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Content as a Service v3 - telecommunications - Tuesday, July 15, 2025 at 13:13
Build an Operating Model for Repeatable AI in Telecommunications
Telecommunications providers are advancing AI across customer engagement, enterprise growth, service, and network operations. Yet 60% of respondents remain at the Piloting or Deploying stages, showing the need for an operating model that can turn individual initiatives into repeatable execution.
This report examines the ownership, measurement, cross-functional collaboration, learning practices, and internal champions associated with greater AI maturity. It also outlines five actions executives can take to support responsible deployment across customer, commercial, technology, network, and oversight functions.
Foreword
Telecommunications providers are applying artificial intelligence (AI) across customer engagement, enterprise growth, service, and network operations. The opportunity extends beyond automating tasks. They realize it depends on whether the operating model around deployment evolves at the same pace.
This work crosses organizational boundaries. Consumer, enterprise, and marketing teams shape propositions, communications, and customer relationships. Customer care manages service interactions, while network and technology functions provide the systems behind them. Data, finance, privacy, legal, security, risk, and governance partners also influence what can move forward. When priorities remain separate, AI initiatives can be difficult to connect and extend.
Responsible deployment is especially important in an industry built on trusted communications, sensitive customer information, and reliable infrastructure. Clear ownership, shared decision rights, early measurement, and coordinated working practices can help teams address commercial objectives, customer outcomes, technical requirements, and regulatory obligations together. Governance supports repeatability by clarifying expectations before deployment.
This report compares the telecommunications picture with the global cross-industry Leader benchmark. It identifies five operating model behaviors associated with mature AI deployment and translates them into actions for executives pursuing responsible, repeatable execution across consumer, enterprise, customer care, and network operations.
Gaurav Mallick
About the Research
This report draws on global research conducted by Incisiv on behalf of Adobe.
MARKETS COVERED
Australia and New Zealand, Central Europe, India, Japan, Latin America, the Middle East, North America, Southeast Asia, South Korea, the United Kingdom and Ireland, and Western Europe.
How Leaders Were Identified and Scored
The 342 Leaders come from the broader cross-industry sample of 5,633 executives across 12 industries. They are a global cross-industry cohort, not a telecommunications-specific group, and provide the benchmark for the reported telecommunications findings.
How Maturity Was Measured
Each respondent’s organization was assessed across six operating model pillars, scored from 1 to 5. The pillar scores were combined into an Operating Model Maturity Composite.
Structure
Dedicated AI ownership with budget authority.
People
Roles and culture evolving for an AI-enabled environment.
Governance
Risk framework calibrated for AI velocity.
Technology
Infrastructure built for scale, not just pilots.
Workflows
AI embedded into how work is actually done.
How Leaders Were Identified
To qualify as a Leader, a respondent’s organization had to meet both criteria — an Operating Model Maturity Composite above 3.27 and operation at the Expanding or Transforming stages of AI deployment. Applying both criteria identified 342 Leaders, representing 6% of the 5,323 active respondents.
Unless otherwise stated, “Leaders” refers to the global cross-industry cohort of 342 respondents whose organizations met both Leader criteria — an Operating Model Maturity Composite above 3.27 and operation at the Expanding or Transforming stages. They represent 6% of the 5,323 active respondents and are not a telecommunications-specific cohort.
Executive Summary
AI activity extends beyond exploration across the telecommunications respondent base, but repeatable execution remains limited. The findings point to an operating model challenge — responsibility is distributed, cross-functional relationships remain sequential, and measurement is rarely comprehensive. These conditions may make it harder to connect customer and network priorities, evaluate value, and extend successful approaches across functions.
The Industry Picture
of telecommunications respondents are concentrated at the Piloting or Deploying stages.
This is the largest concentration across the five stages, indicating that respondents have progressed beyond exploration without reaching Expanding or Transforming. The result does not show a stall or a scaling threshold. It highlights the need for ownership, governance, measurement, and working relationships that support repeatable deployment. Moving further requires a consistent way to assess value, coordinate oversight, share learning, and decide which approaches should extend across consumer, enterprise, customer care, and network operations.
of telecommunications respondents report a formal AI owner with a defined title and budget authority.
