New year, new strategy deck. It’s easy to fill slides with ambitions – “become AI-driven”, “modernise our stack”, “improve customer experience” – and then watch day-to-day reality swallow them whole by February.
If you want Q1 2026 to set up a better year rather than just a busier one, focus on a small number of tech initiatives that compound. Here are three that are realistic, high-leverage, and suitable for most organisations, whether you’re a startup or an established SME.
1. Run a Data Hygiene and Ownership Sprint
Everything you want to do with AI, automation and analytics depends on having data that’s at least vaguely trustworthy. Unfortunately, many businesses start the year with CRMs full of duplicates, file shares full of mystery documents, and dashboards nobody believes.
In Q1, run a time-boxed sprint dedicated to data hygiene and ownership in one or two critical domains – for example, customers and products.
Tasks might include:
- Defining the “system of record” for each key data type.
- Fixing obvious duplicates and filling critical gaps (like missing contact details or IDs).
- Setting simple standards for fields and naming.
- Assigning clear ownership: who is responsible for keeping each dataset in reasonable shape?
You don’t need perfection. You need a baseline from which future improvements are possible. A few weeks of focused effort here will make every subsequent project – from CRM changes to AI pilots – more effective.
2. Automate One Painful Workflow End-to-End
Rather than dabbling in dozens of small automations, pick one painful, high-volume workflow and commit to improving it end-to-end by the end of Q1.
Good candidates include:
- Onboarding new customers or staff.
- Invoice processing and approvals.
- Support ticket triage and escalation.
- Routine reporting cycles.
Bring together the people who actually run the process, someone with automation skills (no-code is fine), and a decision-maker. Map the current state, then redesign with three aims: fewer manual steps, clearer ownership, and better visibility.
Use whatever tools you already have – workflow features in your CRM, low-code options in Microsoft 365, integration platforms – rather than buying new platforms immediately. Aim to cut manual effort by 30–50%. Measure before and after: how long does the process take, how many handoffs, how many errors?
Completing one end-to-end automation does three things. It frees up real time, it gives your team a concrete success story, and it builds confidence that change is possible. That momentum is worth more than ten half-finished “digital transformation” ideas.
3. Create a Practical AI Playbook for Your Team
AI will not wait for your strategy to be perfect. Staff are already using tools informally – sometimes cleverly, sometimes riskily. In Q1, replace ad-hoc experimentation with a simple, practical AI playbook.
The playbook doesn’t need to be fancy. It should answer four questions:
- Where do we encourage AI use? (e.g. drafting documents, summarising calls, brainstorming ideas, coding assistance.)
- Where do we ban or restrict it? (e.g. handling sensitive personal data, making final decisions on high-risk issues.)
- Which tools are approved? (and how to access them securely).
- What are the red lines? (data protection, confidentiality, representing AI output as human judgement.)
Back this up with two or three short training sessions showing practical examples in your context. Capture and share early success stories: a team cutting proposal writing time, a manager using AI to prepare performance reviews more fairly, a developer using AI to tame a legacy codebase.
The goal is to move from “everyone doing their own thing” to “we have shared practices”. You don’t need to anticipate every future use case; you need enough structure to avoid obvious pitfalls and to learn together.
None of these initiatives will win design awards. They are deliberately unglamorous. Yet they create the conditions for everything else you might want in 2026: better products, smoother operations, credible AI use and more resilient teams.
If you finish Q1 with cleaner core data, at least one fully automated workflow, and a living AI playbook that people actually use, you’ll be well ahead of the pack – and your big strategic slides will finally have something solid underneath them.
