# IT Leaders Share Practices That Improve Technology Adoption - Published: 2026-10-26 - Author: [CIO Grid](https://ciogrid.com/author/ciogrid-com.md) - Topics: [Q&A](https://ciogrid.com/qa.md) - Source: https://ciogrid.com/qa/it-leaders-share-practices-that-improve-technology-adoption/ - Publisher: CIO Grid ### Let Floor Teams Select Initial Deployment The action that helped most was letting the people closest to the work choose where the new tool goes first. When we introduced new software around our robots and fulfillment, I did not roll it out to everyone. We picked one recurring pain point, a task people already disliked, and ran the new tool next to the old process for a couple of weeks. That protected essential work because nothing was switched off until the team trusted the new way. It also meant the first success story came from a peer on the floor, not from me. People adopt what a teammate shows them working on a real order far faster than what leadership announces. The rule I use now is simple: never replace a process in the busiest week of the month, and always keep the old path available until the team says they no longer need it. --- ### Eliminate Old Templates for Core Duties The one action that got employees to adopt it was taking the old way away, so there was nothing to fall back on. In one AI implementation I ran, training alone did not fix a dip in adoption, so we made the AI tool the only sanctioned way to do three specific tasks and pulled the old templates off the shared drive. Essential work kept moving because those three tasks were narrow and already well understood, so nobody had to relearn their whole job at once. Adoption went from 38 percent to 82 percent in nine days, once the old way simply was not there to fall back on. --- ### Review Exceptions Daily to Build Trust The dangerous moment in implementation is not launch day. It is the first operational exception, when an employee encounters a situation the new process did not anticipate and quietly returns to the old method. One exception can become a parallel system before leadership notices it. We made adoption stronger by establishing a daily exception review during the first month. Employees could flag a broken handoff, unclear field, missing permission, or customer-impacting issue without needing permission to challenge the design. Each issue received a visible decision, either fix it, train it, or explain why it remained. That created trust because staff could see that feedback changed the operating model, rather than disappearing into a support queue. --- ### Link Software Use to Performance Pay The action that made technology adoption work in my NBN telecom company was tying the new software directly to how my crews got paid. We built custom software where field workers captured their completed work on site and an office admin reviewed it. Paper sheets and phone calls were how things had always been done, and if I had simply announced the change, people would have kept doing both and resented the extra step. Instead, pay moved to a legal minimum base plus uncapped performance pay, and only work recorded and approved in the system counted toward it. Adoption became the easiest choice instead of a burden, because the tool made people money. Some of our people went on to earn around $200,000 a year. Our office admin reviewed every entry, so crews got quick feedback when something was captured wrong. My advice is to find the one thing employees already care about, connect the new tool to it, and let the people who adopt it first teach everyone else on the job. --- ### Preserve Quality Checks for AI Work We made the new technology prove itself inside the checks people already trusted. When we brought AI into our SEO and ads work, the reasonable worry was that it writes confidently and is sometimes wrong. Asking people to simply trust it would have failed. The action that made adoption work was keeping the safety net exactly where it was. Every AI-assisted task still starts with a written checklist of what done looks like, and it still gets checked against that list by someone who did not build it before a client sees anything. The checklist did not change. Only who produced the first draft changed, so nobody had to choose between the new tool and the standards they relied on. We also kept a running list of the specific mistakes the tool made, such as suggesting sources that turned out not to exist, with the rule that now prevents each one. That list turned a vague distrust into concrete rules. People can use the tool every day because they know which of its habits to watch for, and essential client work never had to pause while everyone learned. --- ### Assign a Pilot Mentor and Log Friction I run new technology in parallel with the old process for a short fixed window so essential work never stops. I pick one low risk workflow, keep the existing method fully live, and let the team operate both until the new method proves faster or cleaner. One reliable team member is assigned to provide direct support, answer questions as they arise, and log friction points daily. That team member runs a fifteen minute standup three times a week only on the pilot workflow. Then we expand once error rates drop. Essential client or revenue work continues on the familiar path until the team itself asks to switch fully. --- ### Have Early Users Create Practical Guides I run my rollouts through what I'd call a parallel track. My team keeps doing their current workflow while a small group tests the new tool on a live