficiently and focus on higher-value tasks.For example, developers using tools like GitHub Copilot can reduce time spent on routine documentation or repetitive coding. It means that they will have more time to concentrate on more meaningful work.AI in business leadership and educationAI is also reshaping learning for the next generation. Students increasingly rely on laptops and digital tools. As a result, the value of traditional skills like handwriting can become rather questionable. The pace of technological change means today’s students may receive education very differently from previous generations. Curricula must adapt continuously, and the skills taught at the start of a multi-year program may be outdated by graduation. AI can now draft essays or assist with research far more efficiently than students could on their own. But true learning comes from the process, which includes structuring ideas, conducting research, and developing critical thinking.Apart from that, Thibaud noted that formal education is no longer the only path to success. Many of his colleagues at Mobilexpense, including some in leadership roles, never completed traditional schooling. But they have strong self-learning skills and solid practical experience. Moreover, AI can be used as a great research tool. Nevertheless, it shouldn’t be seen as a replacement for critical thinking. It is still very important to know how to ask the right questions.AI can help with market research or idea generation. But the ultimate intelligence lies in identifying the right problem to solve and understanding the target audience. Human judgment still remains central. AI-native companiesToday, we can observe the emergence of AI-native companies. Such startups are built from the ground up with AI tools integrated into their business models. These can be just five-person teams creating projects that could become unicorns. Such companies can scale efficiently and leverage AI to accelerate research, planning, and product development.However, established companies like Mobilexpense retain significant advantages. They have extensive transactional data, deep industry insights, and a loyal customer base. With all this, they can leverage AI to enhance existing operations and create new value.For example, for Mobilexpense, AI presents an opportunity to transform expense management from a routine administrative task into a strategic process. Thanks to the AI capabilities, the company can provide a comprehensive solution that supports better decision-making and creates a more engaging experience for users.How investors should evaluate innovationsLarge language models are just a small subset of AI, which spans a wide range of computing capabilities. For executives and investors, understanding the different subsets and their practical applications is crucial. While AI offers significant opportunities, not every solution represents true innovation.Many new startups that enter the market appear to leverage AI superficially. For instance, they just package LLMs into niche applications without addressing real business problems. These so-called “ChatGPT wrappers” often fail to deliver long-term value and are likely to be demystified quickly. Investors always need to analyze the actual impact and novelty of AI solutions to distinguish genuine innovation from marketing hype.Thibaud mentioned that AI is similar to prior waves of tech cycles, where repetitive business tasks were transformed into scalable software solutions. The potential of artificial intelligence is enormous. But not every AI application substantially improves outcomes. For this reason, investors should focus not only on the initial idea alone, but also on teams and their ability to pivot and execute. Enterprises have broader expertise and resources than smaller businesses. That’s why they are better positioned to validate emerging AI technologies. Smaller companies or less experienced investors may be more vulnerable to hype and may adopt solutions that lack sustainability. Thibaud predicted that the current wave of AI hype is likely to settle within the next 12–24 months. Its true winners will be those startups that can integrate AI into enterprise-ready, platform-ready, and scalable solutions.AI’s greatest impact: What to expectAt the end of their conversation, Max also asked Thibaud to share his thoughts about the areas that are most likely to be transformed by AI.While it is still difficult to predict the full scope of AI’s impact, Thibaud believes the greatest potential lies in enabling augmented humans. These employees across different departments will leverage AI tools to dramatically enhance productivity and decision-making.According to Thibaud, in the future, engineers, salespeople, and product managers will be able to operate at a thousand-times capacity. It will be possible not only through technology, but also by using AI to solve real business problems. The key idea that Thibaud emphasized is that tech transformations have never been about technology but also about business problems. In turn, business is always directly related to people.Given this, it becomes vital to educate people, motivate, and incentivize them to get on the AI train. As long as organizations stay transparent, they can work together to make this world a better place for everyone.How technologies are changing the world around us is one of the most widely discussed topics in the Innovantage podcast. If you are curious to learn more, don’t miss our next episodes that will uncover new insights from leading tech and business experts.