Skillable executives say software suppliers and technology leaders will place greater emphasis on proving value from AI and on training programmes that measure performance in realistic scenarios through 2026.
The company, which develops virtual lab environments for training and validation, described a market in which many buyers remain unconvinced by AI claims and where organisations struggle to translate AI spending into operational results.
Software sales
Mark Mangelson, Chief Revenue Officer at Skillable, said software revenue teams now face a more complex selling environment. He pointed to rapid product changes driven by AI and to higher expectations from buyers. He also said that a growing number of competing products has made it harder for customers to connect new purchases to business outcomes.
"As we enter 2026, software revenue leaders face a new level of complexity and opportunity. The pace of product innovation has accelerated, due to AI advances, and buyer expectations are evolving as a result, but so is market noise and confusion. Many buyers are struggling to connect new investments with tangible business value and the sellers who can prove that their solutions are clear growth and profitability drivers will stand out in 2026," said Mark Mangelson, Chief Revenue Officer, Skillable.
Mangelson said the software market continues to grow, and he argued that growth has increased the volume of competing messages. He said many products now describe themselves as AI-enabled, which has raised the bar for differentiation and made proof points more important in procurement cycles.
"The software market continues its march toward the trillion-dollar threshold, and with growth comes noise. Every category is crowded. Every product claims to be AI-powered. In 2026, the companies that rise above the noise will be those that prove value, not just pitch it. Expect virtual labs and "experience-first" selling to become central to differentiation, enabling prospects to explore environments that mirror their stack, workflows, and use cases. By delving deeper than a demo, software companies that allow prospects to get hands-on early on in the buying process will build trust, set realistic expectations and stand out for their product quality, transparency and immersiveness."
He also linked sales performance to training and readiness. Mangelson said companies will invest more in learning environments that adapt to users and simulate real situations. He said sellers and partners need greater technical fluency for conversations with senior decision-makers.
"For sales enablement, expect to see more software companies turning to adaptive and immersive AI learning environments to fully prepare and train sellers and partners. AI will tailor learning to each user's current skill level, creating more personal and relevant experiences that don't disengage or bore learners. Reps will enter calls with greater technical fluency, thus, better able to lead conversations with senior-level executives who expect more than a surface-level demo," said Mangelson.
Mangelson also forecast wider use of agentic AI in sales. He warned that leaders should treat governance and training as prerequisites. He said poor use of such tools could affect prospect relationships.
"We will see more uses of agentic AI in sales too. It's vital that sales leaders don't introduce AI solutions into their sales tactics without training and assessment, however, as misuse of agentic AI tools could impact prospect relationships. Training sales reps using scenarios where they'll use an AI tool, allows them to practice and test different uses for AI, before they use them in real-world conversations and workflows," said Mangelson.
He said that human factors remain central in commercial deals, even as AI reshapes product features and go-to-market tactics. "Behind every AI advancement and every product launch, it's still people who ultimately create trust and close deals. In 2026, the leaders who elevate both the buyer's experience and the seller's expertise will define what winning looks like in the next chapter of software," said Mangelson.
AI returns
Frank Gartland, Chief Solutions Officer at Skillable, said many organisations have installed AI features but have not changed how staff work. He said this disconnect has limited the returns from spending on AI and has contributed to weak outcomes from pilot projects.
"As we head into 2026, one truth is clear: AI may be deeply embedded in the technologies we use to work but most people don't seem to be changing their workflows and habits to take full advantage. Despite billions invested in AI, most AI pilot projects are disappointing leaders and lack tangible results. A widening gap in AI proficiency continues to hinder AI software growth, adoption and return on investment (ROI)," said Frank Gartland, Chief Solutions Officer, Skillable.
Gartland said many training programmes still focus on content consumption rather than demonstrated competence. He said video-based learning can spread information but often fails to show whether people can apply AI tools in practical settings.
"Many organizations are recognizing that 'tried and true' video-based training and e-learning programs work for content and knowledge-dissemination, but are not producing results in terms of helping a workforce or groups of customers have confidence using AI-enabled applications and workstreams to get more done. Engaging with AI-related training content and learning journeys is not an accurate measure of a person's effectiveness in producing better (or more) work faster," said Gartland.
He said organisations will seek stronger indicators of capability and will place more weight on scenario-based learning. He said that realistic practice will form part of training and assessment.
"In 2026, more organizations will move away from proxy measures for AI training, as too much is at stake when people need to responsibly and confidently use such technologies. AI training will need realistic scenarios embedded in the learning experience, as this will show someone exactly how to use, or not use, AI in their daily work," said Gartland.
Gartland said AI changes job design and the skills people need. He said discernment has become more important and that many roles now require workers to judge AI outputs and guide systems through iterative prompts.
"AI redefines what it takes to do a 'job' effectively and efficiently. AI enables people to more easily develop their true potential because it helps us arrive at higher level outcomes faster. The skills we need to hone are those of discernment. Now, every person needs to manage and assess various forms of AI, to provide the right prompts and iteration to arrive at the right outcomes and the expertise to recognize what that right outcome is. This changes what skills are required in the workplace, what skill level is needed and how these skills are assessed and validated. As we enter 2026, there will be a race to 'credentialize' the ability to work alongside AI, whether that's through micro-credentialing, certifications, hands-on proof of performance or another validation tactic," said Gartland.
He said vendors and internal training teams will rely more on practice environments that reflect real job tasks without putting live systems or sensitive information at risk. Gartland said these approaches will extend beyond engineering teams to other business functions.
"For AI proficiency to be built quickly and to the level that a business needs, users must practice and experiment with an AI solution in contexts that match their real-world tasks and responsibilities. Scenarios will be increasingly relied on by AI vendors and in-house trainers to provide a safe environment for someone to understand an AI tool, without exposing production systems or data. Someone will be able to use an AI feature to format a database, create a report or consolidate documents into an at-a-glance summary, in a safe way that isn't live, isn't customer facing and doesn't risk business operations. These practice ecosystems will become the backbone of AI readiness programs, not just for engineers, but for every role AI touches."
Skillable's executives said organisations and software suppliers will focus more on hands-on experience, training and verification of skills as AI becomes more embedded in products and day-to-day work.