Elsewhere, responsibility may be part-time, shared, project-specific, or absent. Ownership also requires decision rights when initiatives involve consumer, enterprise, customer care, data, technology, network operations, finance, privacy, security, regulatory, risk, or governance partners. Without authority, accountability may not resolve competing priorities.
of telecommunications respondents describe the relevant business and information technology relationship as Siloed, Transactional, or Consultative.
These models can depend on sequential requests and reviews. AI deployment still requires security, privacy, regulatory, and operational oversight, but earlier collaboration helps teams consider customer value, feasibility, data use, integration, and measurement together. Shared accountability can reduce avoidable rework while preserving review and control.
What Leaders do Differently
The 342 Leaders form a global cross-industry cohort, not a telecommunications-specific group. Leaders are structurally different. They score 60% higher than the industry on organization structure and 46% higher on workflow transformation. The gap isn’t technological, it’s organizational.
These five behaviors are more common among Leaders and provide a blueprint for repeatable deployment.
Leaders are six times more likely to report a formal AI owner with a title and budget authority. Effective ownership includes decision rights and a mandate reaching the customer, commercial, technology, network, and oversight functions involved.
44% of Leaders report comprehensive measurement, compared with 11% of telecommunications respondents. Leaders establish outcomes and measurement approaches early, supporting performance evaluation and future investment decisions.
80% of Leaders report Collaborative or Integrated relationships, compared with 24% of telecommunications respondents. These models involve relevant functions in definition, delivery, governance, and measurement instead of relying primarily on handoffs.
37% of telecommunications respondents report destructive failure patterns, compared with under 1% of Leaders. Constructive review helps teams examine underperformance, preserve learning, and apply it to subsequent work without weakening accountability.
42% of Leaders identify finding and empowering internal champions as their most impactful culture action. With support, appropriate authority, and clear guardrails, champions can help colleagues evaluate and extend proven practices.
These behaviors reinforce one another. The report examines the telecommunications findings before considering how the global cross-industry benchmark can inform responsible, repeatable AI deployment.
Part One
The Industry Picture
Finding 01: The Frozen Middle — 60% of Telecommunications Respondents are Piloting or Deploying AI
AI activity has moved beyond exploration for most respondents, but only 20% have reached the Expanding or Transforming stages associated with broader deployment.
Telecommunications providers operate across consumer, enterprise, customer care, technology, and network environments, where AI initiatives can involve systems, data, objectives, and decision paths. Against that backdrop, the stage distribution matters — 60% of respondents are Piloting or Deploying, while only 20% have reached Expanding or Transforming.
The Frozen Middle captures the gap between having AI in use and extending it across functions. It does not mean respondents lack activity or have stopped progressing. It describes the stages where pilots are tested, refined, integrated, and prepared for deployment.
Moving beyond the Frozen Middle means turning individual pilots and deployments into repeatable execution. That requires an operating model that can connect decisions, data, systems, and measurement across functions. The findings that follow examine these conditions separately, beginning with whether responsibility for AI comes with the authority and resources needed to act. Together, they show what makes coordinated deployment easier or harder to sustain.
Finding 02: The Ownership Vacuum — Only 15% of Telecommunications Respondents Report a Formal AI Owner
Most respondents rely on part-time, committee-led, or project-specific ownership, leaving formal accountability with title and budget authority relatively uncommon.
Formal accountability remains the exception, while most respondents rely on arrangements that divide responsibility across roles or groups. In a telecommunications provider, that can leave customer, commercial, technology, network, and oversight teams coordinating decisions without one role able to set priorities, commit resources, or resolve conflicts across those functional boundaries.
15% of respondents have a formal owner with title and budget authority. Part-time ownership is most common at 32%, followed by committee ownership at 23%, ad hoc ownership at 21%, and nobody accountable at 8%. Most respondents rely on arrangements that distribute or limit accountability.
A formal owner does not need to replace functional leadership or specialist governance. The role needs a mandate across customer, commercial, technology, network, and oversight functions. Where that role sits will vary by provider. What matters is whether the owner has the decision rights, budget authority, and reach to resolve competing priorities and move approved initiatives forward.
Finding 03: The Approval Gauntlet — On Average, 16 of Every 100 Proposed Telecommunications AI Initiatives Reach Production
Integration has the highest stage-specific attrition rate — the mean falls from 49 initiatives after security review to 31 after integration assessment, an implied reduction of 37%.
