EQengineered Team: 2027 Predictions
What will 2027 mean for the way enterprises build, adapt, and compete? We asked the EQengineered team to share their predictions, drawing on their experience helping clients navigate technology decisions and organizational change. Their perspectives offer a practical look at what may lie ahead and what leaders should be preparing for today. Underpinning this outlook is our approach to Engineering Intelligence: connecting strategy, people, engineering, and institutional knowledge to turn technological possibility into measurable business value.
2027 EQer Predictions
“In 2027, the focus will shift from generating more code with AI to reducing workflow frictions that would enable the increased use of AI in other areas outside of pure code generation. Organizations will invest heavily in integrating AI safely into workflows, processes, and tooling. Evolving best practices and guidelines will result in increased security, observability and measurably better outcomes for organizations.” Jen Parker
“In 2027, AI will drive a new era of hyper‑innovative technology, creating smarter languages and making every process dramatically more robust.” Girish Kumar
“In 2027, security is going to become the first question we ask about every change and every dependency we bring in. Supply chain attacks keep piling up, and one bad package can do a lot of damage, so it can't be something that gets weighed later.” Seth Carney
“In 2027, organizations will continue to invest heavily in AI adoption yet shift the focus from the shiny new AI capability toward human capability. Upskiliing and utilizing the transferable skills of existing employees will occur in parallel to the adoption of AI, translating to the enhanced operations of the entire organization.” Andrea Leppert
“As technology and AI continue to transform the workplace, 2027 will be a defining year for organizations to reaffirm that their people remain their greatest asset. While innovation will drive efficiency, it will be human connection, trust, creativity, and a strong sense of belonging that truly differentiate successful organizations. The role of HR will be to ensure that as businesses evolve, their commitment to investing in, developing, and valuing their people grows even stronger.” Jessica Vaughan
“I think the shift on the technology side will be toward token optimization: companies that have established workflows in place will start encouraging employees to optimize local models on their own machines and utilize API frontier models for planning/architecting solutions. I think either that will occur, or at the least we will start seeing many companies focus on optimizing token usage as opposed to their initial token maxxing phase from this last year! I suspect Anthropic and OpenAI are starting to feel the pinch of no profitability and the cost of training new models, and may start increasing plan costs soon as well, causing companies and individuals to move in this direction!” Nithin Mahesh
“In 2027, we'll see a substantial increase in operational cost with AI-assisted development and AI-driven workflows. This will result in organizations being more cautious and pragmatic over their individual usages, and overall strategies.” Chris Flanagan
“AI advances; responsible use becomes essential. The tools will keep getting better. I’m hopeful we’ll put just as much effort into using them thoughtfully and responsibly. Engineering hiring picks back up. I’m optimistic we’ll see renewed investment in people, alongside the political shift in the country.. People and AI become a stronger team. The biggest gains will come from pairing human judgment, creativity, and experience with AI’s speed and capabilities.” Sara VanDeventer
“In 2027 as enterprises begin to lean more on agent driven planning and development with fewer human checks in place before final ship, efforts towards achieving a scalable, cost effective mechanism/process of increased first pass accuracy and the reduction of review iterations by validator agents will be a high priority. The combination of frontier and local models will be a big push, along with the development of improved means for agents to share context and selectively prune context from their current window without a reset.” Ian Kerr
“In 2027 customer fatigue and perhaps a large scale breach will have customers wanting to know why they should trust an implementation of AI instead of just seeing cost benefits. Trust in our product and human processes and oversight will be as important to selling our services as the quality of our applications.” Brendan Sheehan
“In 2027, companies that don't fully embrace AI are going to need help to stay viable. As our world changes, and outside work people learn and utilize AI; the demand for immediacy of response, features and functionality will be expected from the companies consumers utilize. Context switching is going to become more and more expected, and humans will need to structure their day to stay focused and productive and prevent burn-out.” Deirdre DeVille
