By:
AI & ROI – Insights provided by Ron Thomas, Chief Revenue Officer at Smartcat
Prediction 1 - Speed to market will become the most accurate indicator of AI ROI: Across the organizations we support, speed to market is the clearest test of whether AI is delivering real value. The impact does not show up in abstract efficiency metrics, but in whether teams can prepare customer-facing materials, adapt them for multiple regions, and launch on schedule. In scientific, regulatory, and technically complex environments, even small regional delays introduce downstream risk. In some cases, they can stop a launch entirely. These workflows leave no room for misalignment. Scientific nuance must remain intact, procedural accuracy must be exact, and regulatory expectations must be met in every market.
If AI does not shorten time to launch, it is not delivering ROI. Leaders are moving beyond incremental efficiency gains and asking a more direct question: Does AI help us meet critical launch windows without sacrificing precision? In 2026, speed to market will be the most practical way for executives to judge whether their AI investments are working. Global demand alone no longer guarantees success. Local relevance does. A product or therapy only truly launches when every region receives accurate, approved, and locally clear communication on time. AI matters because it can collapse complex, multi-region workflows while preserving the accuracy required for safety, compliance, and operational readiness. The organizations that can consistently shorten this path without losing precision will have the strongest evidence that their AI strategy is delivering real ROI.
Prediction 2 - AI optimism will give way to accountable commercial infrastructure: Across executive conversations, the tone around AI has shifted from optimism to accountability. Leaders are now evaluating AI with the same standards they apply to revenue systems, expansion strategy, and operating cost. This shift is most pronounced in regulated, technically precise environments, where success depends on disciplined review cycles and authoritative customer communication. In these settings, AI only creates value when its outputs withstand financial and operational scrutiny. AI that cannot withstand financial and operational scrutiny is not infrastructure; it is experimentation. AI must be tuned to how work actually happens, including workflows, approvals, language requirements, and compliance obligations, rather than to generic models.
AI also has to perform in real customer decision moments. Customers often make fast, high-stakes decisions when dealing with medical information, operational guidance, or complex product behavior. These moments shape trust and directly influence outcomes. When AI supports them well, it drives measurable commercial impact. When it does not, it becomes friction. In 2026, companies will judge AI by whether it withstands financial scrutiny, enables revenue growth, supports expansion into new markets without proportional cost increases, and shortens critical cycle times. AI is being judged less on promise and more on performance, and that shift will define how organizations invest in it.
Prediction 3 - AI will be central to expanding global reach without increasing cost: In 2026, global organizations face a hard constraint. They want to expand into more markets without growing headcount at the same pace. Traditional, linear workflows cannot support the volume and precision required. A single product update can trigger regulatory, technical, and safety changes across dozens of countries, from revising scientific explanations and technical specifications to securing local approvals before distribution can proceed. When organizations rely on manual processes to manage this complexity, costs escalate quickly as teams, vendors, and coordination layers multiply. No enterprise can staff its way into this future.
AI enables a fundamentally different operating model. It allows work that once moved sequentially to happen in parallel, preserving quality while increasing throughput. This makes it possible to launch globally in close succession or simultaneously, rather than market by market. AI is becoming core infrastructure for expanding global reach without linear cost growth. The organizations that lead will be those that use AI to reduce friction instead of adding layers, treating it as a foundational system for how global work gets done.
AI & HR – Insights provided by Stacey Richey, Global VP of People at Smartcat
Theme 1 - ROI of AI in the Business: “In 2026, a working understanding of how to use AI effectively will become a basic requirement for many roles. The organizations that see the most value from AI will be the ones that strengthen the culture and systems that guide how employees use these tools in their day-to-day work.”
Theme 2 - Readiness Gap and Leadership Responsibility: “Many companies are already seeing a gap between the speed of AI deployment and employees’ ability to use it effectively, and I expect that gap to widen next year unless leaders put the right support in place. The companies that do this well will be the ones that focus as intentionally on their people and workflows as they do on the technology itself.”
Theme 3 - Distinguishing Substance from Hype in 2026: “Much of the hype in HR tech focuses on AI replacing the function or transforming talent management overnight. The real substance in 2026 will be far more practical. HR leaders will need to balance using AI to streamline their own routine work with their broader responsibility to enable the organization. AI will help teams streamline routine work, strengthen judgment through better insights, and support more consistent decision-making across the organization. HR’s value will continue to come from empathy, culture-building, conflict resolution, and context setting.”
Concise - ~150 words
In 2026, AI’s role in the business will only continue to grow, and a working understanding of how to use these tools effectively will become a basic requirement for many roles. Employees will be expected to apply AI appropriately, recognizing where it supports their work and where its limitations require human judgment. To support that capability at scale, HR will need to integrate AI fluency into hiring, onboarding, and development. Crucially, HR teams must also model this behavior, balancing the rollout of AI strategy across the business with their own adoption of the technology.
Many organizations are already seeing a gap between the speed of AI deployment and employees’ ability to use it effectively. I expect that gap to widen next year unless companies invest in training and set clear expectations and guardrails for internal AI use.