Moving an AI idea from proposal to production requires decisions across business and its technology, data, network, security, finance, and risk partners. The survey follows proposals through information technology evaluation, security review, integration assessment, budget approval, and production launch. Although the largest numerical decline occurs at the first gate, integration has the highest attrition rate relative to the initiatives entering that stage.
Integration review may consider system compatibility, data access and governance, privacy, security, regulatory requirements, cost, vendor dependencies, and deployment readiness. For telecommunications providers, initiatives may need to connect customer care, billing, service, and network environments. Each assessment helps determine whether an initiative is practical and responsible to deploy while preserving sight of the customer, commercial, or operational outcome.
Establishing data rights, system ownership, governance requirements, and integration needs during ideation gives business and technology partners a clearer basis for evaluating feasibility and value together. It does not guarantee that a proposal will advance, but it can surface constraints before formal review and reduce the risk of discovering fundamental integration issues late in the process.
Finding 04: Business-IT Handoff — 76% of Telecommunications Respondents Operate Through Handoff or Advisory Relationships
The dominant relationship models support requests, delivery, and consultation, but stop short of the shared accountability built into Collaborative and Integrated ways of working.
Telecommunications AI depends on customer platforms, business support systems, network data, care environments, and technology teams that connect them. Business teams may define the business objective, while information technology evaluates architecture, access, integration, security, and delivery. The relationship between those functions shapes how decisions move with the work.
Siloed, Transactional, and Consultative relationships provide different levels of interaction, but none establishes joint accountability. Separate teams, request queues, and advisory involvement can support established delivery processes. They become more limiting when AI requires business and technology teams to refine requirements, resolve data or integration questions, and adjust measurement during implementation.
Collaborative and Integrated relationships bring both sides into the work when objectives, data needs, controls, and measures are defined. For providers, this can connect customer, commercial, care, technology, and network perspectives before choices become difficult to change. Shared accountability does not remove functional ownership or independent review. It gives teams common outcomes and a clearer process for resolving issues without repeated handoffs.
Finding 05: The Measurement Gap — Only 11% of Telecommunications Respondents Measure AI Returns Comprehensively
Most respondents rely on partial metrics, leaving portfolio-level tracking and full attribution uncommon.
Telecommunications providers generate extensive customer, service, and network data, but data availability does not automatically show AI value. Basic measures can show whether a model ran, an interaction was completed, or a process became faster. They do not establish whether the initiative improved retention, service resolution, customer experience, network performance, cost, or another intended outcome.
Stronger measurement begins before deployment. Teams define the intended outcome, record a baseline, and agree how performance will be assessed. Once an initiative changes an offer, care interaction, workflow, or network decision, pricing moves and service conditions can make its contribution harder to separate. The measures and attribution approach need to reflect the use case and operating context.
Pre-deployment measurement gives leaders a basis for investment, oversight, learning, and expansion decisions. It also helps customer, commercial, care, technology, and network teams distinguish AI activity from the value the initiative was intended to create.
Part Two
What Leaders Do Differently
Leaders are structurally different. They score 60% higher than the industry on organization structure and 46% higher on workflow transformation. The gap isn’t technological, it’s organizational.
These five behaviors are more common among Leaders and provide a blueprint for repeatable deployment.
01: Real Ownership — Leaders are 6 Times More Likely to Have a Formal AI Owner
Real Ownership combines a defined title and budget authority with the decision rights needed to work across customer, commercial, technology, network, and oversight functions.
Leader vs. Industry
Leaders are six times more likely to have a formal Al owner with title and budget authority. In telecommunications, 15% of organizations have named one. Leaders also operate business-IT collaboratively 3.4 times more often and measure ROI comprehensively four times more often.
AI decisions in telecommunications can span consumer, enterprise, customer care, technology, and network operations. Part-time owners, committees, and leads may coordinate work across those boundaries, but coordination does not provide authority. When priorities conflict, someone must be able to make decisions, allocate resources, and hold the functions accountable for delivery.