“I think we’re going to spend more time defining requirements clearly, creating workflows, and designing the guardrails than designing the screens. Once the AI can take actions, the design work is mostly deciding what the agent can and can’t do, and how a human stays in control when it gets something wrong. Design Workflows will become increasingly more agentic as guardrails are optimized. The upfront bottleneck gets relief through synthetic research to shape the prototypes, quicker interactions with users through prototypes, and easier understanding of legacy software through the use of AI. Context will continue to be a necessity and progressive disclosure optimizations will continue to be enhanced.” Rachael Guay
“In 2027, I expect productivity per token to keep climbing rapidly. Enterprises will get serious about building generative AI pipelines into their operations, and AI-driven breakthroughs will emerge in fields like physics and biotech.” Will Ramirez
“In 2027, AI-agent assisted cyberattacks on both modernized and outdated infrastructure (Essentials like water, power, food, healthcare, financial resources) by adversaries both foreign and domestic, will increase, and become increasingly more sophisticated, as evinced by recent events involving Hugging Face, Binance, and supermarket systems here and abroad. In the realm of UX/UI, artificial intelligence will continue to tighten and compress an already tight job market, evolve how we work, and become the key differentiator in the value we bring to the table. It'll still involve humans building software and systems for other humans, but faster, more efficiently, predictably, thoroughly, and consistently. And it'll also continue to transform the SDLC in ways we are only just beginning to re-imagine.” Dean Wong
“In 2027, AI will continue to make consumers suspicious of corporate sponsored media. Real human opinions, original research, founder trust, and verified customer experiences will become the primary competitive differentiators. It will be more important than ever for the employees of EQengineered to continue to create their own content, in their own voices, and be the trusted faces of our company.” Rachel Mezzatesta
“Data center curtailment will make 2027 the year enterprises accelerate deploying private LLMs and negotiating compute capacity directly with the infrastructure owners.” Karl Pansewicz
“2027 will be a year when organizations experiment with autonomous agents. Autonomous coding agents will build software in factories and automatically patch running applications in production environments. Custom-built agents will function on their own as specialized employees, making decisions that are part of common business processes.” Ed Lyons
“In 2027, i predict more and more innovations will occur because of AI. Also for our group I see more automation of standard practices and increased delivery speed . The key to success will be to fully test and confirm that ai results are reliable and consistent. I also hope that the level of empowerment given by ai technology does not distract us from applying due process and questioning our actions.” Anne Lewson
“Both Anthropic and OpenAI will go public in 2027. AI Regulations will take shape with AI tax. Medicine discovery will be accelerated with AI.” Yuxin Wang
“In 2027, I predict growing agent use will push teams to make sure their systems can handle more traffic and more work happening at once. Security and governance will become a bigger part of everyday engineering: app security engineering, deciding what agents can access and change, when a person needs to approve an action, and who is responsible when something goes wrong. Companies will continue to build and refine their operating models around these decisions, adjusting how people and agents work together as the tooling landscape continue to evolve.” Dakota Kim
“There will be an increase in scrutiny at the federal level that will either result in a massive spike in usage (out of fear), or a great slow down as industries navigate any coming changes. Consumer distrust will continue to increase as more flaws are identified. Organizations will become hesitant to incorporate it until rules and regulations exist. Cyber security roles will increase. Organizations may reduce staffing to account for increased AI expenditures. They may also switch to a “well if it’s so easy, we can just use AI in-house to meet our business needs.”” Rebecca Hearn
“More AI tools to choose from but all from the same few companies. Tech will advance faster than its resources and infrastructure, which favors the big companies again. Gov't will invest as security issues arise - and that leads to profits, especially if the AI companies go public, so bundles/contracts/product quality will decline while price rises (cable contracts all over again). It'll look like we have options but we won't as monopolies form. Cheaper AI alternatives will push back and get punished by big corps with gov't lobbying. More dependence on big tech everywhere with less say or safety in the terms. Limitations on use as users rebel (example: game reverse engineering).” Matt Koentjoro
“The tooling around LLMs will continue to improve in two key areas. First, there will be off the shelf software factories wrapped in a nice UI so that many more people will be able to build good software without coding knowledge. Second, purpose built locally run models will become more and more commonplace so that those same software factories will be available for those who don’t want to use cloud models.” Alex Tselevich
