There’s some news coming out from Sage this week via its annual Sage Future event in San Francisco, including:
Sage acquires Doyen AI to help SMBs migrate and go live faster with AI: Sage acquired Doyen AI, a company focused on using AI to make customer onboarding and implementation faster, simpler, and more accurate for finance teams.
- This acquisition removes a major barrier to adoption: Implementation and migration complexity are among the biggest causes of delays in finance system rollouts, often slowing or stalling transformation efforts.
- Improves outcomes for customers and partners: Faster, more accurate migrations reduce effort for customers and Sage’s partners alike, helping them go live faster and realize value sooner.
- Shows applied AI in action: The acquisition demonstrates how AI can be used in practical, mission‑critical implementation workflows, including data migration, mapping, and configuration, to reduce effort while maintaining accuracy, auditability, and control.
Integration of core finance and industry workflows in Sage Intacct: Sage Intacct enhancements integrate planning, spend management, cash flow, and industry-specific workflows into a single platform, aiming to reduce fragmentation, improve visibility, and enable faster, more confident decision-making.
- Key updates include Enhanced Sage Intacct Planning (eSIP) and stronger Sage Expense Management with AI.
- Integration with Sage HCM provides labor spend insights, alongside new receivables capabilities for predictable cash flow.
- Deepened industry-specific solutions are offered for sectors such as insurance, lending, and construction.
AI agent expansion across finance, HR and operations: Sage is embedding intelligent AI agents directly into its core finance, HR, and operations systems (Sage Intacct, HCM, X3) to automate workflows, moving businesses from analysis to direct, confident action with transparent and auditable AI.
- AI agents facilitate faster responses and enhanced operational confidence by automating tasks within existing systems.
- The Sage Intacct Finance Intelligence Agent uses natural language for task preparation, offering clear explanations and audit trails while ensuring user control.
- Sage is opening its AI platform, allowing partners to develop specialized, governed AI solutions for high-trust financial environments.
New AI tools and commercial models for developer platform: Sage has unveiled new tools and flexible commercial models to simplify the development and scaling of AI-powered solutions for partners across its Sage Intacct, X3, and Active platforms.
- A unified developer experience streamlines building and integration.
- New AI tools, including Sage Agent Builder and AI Gateway, enable partners to create integrated AI experiences.
- Flexible commercial models, such as usage-based pricing, are introduced to foster partner growth and innovation.
Sage brings core finance and industry workflows together in Sage Intacct. Sage’s latest updates are designed to bring together the core elements of modern finance in a more connected Sage Intacct experience, including:
- Enhanced Sage Intacct Planning (eSIP), available later this year, provides a more responsive and connected approach to planning.
- Sage Expense Management, now available in the US, strengthens spend control with AI-powered recognition, simplified capture and modern policy handling.
Sage is also continuing to deepen industry-specific capability across Sage Intacct, including:
- Insurance: PolicyConnect connects policy and financial data to help insurance finance teams improve forecasting, risk management and reporting alignment.
- Lending: Lending Management connects lending and finance workflows to reduce errors, simplify audits and improve visibility into performance and risk.
- Product-centric industries: Operations for Sage Intacct helps distributors and manufacturers gain better visibility across inventory, sales and operations.
- Construction and real estate: Sage continues to expand connected workflows that help teams reduce manual work and manage project performance more effectively.
These integrated advancements in AI, platform unification, and partner empowerment solidify Sage’s vision to drive efficiency, insight, and confidence within the financial suite.
Sage and PwC commit to tackling AI trust gap in finance
Posted in Commentary with tags Sage on April 30, 2026 by itnerdSage today announced a new initiative in partnership with PwC, which will redefine how AI is built and adopted in finance, combining transparent, explainable AI with the governance and real-world expertise required to use it with confidence.
The initiative, “Beyond the Black Box”, was announced at Sage Future, and is backed by new research from Sage, conducted by IDC, showing that more than seventy percent of finance leaders (71%) would reject an AI system if it cannot explain its outputs, even if they are highly accurate, showing that trust, not technology, is holding back AI adoption.
Unlike previous AI initiatives that have focused on large enterprises or purely technical audiences, “Beyond the Black Box” was created with SMB realities at its core. It forms part of Sage’s commitment to helping more SMBs benefit from the transformative impact of AI, building upon the company’s Responsible AI framework and AI Trust Label, reinforcing the belief that trust must be built into AI from the outset.
Trust, not technology capability, is the biggest barrier to AI adoption in finance
As AI becomes more capable, the ability to explain and stand behind its outputs is emerging as the defining factor in whether it is trusted and adopted in finance.
The consequences are already measurable. Finance professionals are spending an average of 12.9 hours every week reconstructing, validating and defending AI outputs. Much of this work stems from the need to validate and explain outputs that do not clearly show how they were produced. Rather than removing overhead, opaque AI is creating a new category of it.
Sage describes this as the trust cost of AI – the gap between what AI systems promise in theory and what finance teams can actually rely on in practice. At its core, this is a transparency challenge. Every number, recommendation and AI-supported decision must be explainable to auditors, to boards, and to regulators. When it cannot be, adoption stalls.
From black box AI to glass box
Sage has designed its AI from the ground up for the realities of finance, where every output is transparent, explainable and accountable, so organizations can trust and act on it with confidence.
This represents a deliberate shift away from black box AI, where outputs are generated without visibility into how decisions are made, towards what Sage describes as glass box AI: customers can meaningfully interact with AI results – not blind faith. Every answer is explainable, every recommendation is verifiable, and every output can be interrogated.
Through the initiative, Sage and PwC will combine their expertise into practical tools and frameworks to help finance teams understand, assess, and adopt AI responsibly. This includes embedding trust into how AI is implemented in finance environments while building on Sage’s existing commitment to SMBs, including the Sage AI Academy, which supports organizations with the knowledge and guidance needed to adopt AI with confidence.
From pilot to practice
To help move organizations from AI experimentation to trusted, scalable adoption, Sage selected PwC as its lead partner, drawn by PwC’s proven expertise in deploying AI across its own business. PwC has embedded AI into day-to-day workflows at scale, with 86% of its employees actively using AI tools, more than 240,000 Microsoft Copilot licences deployed, and over 4,000 custom GPTs developed and reused across the firm.
Businesses are increasingly concerned about the probabilistic nature of AI systems, particularly the lack of transparency, explainability, and clear accountability behind AI-generated outputs. Together, Sage and PwC will build transparent AI that gives finance teams control and full visibility into its outputs, backed by the implementation expertise, governance frameworks, and risk management capabilities required to put that AI to work safely, effectively, and at scale.
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