Which technologies should businesses prepare for today?

A digital strategy is not a list of fashionable services that must be purchased urgently. It is a clear company development plan in which every technology is connected to a specific business objective: increasing sales, reducing costs, accelerating processes, improving service quality, or lowering risk.

According to the World Economic Forum, 86% of employers expect AI and information-processing technologies to have a significant effect on business, while AI and big data, cybersecurity, and technological literacy are becoming the fastest-growing skills. At the same time, 63% of companies identify skills gaps as the main barrier to transformation. Implementing technology without preparing employees and changing processes is therefore not enough.

In the coming years, AI will move from answering questions to performing actions. AI agents will be able to analyse enquiries, prepare documents, update CRM records, monitor deadlines, produce reports, and coordinate individual stages of work. The starting point should not be a complex autonomous system, but one recurring process with clear rules, an accountable employee, and measurable outcome criteria.

Data remains the foundation of AI, analytics, and automation. A company must know where client information is stored, who is responsible for its quality, which employees have access, and how well different systems are aligned. If data is fragmented or unreliable, even the most advanced AI will produce weak results.

The next direction is end-to-end automation. The CRM, website, mobile application, warehouse, finance, support, and analytics must exchange information without manual copying. An isolated chatbot may accelerate one stage, but a true digital ecosystem supports the client throughout the journey: from first contact to payment, service, and repeat purchase.

Client interaction is changing as well. Self-service, voice AI assistants, personalised recommendations, and natural-language search are becoming familiar interfaces. At the same time, content must be understandable not only to people but also to AI search: information should be structured, expertise demonstrated, and precise answers given to the audience’s real questions.

Security must develop at the same pace as automation. NIST recommends managing AI risk throughout every stage, from design to operation and outcome evaluation, while the Cybersecurity Framework 2.0 treats cybersecurity as part of corporate governance. This means access control, backups, action logging, integration protection, an incident-response plan, and regular supplier reviews.

Passwords are also gradually giving way to passkeys and stronger multi-factor authentication. According to the FIDO Alliance, around five billion passkeys were already in use worldwide in 2026, so passwordless access should be considered when designing new portals, applications, and internal systems.

A 12-month digital development plan

During the first three months, conduct an audit: describe the client journey, systems, data, manual operations, costs, and risks. Then select three measurable business objectives, assign accountable people, and create a prioritised list of initiatives using four criteria: value, complexity, cost, and risk.

From months four to six, two or three limited pilots can be launched. Examples include an internal AI assistant for the knowledge base, automated enquiry processing, and a management dashboard. Authentication, backups, and access management should be strengthened in parallel, while the teams participating in the pilots are trained.

From months seven to nine, scale only the solutions that have demonstrated value. They should be connected to the CRM and other systems, with unified data, logging, quality control, and escalation of unusual situations to an employee. During the same period, client self-service, voice interfaces, and expert content for traditional and AI search can be developed.

During the final three months, the company evaluates the economic effect, closes unproductive experiments, and embeds effective solutions into its processes. A security audit, backup-restoration test, policy updates, and preparation of the roadmap for the following year are completed.

The result should be measured not by the number of connected services, but by business indicators: operation time, processing cost, conversion, client retention, response speed, error volume, the share of automated processes, employee adoption, and return on investment.

The winner will not be the company with the largest collection of technologies, but the one that can combine quality data, well-designed processes, security, a capable team, and clear objectives.