but lower-stakes process. That way nobody's daily output is at risk, and the small group becomes an internal reference point when it's time to bring everyone else on. The one action that moved the needle most for us was letting the early testers co-build the training materials. When my operations team was moving to a new inventory and order management setup, the people who tested it first wrote the how-to docs in their own language, with screenshots from our actual catalog data. Those guides landed differently than vendor-supplied PDFs because they addressed the exact friction points our team hits on a Tuesday morning, not generic scenarios. Adoption picked up faster because new users could ping someone on their own floor who'd already solved the same problem. The peer credibility removed most of the hesitation, and because the testers had shaped the documentation, they felt ownership over the process rather than feeling like something was handed down to them. --- ### Mobilize Trusted Super Users Early Successful technology implementation necessitates a gradual and cautious approach that mitigates operational risks while simultaneously creating social proof via peer gurus. Installing ERP systems in manufacturing plants and distribution centers in a big bang manner often results in disastrous downtimes because it creates undue pressure on the employees to adopt complex new work processes in the midst of live production. To handle this situation efficiently, I like to implement a strategy whereby only one functional area of the operation is selected to act as a testing ground; for example, a single storage facility in the warehouse or one production line. This ultimately allows one to carry out adjustments and fixes to make the rest of the operations run smoothly without interruptions. By far, the best thing to do to obtain the buy-in from employees is to find and mobilize internal change agents or Super Users among employees rather than in the management. During my work I always try to find out who is the man on the shop floor or in the back office whose advice is sought by people in the office or in the shop. When those influential employees are included into the User Acceptance Testing well ahead of the official launch date they become most credible promoters of the changes. When ordinary employees have some issue to deal with during their work they are much more likely to listen to their colleague than to IT engineer or director working for the company. --- ### Appoint Cross-Functional Tech Champions We treat new tools as operational changes, not one-off projects: small, staged rollouts tied to daily tasks and a clear fallback plan keep essential work running while teams learn. We align deployments with customer-facing cycles so order preparation, shipping and customer service never lose coverage. The action that helped most was appointing cross-functional tech champions who learned the tool first, created short how-to workflows, and coached colleagues during live shifts. That peer-led, hands-on support made adoption faster and avoided interruptions to core operations. --- ### Keep Human Service Amid New Systems My approach is to introduce technology around the service customers already value, rather than redesigning everything at once. At Mills Shelving, we've made it easier for retailers to request assistance through a dedicated online callback form while retaining direct phone support. The technology changes how customers initiate enquiries, but the essential work remains personal: understanding their store layout, checking shelving compatibility and recommending suitable products. The important lesson is to preserve what employees already do well while simplifying the steps around it. Technology adoption becomes more practical when it removes unnecessary friction rather than adding another complicated process to someone's working day. --- ### Replace Weekly Reporting First The rollouts that go wrong are usually the ones launched with a long training deck. We tracked 412,000 signups across 38 SaaS products, and onboarding checklists with 3 to 5 steps were completed 67% of the time, against 18% for checklists with 10 or more. That study is about customers, but I've watched the same shape play out inside agencies adopting new tools: ask people to change ten things and they change none. What helps is choosing one real task the new tool must replace in week one, say the weekly client report, and retiring the old method for that task on a fixed date. Everything else waits. Normal work carries on, because people are only learning one thing, and whoever handles that task becomes the person colleagues ask when they get stuck. The second task gets added after a couple of weeks of the first one working. --- ### Hold Office Hours for Edge Cases People resist being held responsible for outcomes while a new process is unpredictable. That distinction matters in environments where an overlooked step can affect a release, a customer, or an audit trail. I make accountability explicit before asking teams to change behavior. The action that made adoption smoother was establishing an office hour after workflow changes. It was staffed by people able to alter the process, not explain it. Employees brought edge cases instead of saving them or hiding them in workarounds. Patterns from those conversations led to refinements and a record. Adoption accelerated because uncertainty was treated as operational data, rather than reluctance. --- ### Set Clear Decision Boundaries Upfront Successful adoption