Formal ownership is more common among the global cross-industry Leaders. The role combines a defined title with budget authority, creating a point of accountability when work crosses functions. The chart also shows that collaborative business and information technology relationships and comprehensive measurement are more common among Leaders. These results indicate operating model behaviors, but they do not prove that ownership caused deployment maturity.
For providers, the accountable owner may sit within customer, commercial, technology, network, or transformation leadership. The location matters less than the mandate. The role needs reach to resolve priorities while preserving governance, security, privacy, regulatory, and operational responsibilities. Existing lines can remain if decision and resource authority are explicit.
02: Day-One Measurement — 44% of Leaders Use a Comprehensive Return on Investment Framework
Leaders establish intended outcomes, baselines, and attribution methods early enough for measurement to inform deployment and investment decisions.
Day-One Measurement treats evidence as part of initiative design from the outset. The Leader comparison shows that comprehensive measurement is more established in the cross-industry cohort. It does not prove that measurement alone produced deployment maturity, but it demonstrates that mature execution is associated with a clearer framework for evaluating value.
For telecommunications providers, the intended outcome may be customer, commercial, service, network, workforce, or operational. Teams need to define that outcome, record a baseline, and agree how performance and attribution will be assessed before implementation. Customer, commercial, care, data, technology, network, finance, regulatory, and oversight partners should align on the evidence needed for the specific initiative.
Timing matters because baselines are harder to reconstruct after an offer, workflow, service interaction, or network decision changes. Pricing, service conditions, and operational shifts may affect results. Measuring outcomes before and after deployment gives leaders a credible basis for evaluation, learning, oversight, and investment while avoiding claims that cannot be attributed to AI.
03: Genuine Partnership — 80% of Leaders Operate With Joint Accountability
Genuine Partnership brings business and technology teams into the work when objectives, data requirements, controls, and measures are defined.
Telecommunications AI can depend on platforms, business support systems, care environments, network data, and technology teams that connect them. A sequential handoff becomes limiting when information, integration support, controls, or measurement must move between functions. Consultation can improve a request, but it does not make both sides accountable for the outcome.
Partnership starts when business and technology teams define work. They agree on customer, commercial, service, or operational outcomes and share responsibility for data, integration, controls, delivery, and measurement. This model is more common among Leaders, but the association does not establish causation.
For telecommunications providers, joint accountability can connect consumer, enterprise, care, technology, and network perspectives before the brief is fixed. Shared objectives give teams a basis for resolving trade-offs as implementation develops. This does not remove functional ownership, technical standards, security review, privacy obligations, or regulatory oversight. It allows those responsibilities to inform the work without requiring a reorganization.
04: Learning Culture — Under 1% of Leaders Report Destructive Failure Patterns
Constructive learning requires teams to examine underperformance, correct risks, and share lessons without reducing accountability for customer, operational, or regulatory outcomes.
Telecommunications providers operate services and infrastructure where AI underperformance can affect customers, operations, trust, and regulatory obligations. That makes review essential. It can also make teams reluctant to discuss problems openly. When responsibility turns into blame or a failed approach is repeated without examination, the organization loses information that could improve the next deployment.
A Learning Culture does not reduce accountability or treat failure as acceptable. It creates a structured way to identify what happened, correct risks, examine contributing decisions and conditions, and share the lesson. The Leader comparison shows that destructive responses are far less common among Leaders, but it does not prove that learning culture caused maturity.
For telecommunications leaders, this practice can sit within operating and governance reviews. Teams can examine whether data, assumptions, controls, integration decisions, or handoffs contributed to underperformance, then document what should change. Sharing lessons across consumer, enterprise, customer care, technology, and network functions helps prevent repeated issues while preserving responsibility for customer, operational, and regulatory outcomes.
05: Empowered Champions — 42% of Leaders Identify Champions as Their Most Impactful Culture Action
Champions make adoption tangible by translating AI into familiar work while preserving the operational context, human judgment, and governance required for responsible use.
AI adoption becomes tangible when practitioners see how it applies within customer, commercial, care, technology, or network work. Colleagues who understand the context can explain where an approach adds value, where human judgment remains essential, and which data, security, regulatory, or implementation constraints must be addressed before the practice extends.
Among the cross-industry Leaders, empowering internal champions is the most selected culture action shown in the chart. This places practitioner-led adoption ahead of executive sponsorship and formal training, but it does not make them substitutes for one another. It also does not establish that champions alone caused adoption or deployment maturity.