The results companies achieve from AI will depend not just on the technology, but on the culture and systems that guide how work gets done. Leaders across the organization will be responsible for helping their teams build these capabilities, with HR providing the standards, guidance, and resources that support that growth.
Expanded - ~300 words
In 2026, AI’s role in the business will only continue to expand, and a working understanding of how to use these tools effectively will become a basic requirement for many roles. Employees will be increasingly expected to apply AI appropriately, recognizing where it should be used to support their work and where its limitations require human judgment. To support that capability at scale, HR will need to integrate AI fluency into hiring, onboarding, and ongoing development so teams can stay effective and competitive.
Many organizations are already seeing a gap between the speed of AI deployment and employees’ ability to use it effectively. I expect that gap to widen next year unless companies invest in training and establish clear expectations and guardrails for how AI is used internally.
I also expect a growing number of organizational leaders to recognize that AI’s impact on productivity depends not just on the technology itself, but also on the culture and systems that guide how work gets done. HR’s remit has broadened meaningfully in recent years, and as more routine tasks are automated, the people function will play a central role in helping organizations use AI effectively and responsibly. That includes setting expectations, strengthening decision-making skills, and giving employees the structure they need to understand when context matters and when to question AI outputs.
The companies that will see real returns on investment from AI next year will be the ones that put as much focus on their people and workflows as they do on the tools themselves.
AI’s expanding role in 2026 will raise expectations for employees to use it appropriately and with sound judgment. Not everyone will develop those skills at the same pace, which is expected. Leaders across the organization will be responsible for helping their teams build these capabilities, with HR setting the standards and providing the guidance and resources to support that growth. This foundation will be essential as organizations adjust to the speed at which AI is reshaping work.
AI & Marketing – Insights provided by Nicole DiNicola, Global VP of Marketing, Smartcat
Prediction 1 - scaling globally means coordinating complexity, not just creating more:
“In 2026, marketing teams will continue to face the pressure to ‘do more with less’, but the challenge will go beyond volume. Operational complexity is becoming the bigger obstacle. Marketers have learned how to scale volume with AI, but many haven’t figured out how to connect the systems and workflows behind it. That’s where teams still lose time, managing duplicate versions, correcting inconsistencies, or navigating disconnected tools.
Industries like CPG, retail, and life sciences will feel this most as frequent product changes and regulatory demands collide with fragmented processes. To keep pace, marketing teams must focus on solutions that coordinate complexity at scale, not just create more, faster.”
Prediction 2 - the best workflows will be global-ready by design:
“In 2026, more organizations will redesign their content operations so that multilingual and multi-market readiness is built in from the start. High-performing teams are already anticipating regional needs earlier in the process, removing the need for late-stage rewrites or rushed fixes.
This shift is especially visible in industries like life sciences, where expansion into APAC and China has raised the bar for speed and accuracy. Addressing cultural and regulatory nuances upfront reduces delays and duplication—clearing the path for faster, more reliable launches. As expansion accelerates, designing for global readiness upfront will become a competitive advantage for organizations.”
Prediction 3 - the rise of the high-AIQ marketer:
“In 2026, marketing roles will continue shifting toward system shaping, strategic judgment, and relationship-building. As automation takes on more execution, teams with high AIQ—those who can turn brand and business priorities into scalable workflows and quality standards—will lead the way.
At the same time, declining trust in paid channels and rising demand for authenticity and cultural relevance will raise expectations for localized content that feels genuine. Audiences have little patience for generic messaging, mistranslations, or content that feels detached from their market. Marketers who combine AI-enabled scale with strong judgment and cultural fluency will help their brands show up credibly and consistently in every market they serve.”
Prediction 4 - velocity and risk as core KPIs:
“In 2026, go-to-market velocity will become one of the clearest indicators of AI’s value to organizations. The pressure is especially high in manufacturing, CPG, and life sciences, where companies are entering new markets faster, refreshing product lines more often, and responding to region-specific regulatory updates on tighter timelines.
But speed alone isn’t enough, especially in industries where inaccuracies mean millions of dollars lost in regulatory fines. Marketing leaders will need to track risk alongside velocity, monitoring accuracy, compliance, and correction rates to ensure that speed doesn’t compromise quality.
Velocity shows whether teams can keep up. Risk reveals whether they’re staying in control. Together, these KPIs will shape how marketing teams measure success in an AI-powered organization.”
Prediction 5 - rise of multi-agent teams as the operating model:
“In 2026, marketing teams will shift from using AI as isolated tools to deploying coordinated systems of AI agents, each working in parallel across different parts of the workflow, from planning and creation to quality review and localization. This marks a key maturity point in how teams scale. Instead of relying on fragmented tools or manual handoffs, marketers will orchestrate multiple agents within one structured environment where each step is connected, consistent, and monitored.
As global expansion accelerates and campaigns grow more complex, the most effective teams will combine the speed and structure of AI with the judgment and context only people can bring.”