depends on preserving professional judgment. Employees disengage when technology is positioned as a replacement for experience rather than a structure that makes experience easier to apply consistently. This is especially important in work involving relationship management, quality evaluation, and nuanced decisions that cannot be reduced to a checkbox. The action that improved adoption most was publishing decision boundaries before launch. I made clear which tasks should follow the new workflow without exception, which required human review, and who could override the process. That clarity reduced hesitation, prevented unnecessary escalation, and reassured experienced employees that operational discipline would not come at the expense of sound judgment. --- ### Let Employees Build Their Own Automations The single action that most improves adoption is having the people affected build something themselves before you ask them to change anything. We run one-day, hands-on workshops for groups of 20 to 25 non-technical staff, with the goal that they leave able to build their own automations, not just understand AI in theory. I arrive 30 minutes early to meet people individually, because the biggest barrier is not the tool, it is people quietly assuming they are not technical enough. Then we open with a real working build from a client, not a slide. Once a room sees an agent doing actual work, the conversation moves from whether to how. Essential work stays protected because nobody is asked to switch systems on day one. Their first automation sits on top of the tools they already use, and it targets a task they personally find tedious. In practice, people walk out of the workshop asking when they can start building, which is the opposite of the resistance most rollouts budget for. Adoption follows when people are solving their own frustrations, not following an IT mandate. --- ### Invite Harbourmasters to Shape Product Features A marina can't stop working while it changes systems. Boats still arrive, guests still need a berth and berth holders still expect their invoices, so asking a harbourmaster to switch everything over in one go is the fastest way to lose them. What works for us is starting with the core and adding the rest later. A marina begins with the berth map, the boater records and invoicing, and only adds guest bookings, point of sale or the crane module once the basics run on their own. Each step replaces one manual routine instead of all of them at once. The one action that made the biggest difference is giving the people who do the work a direct line into the product. Several of our harbourmasters suggest features, and they see them discussed and built. One of them told us the team gave him the chance to be part of the development process. When people feel a tool is being shaped around their work, adoption stops feeling like something done to them. --- ### Unify Teams Around Customer Problems The action that made adoption work best was changing who owned the problem before changing the tools. When I ran brands like Hurley, P.E. Nation and SurfStitch, I had customer service report directly to me and merged it with post-purchase operations into one department. New technology tends to stall when it lands in one team but the work it changes sits across two or three, because each team keeps running the old process alongside the new one to protect itself. Once the people who answer complaints and the people who fix orders sit together, a new system is not one more thing on top of their day. It replaces a handover that was frustrating everyone. That made it far easier to roll out new tools without disrupting essential work, because the team could see that it took work off their plate rather than adding to it. The best evidence I have seen of this comes from one of the brands that adopted our platform early. Their customer service team's engagement score was 63 percent, the second worst department in the company. Within six months of the team being able to fix order problems before customers complained, it rose into the late 80s and became the highest in the business. People adopt technology quickly when it removes the part of the job they hate. --- ### Budget Onboarding as Delivery Capacity Treat onboarding as scheduled work when a new tool changes an established process. At Ronas IT, we plan and track internal onboarding like client work. The hours people need to learn a process have to be visible alongside delivery commitments; otherwise the new workflow competes with work that already has an owner and a deadline. For a rollout, I'd map the step that changes, identify who depends on its output, and give those people one bounded task in the new system. Keep the existing route available while they do it. A demo proves the tool can work in ideal conditions. A real task shows where permissions, data, or handoffs fail. If a permission or handoff fails, the old route still carries the commitment while the transition owner fixes it. The person responsible for the essential work should decide when the new route is dependable enough to replace the old one. The action worth copying is putting onboarding in the work plan rather than leaving it to spare time. Assign an owner to the transition, allocate time for the people affected, and record the issues they find as tasks. That gives a manager a way to resolve friction without asking staff to absorb it after hours or delaying the delivery they were hired to protect. It also makes the cost of adoption a planning decision. I wouldn't