Champions need more than recognition. They need time, support, authority, and governance to document results and help others evaluate whether it can be adapted. Data, technology, privacy, security, regulatory, and governance teams can provide standards and oversight, while champions preserve customer, service, and operational context. That turns experience into evidence for others without presenting an application as a solution.
5 Actions That Build the Telecommunications Operating Model
These actions translate the Leader behaviors into steps for telecommunications executives. They build on customer, commercial, care, data, technology, network, operations, and governance capabilities and can begin within existing reporting lines.
Name One Accountable AI Owner With Authority Across Functions
Appoint an accountable executive with a mandate, defined decision rights, and budget authority across the functions involved in AI delivery and oversight. The role may sit within customer, commercial, digital, data, technology, network, operations, or transformation leadership, depending on the provider. Existing reporting lines can remain, but the owner must be able to resolve competing priorities, allocate resources, and convene finance, privacy, legal, security, regulatory, risk, and governance partners when decisions cross boundaries.
Define the Intended Outcome Before Deployment
Define the customer, commercial, service, network, workforce, or operational outcome before an AI initiative moves forward. Record a baseline and agree how performance and attribution will be assessed. Care, data, technology, network, finance, and oversight partners should align on the evidence before deployment. Measures should reflect the initiative, not activity, adoption, or efficiency indicators that do not establish value.
Establish Shared Accountability From the Outset
Bring the functions responsible for value, delivery, and oversight into the work while the initiative is being defined. Customer, commercial, care, data, technology, network, finance, privacy, security, regulatory, risk, and governance partners should agree on responsibilities, decision paths, evidence requirements, and measures around the same outcomes. Shared accountability should complement independent review, not replace it. Earlier coordination can help teams identify data, integration, control, and funding constraints before they become late-stage approval problems.
Turn Underperformance Into Structured Learning
Create a structured process for examining underperformance, correcting immediate risks, and sharing lessons. The accountable AI owner and relevant customer, commercial, care, technology, network, and governance leaders should make this a standing part of operating reviews. Document what happened, which assumptions, controls, or handoffs contributed, what changed, and who needs the learning. The purpose is not to normalize preventable errors. It is to improve decisions, controls, and future deployments while preserving responsibility for customer, operational, and regulatory outcomes.
Give Proven Practitioners the Support to Lead
Identify practitioners who have demonstrated responsible AI applications within customer, commercial, care, or network work. Give them recognition, time, authority, and support to document the approach and help another team evaluate whether it can be adapted. Data, technology, privacy, security, regulatory, and governance teams should provide standards and oversight. Champions can extend proven practices when they explain limitations, preserve context, and work within human-judgment and accountability requirements.
Conclusion
Turn AI Readiness Into Repeatable Execution
AI is changing how telecommunications providers engage customers, support enterprises, deliver care, and manage network operations. Capturing that potential takes more than technology. It requires trusted data, connected systems, clear governance, and an operating model that links innovation with customer, commercial, operational, and regulatory priorities.
The research shows a telecommunications respondent base concentrated in the Frozen Middle, with formal ownership uncommon, most business and information technology relationships stopping short of joint accountability, and comprehensive measurement limited. The Approval Gauntlet shows how sharply the field narrows before production. The global cross-industry benchmark indicates that accountable ownership, early measurement, genuine partnership, constructive learning, and empowered champions are more common among Leaders.
The next step is to apply those principles to one priority initiative. Define the outcome, name the accountable owner, record a baseline, and involve customer, commercial, care, technology, network, and oversight partners before implementation. That creates a basis for deciding what should advance, what needs to change, and where deployment can create sustainable value.
About Adobe
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About Incisiv
Incisiv is an industry insights and strategy firm that combines close to a decade of industry trend data with primary research, messaging architecture, and content development to help enterprises navigate transformation, evaluate solutions, and unlock growth. Visit incisiv.com to know more.
Title
Move Telecom AI into repeatable execution.
cardDate
2026-10-19
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CardDescription
See five actions telecommunications leaders can take to strengthen ownership, measurement, collaboration, learning, and adoption.
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caas:content-type/report, caas:industry/telecommunications