“In 2027, I feel the biggest changes we will see in the next year is with company’s adoption and integration of AI. Companies are trying to understand what it can do and how to use this great tool, and more and more of those answers will become apparent. Security will be a huge concern and will be used by both black and white hat developers. Tools will have to evolve to continuously monitor and adapt to these new risks in real time while data governance will be more important but easier to do with a new set of evolving tools driven by agentic development. Developers will have to focus on efficient workflows through adopting off the shelf solutions or building their own, and evolve a different set of skills to address the changing landscape.” Bill Schreiber
“In 2027, I believe we’re going to see companies move past the experimental phase and really focus on scaling up their AI tools. At the same time, because of some high-profile AI mishaps that are bound to make the headlines, everyone will be talking about how to set up proper guardrails. The big challenge for businesses will be growing their tech quickly without sacrificing security and trust.” Todd Alger
“Successful organizations will continue with rapid AI adoption and integration into their workflows. Successful engineers will integrate AI into their toolboxes and transform the way that they work. Engineers who do so will feel more empowered and productive and they and their employers will enjoy increased velocity.” Dave Flynn
“In 2027, the gap between companies that use AI well and those that don’t will come down to training and trust, not tooling. Teams that invest in teaching people how to work with AI, and in clear guardrails, will move faster with fewer surprises. Human judgment will stay the deciding factor.” Russ Harding
“Growth of secondary markets: “Authenticity" economy: People may increasingly pay a premium for things they know are real, human-created, or human-delivered. Ex. Art, photography, handmade goods, real people in customer service, Verified Products growth: Content authentication and digital provenance. Ex. Verified news and journalism; Identity verification and deepfake detection. "Offline" experience economy: Farm visits, community gardens, live music, cooking classes, etc.” Diane Yates
“As AI continues to advance, I expect the boundaries between design, product, and engineering to become increasingly blurred, with roles evolving to be more generalist. AI will make it easier and faster for teams to move from ideas to execution, reducing the time and specialized skills traditionally required to design and build features. I believe the value of design will increasingly shift toward understanding user needs, identifying the right problems to solve, and determining what is worth building in the first place.” Yujing Su
“In 2027, organizations are going to find themselves with the challenge of trying to create efficiencies while dealing with more informational noise. Focusing the message and making best use of human attention will become a central theme.” Julian Flaks
“In 2027, I expect AI to become more deeply embedded in how enterprises operate, with agents taking on broader responsibilities across business and engineering workflows. As their autonomy grows, governance will become an operational capability, built into how organizations assign authority, evaluate performance, and maintain accountability. Human judgment will remain essential to setting direction, weighing tradeoffs, and deciding when AI should act independently or require oversight. The enterprises that make the greatest progress will connect these capabilities through Engineering Intelligence, translating advances in AI into trusted, measurable business value.” Mark Hewitt
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EQengineered’s Engineering Intelligence (EI) Framework has three solution offerings to answer three key questions:
1. EI Strategy answers "What should we do?" (Envision)
Engineering Intelligence (EI) Strategy is EQengineered's executive advisory offering designed to help organizations establish a clear, measurable path toward AI-enabled enterprise modernization.
2. EI Catalyst answers "How do we prepare our people?" (Educate)
Engineering Intelligence (EI) Catalyst is EQengineered's enterprise enablement offering designed to prepare executive leadership, business stakeholders, and engineering teams to confidently adopt Engineering Intelligence across the organization.
While strategy establishes direction, Catalyst builds the organizational capability required to execute successfully.
3. EI Compass answers "How do we execute and continuously improve?" (Guide)
Engineering Intelligence (EI) Compass is EQengineered's embedded delivery offering that operationalizes Engineering Intelligence across software engineering, product development, data engineering, and enterprise modernization initiatives.
Following strategic alignment and organizational enablement, Compass places Engineering Intelligence into practice through forward-deployed engineering teams, AI-assisted software delivery, modern engineering workflows, and continuous business collaboration. Compass transforms Engineering Intelligence from organizational capability into sustained operational advantage.
Rather than delivering isolated projects, Compass establishes an Engineering Intelligence operating model that continuously improves software delivery, engineering quality, modernization velocity, and business outcomes.