switch an entire workflow on the strength of a training session. Move the next bounded task through it, inspect the result with the people who rely on it, then expand the scope. --- ### Map Role Impacts Before Go-Live I help employees adopt new technology without disrupting essential work by planning each role's training and practice time from a change impact assessment completed before go-live. That assessment is the one action that made adoption more successful in the post-merger integrations I have led. A change impact assessment records what changes for each role in its processes, systems and tools, and what support that role needs. The change management lead in the integration management office builds it with the functional executives from each company. The assessment identifies which roles face a small adjustment and which face a fundamental change in how they work. The change management lead plans each role's training and support from it. A role with a small adjustment receives a short briefing. A role whose daily work changes receives role-based training and scheduled practice time before go-live. I plan that practice time inside each team's work schedule, so essential work stays staffed during training. The assessment also gives the go-live decision a factual basis. Before go-live, the workstream leads sign off on each team's readiness against what the assessment requires for each role. The integration steering committee then takes the go/no-go decision against those sign-offs. In one integration, the change management lead worked with the functional executives of both companies to identify the changes affecting each team at every stage. Employees moving to new platforms, including customer service agents, received preparation at each relevant stage. This approach accelerated adoption and ensured continuity in customer service. --- ### Protect Learning Time for Production Migration The honest answer? You can't fully avoid disruption. Any real technology change is going to break someone's workflow for a while. The goal isn't zero disruption. It's making sure the disruption is short and people come out the other side actually better off. I run data platform engineering at Softline Solutions. We manage GCP infrastructure (BigQuery, Terraform, Kubernetes, Kafka) for 1,500+ client accounts. Last year we migrated our entire ETL pipeline from a batch-oriented Airflow setup to a streaming architecture with Flink. That's not a small change. Every data engineer on the team had built muscle memory around Airflow DAGs. Flink thinks differently. Stateful stream processing, event time windows, checkpointing. It's a different mental model. Here's what didn't work: documentation. We wrote a 40-page migration guide. Nobody read past page 3. We did a two-hour training session, people nodded along, then went back to writing Airflow DAGs because that's what they knew. What actually worked was running both systems in parallel for 12 weeks and pairing engineers on real tasks. Not toy examples, real production jobs. One engineer who already knew Flink sat with one who didn't. They'd rewrite an existing Airflow DAG together as a Flink job, deploy it, watch it run. The old DAG stayed active as a fallback. When the Flink version proved stable (usually after a week or two of monitoring), we'd decommission the Airflow version. The one action that made adoption stick was giving people permission to be slow. I told the team explicitly: your velocity will drop for the first month. That's expected. Don't try to hit your old sprint targets. Just focus on learning the new tool by doing real work with it. That removed the anxiety. People stopped sneaking back to Airflow to "just get this one thing done fast." They committed to Flink because they weren't being punished for the learning curve. By week 8, most engineers were writing Flink jobs without help. By week 12, our pipeline latency dropped from hours to minutes and the team actually preferred the new setup. The key was treating adoption as a production workload, not a side project. You schedule it, you resource it, you give it room to breathe. Technology changes fail when leaders expect people to learn new tools on top of their existing deadlines. That's not adoption. That's setting people up to quietly resist. Timur Rakhmatullin, Senior Software Engineer, Softline Solutions --- ### Shorten QA Directions to Speed Closure The action that improved adoption was shortening the instructions people had to act on. We initially focused on removing an infrastructure bottleneck and discovered that the human handoff needed just as much attention. At Market Brew, waiting for QA to test every item serially tied up shared testing systems. Codex let us create parallel mini testing environments. We used monday.com to interface with the whole team, making each teammate a potential QA tester who could own a change request without holding up everyone else's work. But the Codex-generated QA plans became very long and harder to understand. The technology could produce a plan, yet the initial rollout was inefficient for the people expected to use it. We instructed the agents to use a short writing format: active sentences, one instruction per sentence, and a clear separation between the action and its expected result. Testers could understand the plans better, and it became much quicker to close QA items. My lesson is to check the task the employee actually receives. Parallel execution can remove a queue while verbose instructions create another one. Adoption improves when