53%
run both coding and business agents but build and govern each through entirely separate processes.
New Research · 2026
The Hidden Disconnects in Enterprise AI
Seven Tactics for Aligning Agentic Coding and Business Agents
97% of organizations are already using coding agents. New research based on 100+ tech leader surveys and 16 expert interviews reveals the 7 structural disconnects that stall enterprise AI before it pays off and how to fix them.
By the Numbers
What our research found across
100+ enterprise technology leaders
1 in 5
enterprise AI programmes have a defined process for linking agent deployments to business goals.
46%
are unsure how often AI access to their own systems is monitored.
32%
don't know how many business agents are in production.
What you'll find inside
Two agent strategies. One hidden problem.
Enterprises are investing in coding agents and business agents but treating them as two separate problems, with two separate foundations, two separate governance approaches, and duplicated risk.
This report shows what that divide is costing you, and gives both your platform engineering team and your AI innovation team seven concrete tactics to close it.
Why your coding agent investments and your business agent investments are quietly building two incompatible stacks and what it costs every time those stacks have to share data, credentials, or governance.
Why going AI-first without being API-first means your agents are inheriting every broken workflow, undocumented endpoint, and inconsistent error message that was already a problem before AI arrived.
Why most enterprise AI governance is symbolic: organisations have written policies, access controls, and approval lists in place but 46% still don't know how often their agents are accessing their own systems.
The architecture shared by enterprises with genuine production confidence: a single platform layer underneath both coding and business agents, where investment in one directly strengthens the other.
Seven concrete tactics written for both the platform engineering leader and the AI innovation leader, so both teams can use the same research to align strategy and demonstrate ROI.
Written for the people making the calls
This report is most useful if you are:
a CTO, VP Engineering, or Head of Platform
who evaluates AI agent infrastructure.
an AI or ML Lead
who is responsible for production deployments at scale.
a Senior Executive
who is being asked to sign off on AI investment without a clear view of whether coding and business agents are working together or duplicating effort.
a Technical Leader
who knows the shortcuts being taken to hit AI deadlines and needs the data to make the case for doing it properly.
About the research
Independent research.
Real data.
This report is based on survey responses from over 100 enterprise technology leaders and in-depth qualitative interviews with organisations across banking and financial services, manufacturing, SaaS, and data services — including global institutions managing billions in assets.
Research was conducted in Q1 2026. Respondents are predominantly senior practitioners with 10+ years of domain experience, the majority operating within organisations of 1,000+ employees. The research was designed and led by independent analyst Mark Boyd.
in partnership with Jentic. Where findings from specific organisations are cited, identities have been protected unless explicit permission was given.
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