Actual Speak on Productiveness, AI, and What’s Working in IT
As a part of our Two Truths and AI collection, Grammarly hosted an intimate gathering of IT leaders for a candid dialog on productiveness, AI, and the way forward for work. Led by Dean Macris, CISO at Dispel, and Luke Behnke, Vice President of Enterprise Product Administration at Grammarly, the dialog explored considered one of right this moment’s most urgent questions: how will we thoughtfully and successfully undertake AI instruments to drive actual productiveness, ship significant outcomes, and empower the individuals who energy our organizations?
As AI and digital instruments quickly multiply, it’s clear that organizations are feeling the stress to maintain up whereas sustaining safety, belief, and a way of id for his or her groups. This frank dialog dug into the messy center of this transition, providing sensible insights from leaders within the thick of it.
1. Begin with individuals, not instruments
“You probably have a instrument, and also you’re making an attempt to measure enhance individuals’s productiveness, begin with individuals.”
As organizations combine AI into their tech stacks, leaders should perceive how new instruments match into actual workflows and worker conduct. AI adoption could start as a technical initiative, however making it profitable is a uniquely human endeavor. It requires organizational buy-in, intuitive instruments, and a willingness from groups to develop and adapt. Leaders want to think about staff dynamics, how staff members work finest, and the place bottlenecks or frustrations exist right this moment.
IT leaders supply a singular perspective on individuals, course of, and expertise that may assist their organizations meaningfully combine AI to streamline low-level, time-consuming duties whereas amplifying human impression on higher-order duties. A part of the problem organizations face on their path to AI adoption is having a transparent understanding of what individuals and AI every do finest. When there may be misalignment, groups run the danger of outsourcing the mistaken work to AI. This is the reason IT management’s position doesn’t finish at implementation—it should lengthen to making sure that AI enhances core priorities whereas aligning with the foundational behaviors of the workforce.
2. Not every part that counts may be simply counted
“Issues which can be simple to measure are sometimes not vital, and issues which can be vital are normally not simple to measure.”
In relation to productiveness, corporations typically fall into the entice of monitoring what’s best—mouse motion, login time, or e-mail response charges—whereas ignoring deeper, extra priceless indicators like impression, innovation, and worker engagement. This has spawned a brand new office phenomenon Grammarly has coined “performative productiveness” in its newest annual report. That is the place staff spend time on actions that look like productive however don’t drive significant outcomes.
As a substitute of defaulting to shallow measures of success, organizations must outline the metrics that matter to their enterprise. Not like performative metrics, these will sound extra like price discount, buyer engagement, or worker expertise. These typically can’t be measured with a single knowledge level however relatively require a mix of metrics that collectively join the dots throughout worker productiveness, high quality of output, and enterprise outcomes.
Learn extra: The CIO Playbook: Measuring the ROI of AI
3. Shadow AI—with guardrails—drives sign within the noise
“We’re seeing instruments emerge from the bottom up… Shadow AI provides us perception into what’s really working for individuals.”
The rise of “shadow AI”—instruments that staff convey into their very own workflows with out formal IT approval—can really feel dangerous, however it’s additionally a sign. The AI instruments staff use—typically paid for out of their very own pockets—reveal which options are intuitive, useful, and meet actual wants. Quite than shutting these down outright, leaders can research them, be taught from utilization patterns, and contemplate how they could inform a extra coherent AI technique with applicable guardrails. This bottom-up experimentation can assist organizations higher perceive which instruments and use instances are driving worth.
Dive deeper: Making Sense of 2025 AI Traits Webinar (Bounce to the 17:00 mark!)
4. ROI have to be a two-way road
“Distributors want to supply extra clear metrics… In any other case, how can we defend the spend?”
When organizations put money into AI instruments, they want proof of impression—higher workflows, clearer communication, time saved, and so forth. At the moment, the client bears many of the onus for proving the return on their AI investments. It is vital for every group to design its personal AI measurement framework to align impression with predetermined enterprise outcomes; nonetheless, corporations must also maintain their distributors to a better customary for offering mechanisms to measure the impression of AI instruments. With out clear AI benchmarks, it’s troublesome for IT leaders to advocate for budgets or strategic enlargement. The decision is obvious: distributors should step up and share actionable metrics, or danger being deprioritized in a crowded instrument panorama.
5. Creativeness is the following aggressive edge
“We have to transfer from a knowledge-based financial system to an imagination-based one.”
The way forward for work isn’t nearly understanding issues—it’s about dreaming up higher methods to do them. AI can (and will) unencumber time and psychological vitality, enabling staff to give attention to significant work and envision the improvements that can remodel their organizations. When persons are passionate and empowered, they produce higher work—and that’s the true endgame for productiveness. The shift towards imagination-led work means investing in instruments and processes that improve creativity, cut back cognitive overload, and empower staff to suppose boldly about what’s attainable.
This dialog reminded us that we’re nonetheless early within the AI adoption curve, however we’re studying quick. The promise of AI isn’t simply effectivity—it’s inspiration, empowerment, and evolution. By specializing in individuals, grounding our methods in considerate metrics, and embracing experimentation, we are able to create workplaces that not solely enhance productiveness but additionally elevate worker expertise, buyer worth, and long-term enterprise success.