the person can see what they own, what to do next and how to recognize a successful result. --- ### Ask Skeptics to Draft Remedy Lists I run the new tool alongside the old process for one full cycle, such as a month of orders or one round of reporting, and let the people doing the work decide when the old way gets switched off. That way essential work never depends on a tool nobody has used under real pressure, and the team finds the gaps during a quiet week instead of a busy one. The action that does most for adoption is asking the most skeptical person on the team to list what the new tool can't yet do, because that list becomes the fix list and their eventual sign-off convinces everyone else. --- ### Measure Results Over Platform Utilization An important shift to keep in mind is not how much the team is using the new tool or the new automation, but that our initial goal is to make their lives easier or to improve the output. So you have to keep in mind how much of your initial goal you are achieving, be it compliance with an SLA, shortening time to merge or giving a better candidate experience during a recruiting process. You have to keep those metrics in mind and shift the focus from "is my team using this?" to "am I achieving my initial goal?" Perhaps they do not adopt 100% of the new solution, but with a minimum part of the tool or one or two features being used, they still achieve the goal. That is the most important achievement, rather than tool usage. --- ### Offer Job-Specific Rehearsal Sessions When a new technology changes the way people work, employees need to understand how it will affect their daily tasks. Simply introducing a new tool is not enough. They also need guidance and time to get comfortable using it. One approach that can help is giving employees short, role-specific sessions where they can learn the new process, ask questions, and practice before using it in their daily work. This helps reduce confusion and makes the transition easier without affecting essential tasks. Platforms like Skill Lake can support this by helping organizations assign learning activities based on employee roles and track their progress. Managers can then identify who might need additional support. I believe the key is to make employees feel supported during the change, rather than expecting them to figure everything out on their own. When people understand what is changing and have a chance to practice, they are more likely to feel confident using the new technology. --- ### Transfer Legacy Records First The one action that made the biggest difference: we migrate the history before anyone is asked to change how they work. When we replaced a distributor's no-code sales tool with a custom ERP, we moved 2,700+ orders, 240+ clients and 2,470+ invoices across, with the original invoice numbering preserved. On day one, people opened the new system and found their own customers and their own documents. There was nothing to re-enter and no reason to keep a parallel spreadsheet "just in case". Two other choices help. We build around the way the work is already organised, not around the software's logic: for a roofing company, every request is filed under the building it concerns, because that is how the team thinks about its work. And each role sees only what it needs: the distributor's ERP has five role profiles, so a salesperson never lands on accounting screens. Adoption problems are often data problems in disguise. If the old tool still holds information people need, they will keep going back to it. --- ### Validate AI Within Safe Sandboxes "Successful technology adoption starts with understanding the work employees must continue to deliver. Leaders need to identify where a new tool fits, what responsibilities change and where employees still need to exercise judgment. With AI, that includes accounting for the time it takes to review outputs and correct errors. The reality is that we're all learning together how emerging AI services affect different parts of an organization. A policy can set expectations, but it can't tell you what will actually work. That's why I believe the most effective approach is controlled experimentation. Give employees sandbox environments where they can try new tools on realistic, non-critical work with a contained blast radius, so mistakes don't spill into essential operations. The people closest to the work understand the internal processes, the pain points and the unwritten rules that no tool knows on its own. When their creativity and institutional knowledge guide the experiments, AI gets the context it needs to play to its strengths, and the results are amplified rather than generic. Once a use case proves its value in the sandbox, including the time spent verifying and correcting outputs, it can move into everyday workflows with confidence and evidence behind it." ## Related - [Enterprise IT Rollouts: Turn Awareness Into Real Adoption](https://ciogrid.com/qa/enterprise-it-rollouts-turn-awareness-into-real-adoption.md) - [How to Overcome Resistance to Change as a CIO: 4 Proven Strategies](https://ciogrid.com/qa/how-to-overcome-resistance-to-change-as-a-cio-4-proven-strategies.md) - [How to Lead It Teams Through Organizational Change: 6 Best Practices](https://ciogrid.com/qa/how-to-lead-it-teams-through-organizational-change-6-best-practices.md) - [7 Ways CIOs Stay Current with Emerging Technologies While Managing Daily Responsibilities](https://ciogrid.com/qa/7-ways-cios-stay-current-with-emerging-technologies-while-managing-daily-responsibilities.md)