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Claus Jepsen, Unit4’s chief product and technology officer, did not predict that generative AI would disappear in 2025. His December 11, 2024 forecast for BetaNews was more measured: enthusiasm would cool, privacy and intellectual-property controls would tighten, and companies would redirect attention toward practical automation, better integration and more honest delivery promises.
The four predictions are data-privacy scrutiny, the end of the generative-AI honeymoon, a shift toward an automation mindset, and tougher management of customer expectations.
The four predictions at a glance
| Prediction | Signal behind it | Enterprise implication |
|---|---|---|
| Privacy moves to the center | Organizations experimented with AI over internal text without mature governance. | AI-use transparency, risk classification and governance boards become standard requirements. |
| The generative-AI honeymoon ends | Generative AI passed Gartner’s “Peak of Inflated Expectations” in 2024 and was expected to enter the “Trough of Disillusionment” in 2025. | Leaders become more selective, especially where production code and open-source training data create intellectual-property exposure. |
| Automation becomes more practical | ChatGPT made advanced technology easier for business leaders to understand. | Interest shifts toward intuitive, connected and partly “self-driving” enterprise workflows. |
| Expectations require active management | On-demand consumer services have reset expectations for speed. | Companies must explain long, compliant implementations while delivering visible staged benefits. |
1. Data privacy becomes a board-level AI issue
Why scrutiny is increasing
Generative-AI tools can scan, summarize or transform large collections of internal material. Jepsen’s concern is that experimentation has sometimes run ahead of data governance: employees may know what a tool can do without knowing which documents may be submitted, how outputs are retained, or whether a vendor’s terms fit the company’s obligations.
The workforce itself reflects that concern. BetaNews reported that 45 percent of U.S. employees feared their company did not categorize AI applications according to the potential harm to employees and customers. That figure is attributed to the 2024 BetaNews article; it is not a universal measure of every country or industry.
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What stronger governance looks like
Jepsen expects more transparency about internal AI use and the creation of AI governance boards. Those boards would oversee responsible employee use as well as data-compliance expectations between vendors and customers.
- Maintain an inventory of approved AI applications and the information each one may process.
- Classify applications by potential impact on employees, customers and regulated data.
- Document retention, access, review and deletion responsibilities with each supplier.
- Give employees a clear route for reporting unsafe use or unexpected output.
- Review governance when a pilot moves into a customer-facing or production workflow.
This is not simply a security exercise. A company can have technically accurate output and still breach confidentiality, contractual restrictions or sector rules if the underlying data was handled improperly.
2. The generative-AI honeymoon gives way to skepticism
What “AI winter” means in this forecast
Here, an AI winter means a cooling of expectations and investment discipline, not the disappearance of the technology. Jepsen placed generative AI on Gartner’s hype-cycle path from the “Peak of Inflated Expectations” in 2024 toward the “Trough of Disillusionment” expected in 2025.
That transition changes the question from “Where can we add generative AI?” to “Which use case produces dependable value at an acceptable risk?” Fewer high-risk enterprise deployments may proceed, while experiments that cannot show reliable outcomes or a defensible control framework lose priority.
Why production code receives special caution
Jepsen specifically warned about production code trained on open-source material. Open-source availability does not automatically eliminate intellectual-property obligations: licenses can impose conditions, and generated code may require review before it enters a commercial product.
- Separate exploratory code generation from code approved for production.
- Require engineering and legal review for licensing, provenance and security concerns.
- Keep a record of tools, prompts, source material and human changes for consequential code.
- Prefer bounded pilots when the business case or ownership position is unclear.
The practical result is a narrower, more evidence-led portfolio of generative-AI projects rather than a blanket rejection of the technology.
3. Enterprise software shifts toward an automation mindset
How generative AI changes automation interest
ChatGPT’s conversational interface made advanced computing understandable to non-specialists. Business leaders could describe a desired result in ordinary language and see a system respond. Jepsen believes that familiarity will increase interest in non-generative automation: software that connects systems, removes repetitive decisions and completes routine work with less manual intervention.
Why integration and design matter more than spectacle
“Self-driving” enterprise software is useful only when it fits the organization’s actual processes. A polished interface cannot compensate for disconnected data, unclear ownership or an exception that no employee knows how to handle.
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That approach keeps people in the workflow where judgment, escalation or accountability is required. Automation should make the next action obvious, expose its assumptions and provide a safe way to intervene rather than hide decisions behind a novelty interface.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.4. Customer expectations must be managed in an instant-gratification economy
The speed gap between consumer and enterprise services
On-demand apps and same-day shipping have trained customers to expect immediate results. Enterprise work is different: a compliant financial-software implementation or a cloud migration can take months because data, controls, integrations and staff processes must change together.
Accenture was cited as finding that 95 percent of B2C and B2B executives believe customer expectations are changing faster than their businesses can change. The article does not state a publication year for that figure, so it should be read as an attributed estimate rather than a current universal benchmark.
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How to preserve trust during a long implementation
- Explain why security, compliance, data conversion and integration affect the schedule.
- Publish milestones that customers can see and understand, rather than promising one dramatic launch date.
- Deliver staged wins so users receive useful improvements before the entire program is complete.
- Use empathetic change management: acknowledge disruption, train affected teams and provide a route for feedback.
- Report delays early and describe the corrective action in concrete terms.
Transparent communication does not make a long project short. It makes the timeline credible and gives customers evidence that progress is occurring.
A decision framework for enterprise technology leaders
The four predictions can be applied to any proposed AI or automation initiative by asking six questions before approving a full rollout:
- Governance and privacy: What data will the system process, who may access it, and which body approves the use?
- Intellectual property: Could training material, generated output or integrated code create licensing or ownership exposure?
- Automation and oversight: Which steps can run automatically, and where must a person review, approve or override the result?
- Integration complexity: Which existing systems and workflows must connect for the promised outcome to work?
- Time to first value: What useful, measurable stage can be delivered before the complete implementation is finished?
- Customer communication: How will users learn what is changing, why it takes time and what they will receive at each stage?
Jepsen summarized the broader stance as “a healthy dose of AI skepticism coupled with automation pragmatism.” His advice to IT leaders was to approach emerging technology “with curiosity and mindfulness”: investigate its potential, but make governance, human needs and operational proof the conditions for adoption.
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