#308 Buying AI for Finance? What You Need to Know Before You Commit Lyndon Stickley & Rob Steele CEO & CFO, iplicit
Choosing AI for finance is a consequential decision: finance leaders need to weigh the promise of faster analysis against the requirements for accuracy, security, auditability, and predictable costs. In this GrowCFO Show episode, Lyndon Stickley and Rob Steele of iplicit discuss how CFOs can assess the AI market without being swept up in hype or delaying necessary finance-system changes.
They compare the current AI investment boom with the dot-com era and consider how uncertainty can make CFOs hesitate when modernizing legacy systems. Stickley and Steele distinguish AI features from the underlying software architecture, emphasizing the importance of a stable cloud foundation and a finance ledger that produces consistent, auditable results.
Their practical guidance is to apply AI selectively: use it for analysis, forecasting, modeling, and identifying unusual patterns, while keeping core bookkeeping and ledger processes controlled and dependable. Before committing, finance leaders should also evaluate security, resilience, long-term costs, vendor track record, customer support, and staff readiness.
About Lyndon Stickley
Lyndon Stickley is a seasoned, ‘hands-on’ serial entrepreneur, having built and sold six companies during the last 35 years. He has a strong technology bias within his portfolio and has been involved in developing businesses from start-up to 400+ employees. With a particular focus on Sales and Marketing, he is expert in building high-performance teams and developing growth strategies for delivering service-orientated propositions. He was instrumental in assembling iplicit’s founding team and GTM strategy, starting out in 2016 as non-executive director, and assuming CEO position in 2022.
About Rob Steele
Rob Steele’s career in finance software began at 18 when he co-founded Exchequer Software and grew it, without outside investment, into a multi-million-pound business, acquired by IRIS Software in 2005. After supporting a number of start-ups in various industries after this, he joined iplicit as CEO in 2018, when the business had just six employees. He later moved into the CFO seat in 2022 to steer the business through a sustained period of hyper-growth.
Key topics covered:
- Lyndon Stickley compares the AI investment surge with the dot-com bubble, warning that high expectations and investment do not guarantee that every vendor will endure.
- The speakers say AI marketing labels such as “AI-native” may not explain how a finance product actually works; CFOs should investigate the architecture behind the claims.
- They stress that the ledger should remain accurate and auditable, with AI used around core bookkeeping rather than trusted to make unverified financial entries.
- AI is presented as a stronger fit for probability-based work—including forecasting, modeling, variance analysis, and fraud detection—than for deterministic accounting tasks.
- Finance leaders are advised to assess security, scalability, token-related costs, vendor stability, and access to human support before selecting a system.
- Rob Steele recommends training staff to use AI responsibly and efficiently, including ways to avoid unnecessary credit consumption.
Links
Timestamps:
- 00:06:22 — Lyndon Stickley discusses AI market hype and draws parallels with the dot-com bubble.
- 00:09:37 — The speakers explore why CFOs are moving away from legacy systems and why AI-related noise can complicate decisions.
- 00:12:03 — They discuss AI software labels and explain why finance needs a dependable, deterministic ledger.
- 00:18:02 — The conversation addresses AI security, resilience, scalability, and potentially variable usage costs.
- 00:33:00 — Rob Steele describes AI as a useful analyst for forecasting and reporting, but a poor substitute for dependable bookkeeping.
- 00:39:00 — The discussion covers choosing a cloud-based finance partner, planning for future needs, and evaluating service and support.
- 00:51:46 — The guests recommend training staff to use AI efficiently and responsibly.
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